HR Managers Agree on Efficiency and Accuracy

 

HR Managers Agree on Efficiency and Accuracy

A cross section of 100 HR Managers from North American manufacturing companies, with employee counts greater than 100, recently participated in a study regarding attendance policy administration. They represented the following 2-digit SIC Codes:

20 - Food & Kindred Products Manufacturers

22 - Textile Mill Products Manufacturers

25 - Furniture & Fixtures Manufacturers

30 - Rubber & Misc. Plastics Manufacturers

31 - Leather & Leather Products Manufacturers

33 - Primary Metal Industries Manufacturers

34 - Fabricated Metal Products Manufacturers

35 - Industrial & Commercial Machinery Manufacturers 37 - Transportation Equipment Manufacturers

Managers agreed on chief concerns about attendance policy administration:

* Productivity (HR staff are often bogged down in non-automated environments)

* Errors (Embarrassing and costly mistakes are made by hand tabulation)

* Time Savings (Gathering, retrieving and processing of attendance data eats into time needed for other activities)

* Ease of Use (Complexity of administration programs was a common complaint)

* Effective Employee Communication (HR staff members had more important things to do with their time than look up attendance information for employees)

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Recognizing that excellence in communication with employees was key to having good relationships, HR managers noted that:

* Objective data eliminated problems

* Accurate data builds trust between employer and employee

* Access to accurate data actually increased the attendance of factory workers

* Automated data would free up the time of those who spend time “accumulating” the answers for employees.

Two HR Managers Seek an Answer to Their Attendance Policy Administration Challenges

Two HR managers from large nationally-based manufacturing companies experienced the frustrations of time consuming fact checking, endless data tabulation, distracting inquiries and numerous complaints while administrating their companies’ attendance policies.

Burdened with an ineffective way to handle data, each manager was overwhelmed by the amount of time required to input and record attendance activity and monitor policy compliance.

* One of the managers, whose company serves the aviation industry, spent approximately 10 hours weekly to collect and record attendance incidences. This accounted for 25% of her week.

* The second manager who works at a leading beverage company calculated she spent five hours a week, about 15% of her time, on manually documenting attendance and coding incidences.

Since information on attendance activity was only available through each of the company’s HR departments, the managers were frequently interrupted by requests and questions from supervisors and employees.

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Dependency on outdated methods also affected the managers’ progress with weekly and monthly report generation.

* These reports were essential to evaluating employee attendance statistics against policy guidelines. Managers were forced to form their reports from data that had been manually compiled. The inefficiency of this method only increased the odds of numerous errors; so regrettably, the managers concluded their reports were unreliable. In addition, it normally took each manager several hours a month to generate the reports—further robbing them of time for other duties.

Both HR managers relied upon recordkeeping to help them monitor disciplinary activity and comply with union policy.

* Last year, one manager spent over 24 hours, or 60% of her workweek, in collecting documentation for a grievance case. Without an automated system at the time, it was necessary to collect paperwork manually then consolidate the information into a report. Data collected about the case was used as resource material for disciplinary action letters that were also manually created.

Automated Coding Saved Time and Reduced Significant Errors

Each manager had been frustrated with excess time spent on tedious input of attendance policy codes for their reports. They manually coded policy transgressions against daily attendance records. Mistakes were made.

The HR manager at the beverage company had been experiencing challenges with inaccurate coding of employees’ absences, paid time off, and leaves of absence. The previous year, approximately 10-15% of the company’s employees had been paid for days they didn’t work as a result of careless recordkeeping. The automated attendance system reversed this problem and now there’s accurate coding and tracking of attendance incidences for all employees.

With an automated policy application installed and integrated with their time labor management systems, coding became hassle-free. All detailed attendance policy details interfaced automatically thereby increasing worker productivity.

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Automation Produced Accurate Reports in One Step

Previously, report generation was an extended process of data collection, cross-reference and fact checking. Now, reports are automatically created with a command.

Both managers achieved real-time reporting and documenting with automated tracking of attendance incidences. The instantaneous functionality of the program ensured current employee tracking. This eliminated costly errors previously incurred from overpaid labor, union penalties, and delinquent disciplinary action.

Automation Improved Employee Communications

An automated policy application removed subjective analysis and guesswork. The HR managers responded to employee inquires through a simple search process rather than lengthy manual fact

finding. They achieved more timely communications about policy issues in a concise and prompt fashion. This sent a message of fairness and impartiality, which helped to improve morale and productivity among the employees.

The Lack of Automation Wasted Company Dollars and Time

Aware they were wasting company dollars and time, the managers requested their companies explore the automation of attendance policy administration. Each manager’s rationale was similar even though they worked in two different industries. They sought to:

* Save time by eliminating double entry

* Increase accuracy and efficiency with a system that was customizable and configurable to their unique situation and policy

* Acquire an easy-to-use system for entering data and one that required little editing.

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Implementation of an automated attendance system achieved all the goals sought by the HR managers. They resolved the challenges they had experienced prior to the installation. Results included:

HR Manager, Aerospace Company

* HR manager saved approximately 45 hours monthly by eliminating the need to collect data and cross-reference codes.

* Offsite plant saved 90 hours monthly with automated data collection and coding.

* Production of automatic disciplinary letters eliminated the need to generate the letters manually.

* Errors resulting from manual data entry were completely eliminated.

* Reports are 100% accurate and produced on time.

HR Manager, Beverage Company

* HR manager saved 60 hours monthly by eliminating the need to collect data, cross-reference codes and assist supervisors.

* Mistakes generated from manual entry of data were completely eliminated.

* Automatic integration of policy codes eliminated a 15% error incidence rate.

* Reports were completely accurate and produced on time.

* Time spent on documenting grievance case reduced from 24 to eight hours.

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Substantial Financial Results

The application was customized to meet the individual specifications of each company. This understandably impacted the financial investment for the software resulting in each company investing slightly different amounts. The Aerospace company invested $8,500 for its automated system while the Beverage company invested $9,500.

The HR managers realized annual savings of the following:

HR Manager, Aerospace Company

* $10,000 in corporate office HR manager’s time manually collecting and tabulating data.

* $20,000 in offsite HR department’s time manually collecting and tabulating data

They began to experience an ROI in less than four months.

HR Manager, Beverage Company

* $16,000 in HR manager’s time manually collecting and tabulating data and assisting supervisors with attendance questions.

* $45,000 in one supervisor’s time monitoring attendance compliance.

They began to experience an ROI in less than two months.

The following page documents the process and data flow of a sample attendance policy administration program.

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Attendance Advisor Process and Work Flow

Attendance Advisor

Automated Attendance Policy Administration

Human Resources

HR adjusts and

correct TLM data

Supervisors adjust

and correct TLM

data

Time Clocks

record

punches

TLM System uses dock

data and schedules to

record exceptions

(differences between

schedule and actual

punch). Can also accept

manual entries

Human Resources

HR exports

i•- attendance files

from TLM

Supervisors

TLM System TLM System 

TLM System TLM System

Attendance Advisor 

Attendance Advisor Attendance Advisor 

Attendance Advisor

•

HR uploads

attendance files

from TLM

Employees can view attendance data from Attendance Advisor via web

HR views and prints

actions (ex. warnings)

prints reports, etc...

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How Security Levels Should Work

* All Administrator levels can see all employees for their company.

* Supervisors can only see themselves, and any employees explicitly assigned to them using the Edit Supervisors Admin utility.

* Employees can only see themselves.

Level 1 Administrator Functionality:

* Import/Process

* View, create and edit events

* View actions and modify action status

* Edit employee details

* Run reports

* Customize action document text

* Edit supervisor assignments

* Edit access levels for custom reports

* Change company home page greeting

* Edit company details

* Add new employees to the company

Level 2 Administrator Functionality:

* Import/Process

* View, create and edit events

* View actions and modify action status

* Edit employee details

* Run reports

Level 3 Administrator Functionality:

* View, create and edit events

* View actions and modify action status

* Edit employee details

* Run reports

Supervisor Functionality:

* View, create and edit events of their employees

* View actions and modify action status of their employees

* Edit employee details of their employees

* Run reports that include their employees

Employee Functionality:

* View their own employee details

* Run report on their own attendance record

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HR Managers Talk About Their Attendance Policy Systems

Regardless of industry, HR managers agree that automation saves time, increases productivity and improves accuracy. Relieved of a time consuming chore and no longer burdened with worry, human resources can focus on other responsibilities.

“We save at least an hour daily, sometimes more depending on absences. Our monthly calculations previously took three hours with additional time for counseling. That’s a timesaving of over 11 hours a week. The system provides accuracy, saves time and provides more timely discipline counselings. Through its capacity to automatically calculate attendance incidences, the system instantly generates counseling letters for disciplinary action.

“It administers the rules consistently and accurately; counseling is timely, employees are notified of their point status as it appears on their pay stub every week. There’s definite peace of mind.”

* Ohio – Precision Castparts Corp.

“Since the installment of the system, we save one hour daily with the elimination of double entry. Prior to the system, we had 10-15% attendance transgressions, now it’s down to 5% containment and we anticipate it going down to zero percent. The system catches anything and everything. It holds managers and employees accountable to the policy.

“Including ‘points’ on pay stubs has saved us from potential employee grievances, financial loss or penalties. We estimate a savings of $500 - $1,500 from just one grievance occurrence. Three grievances were filed last year and if we’d had the system then, we could have saved $500 - $1,500 per person.”

* Kentucky – Buffalo Trace Distillery

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HR Managers Talk About Their Attendance Policy Systems

“Our attendance policy administration program was able to take our very complex points system and automate it for us. We have saved countless hours of manual tracking. Whenever we modify our internal points system, the program developer modifies our program and gets the update to us in a hurry.”

* Ohio- CresCor

"The attendance policy administration program has saved time. I do not miss the manual recording of the attendance. Attendance records are updated earlier than before. Therefore, the warnings are issued to employees sooner.

“I like everything about the attendance policy administration program. I would not want to ever go back to the old way."

* Illinois- HCC, Inc.

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Conclusion

Whether summarizing attendance data in monthly reports or communicating status to employees, it’s critical to get it right. HR managers must have accurate and real-time information at their fingertips. Otherwise, mistakes can lead to incorrect attendance bonus payments, missed or unnecessary disciplinary actions, and government or union penalties, not to mention reduced employee morale.

An automated and simple-to-use attendance policy administration program can help HR managers maximize time and minimize errors for effortless and accurate documentation.

Managers can realize a corporate resource savings - both monetary and labor within months of installation. As each HR manager experienced at her company, it’s feasible to expect an ROI of 350% to over 600% in less than a year of installation. That translates to tens of thousands of dollars saved within a matter of months. Returns of this magnitude can end up as profits for companies.

To explore Points North’s solutions and services or to speak to white paper author Frank Le Fevre, visit www.points-north.com.

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Scheduling and Control of

Flexible Manufacturing Systems: A Critical Review

Chuda Basnet

Department of Management Systems

University of Waikato, Private Bag 3105, Hamilton, New Zealand

and

Joe H. Mize

School of Industrial Engineering and Management

Oklahoma State University, Stillwater, OK 74078, U.S.A.

Abstract

      Flexible manufacturing systems (FMS) are distinguished by the use of computer control in place of the hard automation usually found in transfer lines. The high investment required for a FMS and the potential of FMS as a strategic competitive tool make it attractive to engage in research in this area. This paper presents a review of literature concerning the operations aspect of FMS. Articles emphasizing many methodological perspectives are critically reviewed. The review is done from multiple viewpoints. Future research directions are suggested.

Key Words: Flexible manufacturing system, production planning, scheduling, production control.

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1. Introduction

      Flexible manufacturing systems (FMS) are distinguished by the use of computer control in place of the hard automation usually found in transfer lines. This enables FMS's to reconfigure very rapidly to produce multiple part types. Use of fixtures and tool magazines practically eliminates setup time. These features permit economic production of a large variety of parts in low volumes. FMS's are increasingly being adopted in the manufacturing sector on account of the additional advantages of rapid turnaround, high quality, low inventory costs, and low labour costs. The high investment required for a FMS and the potential of FMS as a strategic competitive tool make it attractive to engage in research in this area. The research problems raised by the industrial espousal of FMS could be broadly classified into two problem areas: design problems and operation problems. At the design stage, one is interested in specifying the system so that the desired performance goals are achieved. The operation problems are aimed at making decisions related to the planning, scheduling, and control of a given FMS. This paper presents a review of the published literature on the operation problems of FMS. We take stock of the progress in this area considering various aspects of the literature.

      A considerable body of research literature has accumulated in this area since the late 1970's when the first papers were published. A few surveys of the literature have also appeared (Buzacott and Yao 1986, Rachamadugu and Stecke 1989, Gupta et al. 1989). However, these reviews focused on specific perspectives such as analytical models, or scheduling problems. In this paper we have attempted to review articles having wider methodological perspectives while concentrating on the operations issues. We have also brought the review more up-to-date. We review the literature from multiple viewpoints:

1. Methodology used in resolving the problem

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2. Applications viewpoint

3. Time horizon considered

4. FMS factors considered

In the following sections we present the review from the above viewpoints. In

the final section we will conclude with some directions for future research.

2. Methodology

Based on the methodology followed, FMS operations literature could be

classified in the following ways:

1. Mathematical programming approach

2. Multi-criteria decision making approach

3. Heuristics oriented approach

4. Control theoretic approach

5. Simulation based approach

6. Artificial intelligence (AI) based approach

      There is some cross fertilization among these approaches. For example, some AI based approaches use simulation to generate or evaluate schedules. In the following discussion, the approaches are classified on the basis of their main emphasis.

2.1. Mathematical programming approach

      In this approach, the researchers have cast the problem into an optimization model. Buzacott and Yao (1986) present a comprehensive review of the analytical models developed for the design and control of FMS up until 1984. They strongly advocate the analytical methods as giving better insight into the system performance than the simulation models.

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     To manage the complexity of the problem, Stecke (1983) and many other authors who have followed her divided the FMS operation problem into two subproblems: preproduction setup and production operation. In this view, a FMS is prepared beforehand for the given part mix: loading the tools, allocating the operation to the machines, allocating the pallets and fixtures to the different part types. After this preparatory planning phase, the remaining problems are called operational problems and solved later. Stecke (1983) places stress on preproduction setup of the FMS. This is to be carried out frequently, as the part mix changes. To carry out a complete setup, a FMS manager would solve 5 problems:

1) Part type selection problem. This problem determines the part types to be produced in the FMS out of the total production requirement of the company.

2) Machine grouping problem. Stecke would partition the machines in the FMS so that machines in a group can all perform the same operations.

3) Production ratio problem. This problem is related to problem 1 - determine the ratio of the parts selected to be manufactured in the FMS.

4) Resource allocation problem. This problem determines the allocation of pallets and fixtures to the part types.

5) Loading problem. The solution to the problem will simultaneously allocate

operation of the part types and the corresponding tools to the machine

groups.

     Stecke (1983) then goes on to describe models for the grouping and loading problems. For these problems, the major constraint is the capacity of tool magazines of each machine tool. The minimum number of machines required to cover all operations is calculated using an optimization formulation to pack as many tools as possible in few machine tools, at the same time making enough tool

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allocations to cover all the part types. This formulation gives the number of groups needed. If there are more machines than the number of groups, the additional machines are tooled identical to some of the ones that are grouped. This way, the machines are pooled to allow maximum flexibility. In Stecke's methodology, the operations and corresponding tools are then assigned (loaded) to the machine groups. She suggests 6 different objectives to optimize during the loading phase: 1) Balance the assigned machine processing times. 2) Minimize the number of movements from machine to machine. 3) Balance the workload per machine for a system of groups of pooled machines of equal sizes. 4) Unbalance the workload per machine for a system of groups of pooled machines of unequal sizes. This objective stems from earlier results of Stecke and Solberg (1982) that recommends unbalancing the workload for each machine when the pooled group sizes are unequal in order to obtain maximum production rate. 5) Fill the tool magazines as densely as possible. 6) Maximize the sum of operation priorities.

      The formulations of Stecke (1983) lead to large nonlinear mixed integer problems. She suggests various linearization schemes. Stecke's planning problems place much of the scheduling problem in the setup stage. Once the setup is done as per the five specific sub-problems, most of the resource allocation is already complete. The setup is carried out for a particular part mix. It is not clear when one of the six loading objectives is to be favoured over the others. In some cases, where the machine tools are separated over a long distance, the choice is obvious. In other cases the answer is hard to discern. The grouping problem does not consider the production ratio of parts. Thus, it could give an answer which is not desirable from the view point of maintaining the production ratio. Another problem with the formulation is the large number of variables and constraints that result from the linearization of the problems. That makes the approach computationally expensive. Berrada and Stecke (1983) have proposed an efficient branch and

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bound procedure for solving the loading problem with the objective of workload balancing. Stecke's approach is explained here at length because other mathematical modelling approaches build upon this foundational work.

Lashkari et al. (1987) developed a formulation of the loading problem. Their

formulation considered refixturing and limited tool availability. Besides this

problem, they place an upper bound on the number of tools that may be assigned. They consider two objectives: 1) Minimization of total transportation requirements of the parts, and 2) Minimization of refixturing requirements. The formulations have products of 0-1 integer variables. Lashkari et al. (1987) linearize the formulation to solve the problem using linear integer programming code. Their computational experience shows that even for small problems, the problem size becomes very large. In order to reduce the search, they suggested dividing the problem into two sub-problems, the result of which could be used as an upper bound for the original problem. Unlike Stecke (1983), Lashkari et al. will permit only one allocation of a machine to an operation. This would curtail some flexibility at the operation control level. Their modelling is suitable only when the parts must always traverse to and from a central storage for every inter-machine transfer. Further, the objective function lacks the relative weighting for the different part types. Wilson (1989) used simpler and more straight forward formulation of the constraints to solve the same problem as discussed by Lashkari et al. (1987). He demonstrated substantial savings in computational effort using his modelling of the constraints and the objective function. Shanker and Rajamarthandan (1989) present a similar model with the objective of part movement minimization. In contrast to Lashkari et al. (1987), they do not require the parts to go to a central storage after every operation. Also, they are not interested in the distance travelled: only the number of movements is of concern. Like Wilson (1989), they exploit the

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particular structure of the problem to obtain linearization of the problem. They also reported that high computational effort was required.

      Han et al. (1989) address the setup and scheduling problem in a special type of FMS: where all the machines are of the same type, and tools are 'borrowed' between machines and from the tool crib as needed. In their model, the number of tools is limited. The purpose of their model is to assign tools and jobs to machines so that the 'borrowing' of tools is minimized while maintaining a 'reasonable' workload balance. This is a nonlinear integer programming problem, and is computationally expensive. To solve the problem efficiently, the authors propose to decompose the problem. The two sub-problems each have the same objective as shown above. But the constraints are divided. The first problem finds an optimum tool allocation, given the job allocation. The second problem finds an optimal job allocation, given the tool allocation. Phrased in this way, both problems become linear. The first problem is a capacitated transportation problem, and the second is a generalized assignment problem. It is suggested to solve the two problems iteratively. The FMS investigated by Han et al., is special. All machine tools are assumed identical. Consequently, the jobs remain at one machine, and the tools are moved to the machines as needed.

      Kimemia and Gershwin (1985) report on an optimization problem that optimizes the routing of the parts in a FMS with the objective of maximizing the flow while keeping the average in-process inventory below a fixed level. The machines in the cell have different processing times for an operation. Network of queues approach is used. The technique showed good results in simulation. Chen and Chung (1991) evaluate loading formulations and routing policies in a simulated environment. Their main finding was that FMS is not superior to jobshop if the routing flexibility is not utilized. Avonts and Van Wassenhove (1988) present a unique procedure to select the part mix and the routing of parts in a

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FMS. A LP model is used to select the part mix using cost differential from producing the part outside the FMS. The selected loading is then checked by a queuing model for utilization in an iterative fashion.

      Hutchison et al. (1989) provide a mathematical formulation of the random FMS scheduling problem, where random (not preselected) jobs arrive at the FMS. Their formulation is a static one in which N jobs are to be scheduled on M machines. The objective is to minimize the makespan. They present a mixed integer 0-1 programming formulation. They solve this problem by a branch and bound scheme. A single formulation solves the allocation of the operations to the machines and the timed sequence of the operations. However, their study assumes that material handling devices, pallets, buffers, and tool magazines do not constrain the system. Further, at most one alternative is allowed for any operation. An alternative approach to this problem is to decompose it into two subproblems. The first problem is the allocation of the jobs to the machines in the routings. The second problem is the time bound sequencing of the jobs, the standard job shop problem. Hutchison et al. (1989) report on a comparison of the performance of the above two methodologies and another methodology which was based on dispatching rule (SPT). A novel feature of their simulation experiment is their use of a measure of flexibility: probability of an alternate machine option for any operation. They concluded that the programming formulations produced substantial improvement in makespan over the dispatching rules. However, as compared to the decomposed problem, the unified formulation did not produce significant improvement in makespan to justify the additional computational effort required.

      In the above approach, the tool magazines do not constrain the system. Hence the first subproblem of the decomposition can allocate all the jobs to their machines. However, when the tool magazine is considered restraining, it may not

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be possible to allocate all the jobs for one tooling setup. Then this subproblem resolves to a selection problem. Out of the pool of waiting jobs, jobs are selected to be processed in the next planning period (part type selection problem). The selected parts are then sequenced. The process is repeated period by period. In this approach, it is assumed that at the beginning of each planning period all the tools are reassigned and replaced in the tool magazine.

      Shanker and Tzen (1985) propose a mathematical programming approach to solve this part selection problem for random FMS. Their approach is similar to (Stecke, 1983). Stecke assumes the part ratio as given and the planning horizon as indefinite whereas Shanker and Tzen consider individual parts and a fixed planning horizon. They have a constraint on the tool magazine capacity which is very similar to Stecke's. They constrain the model to find a unique routing for each part type (in contrast to Stecke). Two objectives are considered: 1) Balancing the workload, and 2) Balancing the workload and minimizing the number of late jobs. The resulting problems are, again, non-linear integer problems. Even after linearization, the problems are computationally too expensive, and they further propose two heuristics corresponding to the two objectives. For balancing the workload, they propose essentially a greedy heuristic which attempts to allocate to the most lightly loaded machine the longest operation first. For the second objective, the same heuristic is modified to include the overdue jobs with the highest priority. Their computational experience showed that the analytical formulations would be too formidable to be of practical use. Shanker and Srinivasulu (1989) modify the objective to consider the throughput also. A computationally expensive branch and backtrack algorithm is suggested as well as heuristics.

      In the above approaches for random FMS, the scheduling of the FMS is decomposed into two problems: part type selection, and sequencing of jobs. The

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sequencing is done using one of the dispatching rules. Of course, some (e.g. branch and bound) search could be used to solve the sequencing problem too. Hwan and Shogun (1989) present the part selection problem for a random FMS with machines of a single general purpose type capable of producing all part types. They include the due date and the quantity of parts needed to be produced in their formulation. By ignoring the tool overlapping (cf. Stecke, 1983), they considerably simplify the tool magazine constraint. Their objective is to maximize the number of part types selected over a planning horizon. They take care of due dates by weighting on the selected part types. By assuming a single machine type, their problem essentially boils down to maximizing the utilization of the tool slots in the tool magazines. They report computational experience on two Lagrangian relaxation techniques they used to solve the problem. Their heuristics and Lagrangian methods obtained solutions close to optimal solutions found by the branch and bound method. The CPU times required by the three methods are successively order of magnitudes higher.

      Sarin and Chen (1987) approach the loading problem from the viewpoint of machining cost. Computational methodologies to solve the integer programming formulation are proposed. Ram et al. (1990) consider this problem as a discrete generalized network and present a branch and bound procedure. Co et al. (1990) have suggested a four pass approach to solve the batching, loading and tool configuration problems of random FMS. In this approach, compatible jobs are batched together using integer programming. The solution is then improved upon in three further stages.

      Jaikumar and Van Wassenhove (1989) propose a hierarchical planning and scheduling decomposition of FMS operation problems. In the first level, an aggregate production model is used. This is a linear programming model that chooses parts to be produced in a FMS during the next planning period. The

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remaining parts are assumed to be produced elsewhere at a cost difference. The objective is to maximize the cost difference while allowing for the inventory cost for work in process. The essential constraints are the demand for the parts and the machine capacity. Put simply, the objective of the second level is to minimize tool changeover. The production requirements and the tool and machine allocation are determined in levels one and two. All that remains in the third level is to determine a feasible schedule that will fulfil the above requirements. Detailed requirements such as buffer requirements, and material handling constraints, are taken care of at this level. Jaikumar and Wassenhove recommend simulation using some dispatching rule to carry out this level. If a feasible schedule cannot be obtained, the planning process is reiterated. They discuss the application of their framework in an existing FMS and point out that the primary problem is at the first level - selection of parts. Once this is decided upon, the other two problems can be solved by simple heuristics.

      Mathematical models in the literature are not efficient for reasonably sized problems. Further, they make simplifying assumptions which are not always valid in practice. The assumptions, of course, change with the models: some models assume automatic tool transport, some others will neglect delays caused by automated guided vehicles (AGV), still others will assume that tool magazines, pallets and fixtures do not constrain the models in any way, and so on. The models also take a static view of the shop floor. It is assumed that all the planned activities will be carried out exactly, or the disruptions are infrequent enough that periodic solution of the problems will be practical.

2.2. Multiple-criteria decision making approach

Operating an FMS is an activity with multiple criteria. Some authors have

brought in these criteria in their modelling. Lee and Jung (1989) formulate a part

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selection and allocation problem using goal programming. Their model considers the goals of 1) meeting production requirements, 2) balancing of machine utilization, and 3) minimization of throughput time of parts. Deviational variables representing the under- and over- achievement for each of the goals are used to measure the deviation from the goal. The model casts even the technological constraints into goal constraints. The goal programming model of Lee and Jung can provide the decision maker with a satisficing solution for given goals and their prioritization. But even with restrictive assumptions, the model is computationally expensive for practical use.

Ro and Kim (1990) discuss heuristics for solving six operational control subproblems considering the criteria of makespan, mean flowtime, mean tardiness, maximum tardiness, and system utilization to solve sub-problems.

      O'Grady and Menon (1987) present a case-study where multiple criteria were used in making decisions about master scheduling a FMS. Conflicts are resolved by using assigned weights for the criteria of tool magazine use, machine utilization, due-date performance, and choice of sold products. Integer programming formulation is used. Kumar et al. (1990) present a multi-criteria approach to the loading and grouping problems in a FMS. Their approach aims to provide a large number of feasible solutions (and objectives) for the choice of the decision maker.

      Optimization of FMS operations is difficult. It is even more difficult to do it with multiple criteria. But in view of the multi-objective nature of the operation problems, much work needs to be done in this area, and we have just seen the beginning of this approach.

2.3. Heuristics oriented approach

To counter the mathematical difficulties with optimization, use of heuristics

has been actively investigated. These heuristics may take the usual form of dis

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patching rules or they may be more complicated. Extensive study of dispatching rules have been carried out in the general job shop context (Conway 1965; Conway 1965b; Gere 1966; Panwalker and Iskander 1982). In the same vein, numerous simulation studies of dispatching rules have been carried out in the FMS area.

      Nof et al. (1979) carried out a study of different aspects of planning and scheduling of FMS. They explore the part mix problem, part ratio problem, and process selection problem. In the scheduling context, they report on three part sequencing situations: 1) Initial entry of parts into an empty system, 2) General entry of parts into a loaded system, 3) Allocation of parts to machines within the system (dispatching rules). They examined three initial entry control rules, two general entry rules, and four dispatching rules. Their conclusion was that all these issues were interrelated: performance of a policy in one problem is affected by choices for other problems.

      Stecke and Solberg (1981) investigated the performance of dispatching rules in a FMS context. They experimented with five loading policies in conjunction with sixteen dispatching rules in the simulated operation of an actual FMS. Under broad criteria, the shortest processing time (SPT) rule has been found to perform well in a jobshop environment (Conway,1965 ; Conway, 1965b). Stecke and Solberg, however, found that another heuristic - SPT/TOT, in which the shortest processing time for the operation is divided by the total processing time for the job - gave a significantly higher production rate compared to all the other fifteen rules evaluated. Another surprising result of their simulation study was that extremely unbalanced loading of the machines caused by the part movement minimization objective gave consistently better performance than balanced loading. Iwata et al. (1982) report on a set of decision rules to control FMS. Their scheme selects machine tools, cutting tools, and transport devices in a hierarchical framework. These selections are based on three rules which specifically consider the alternate

13

resources. Montazeri and Van Wassenhove (1990) have also reported on simulation studies of dispatching rules.

      Buzacott and Shanthikumar (1980) consider the control of FMS as a hierarchical problem: a) Pre-release phase, where the parts which are to be manufactured are decided, b) Input or release control, where the sequence and timing of the release of jobs to the system is decided, and c) Operational control level, where the movement of parts between the machines is decided. Their relatively simple models stress the importance of balancing the machine loads, and the advantage of diversity in job routing. Buzacott (1982 ) further stresses the point that operational sequence should not be determined at the pre-release level. His simulation results showed that best results are obtained when: 1) For input control, the least total processing time is used as soon as space is available, and, 2) For operational control, the shortest operation times rule is used.

      In the study of Shanker and Tzen (1985), the formulation of the part selection problem is mathematical; but its evaluation was carried out in conjunction with dispatching rules for scheduling the parts in the FMS. Further, on account of the computational difficulty in the mathematical formulation, they suggested heuristics to solve the part selection problems too. On the average, SPT performed the best. Moreno and Ding (1989) take up further work on heuristics (for part selection) as mentioned above, and present two heuristics which reportedly give better objective values than the heuristics in (Shanker and Tzen, 1985). This, however, they are able to do by increasing the complexity of the heuristics. Their heuristic is 'goal oriented' - in each iteration, they evaluate the alternate routes of the selected job to see which route will contribute most to the improvement of the objective. Otherwise, their heuristic is the same as that of Shanker and Tzen.

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      Chang et al. (1989) report on a heuristics based beam search technique designed to solve the random FMS scheduling problem. The root of their search tree has no operation scheduled. They progressively go along the time line and schedule more and more operations until at the final leaf, all the operations are scheduled. At each node, to evaluate the schedule, they carry out a simulation using the SPT rule. This SPT rule identifies the critical path in the schedule. For the first machine in the critical path, they evaluate all the possible alternate assignments. Only a certain number (beam width) of assignments is then selected depending on the makespan obtained. A contribution of Chang et al. is a measure of flexibility of the manufacturing system. This is called the flexibility index. It denotes the average number of workstations able to process an operation. Flexibility index is 1 for the conventional job shop. For various values of the flexibility indices, they compare their algorithm against several dispatching rules. As can be expected, their algorithm gives better results than the dispatching results at the cost of increased computational effort. It can also be seen that as the flexibility of the FMS increases, even a beam width of 1 gives very good results.

      Chang and Sullivan (1990) propose a reduced enumeration algorithm for generating sets of active schedules for FMS. Test problems showed this to be an effective approach compared to complete enumeration.

      Donath (1988) developed a heuristic based hierarchical methodology to schedule a FMS in near real-time. In his approach, at every point of decision, e.g. completion of a job, a program called 'SCHEDULE' is run. This makes decisions on the next assignment of assignable operations. His decomposition has two main subproblems. In the first, a cost of assigning an operation to a machine is calculated on the basis of process time, idle time, and the average time for that operation. Secondly, a generalized assignment problem is solved to assign the jobs to the machines. All the pending operations are assigned even if they were

15

assigned already (but not carried out). The runtime of SCHEDULE is said to be near real time (about a minute).

      Slomp et al. (1988) consider three quasi on-line procedures for scheduling FMS's. These procedures are essentially heuristic rules for the selection of a work

station, a transport device, and an operator. The selections are made

hierarchically, and the three procedures differ in the way these selections are placed in the hierarchy. In the Function Sequential Scheduling (FSS) procedure, the selections of workstation, transport device, and the operator are made for each operation sequentially. The Function Integrated Scheduling (FIS) makes all the three assignments simultaneously. In the Function Phased Scheduling (FPS) procedure, the workstation assignments are completed first, in phase one; then, the transport device and operator assignments are made in phase two. When the makespan is used as the criterion, the SPT/TOT rule performed the best. This result is the same as that of Stecke and Solberg (1981), although their criterion was the production rate. Slomp et al. concluded that FPS performed worse than FIS and FSS, and that FIS is to be favoured when there is heavy workload on transport devices and operators, otherwise FSS is recommended.

      Co et al. (1988) describe an investigation of scheduling rules for FMS where they found that performance (mean flow time) of jobs is insensitive to some common dispatching rules so long as the FMS is loaded lightly (less than 2 jobs/machine). Choi and Malstrom (1988) used a physical simulator to assess several dispatching rules. Wilhelm and Shin (1985) tested the efficacy of three levels of alternate operations in FMS. Adaptive, dynamic control of alternate operations was found the most effective. Denzler and Boe (1987) investigated heuristic loading rules to decide on the part to be introduced next into an FMS. Very simple rules were found quite effective. Sabuncuoglu and Hommertzheim (1992) investigated dispatching rules in the context of AGV scheduling rules using

16

discrete event simulation. The effectiveness of scheduling rules was demonstrated particularly for higher utilization levels. Among the rules for selecting jobs by machines, SPT performed well, while for selecting jobs for AGV's the rule of shortest travelling distance (STD) and largest queue size (LQS) performed well. Co et al. (1990) have compared the performance of two machine selection heuristics combined with three grouping heuristics from multiobjective points of view.

      Mukhopadhyay et al. (1991) have developed an integrated heuristic approach to tool allocation, parts scheduling, pallets scheduling, machine scheduling, and AGV scheduling. Priority rules and the analytical hierarchy process (Saaty 1980) are used to make a series of operating decisions.

      Heuristic rules are excellent for dynamic problems. Some of them, for instance, SPT, have very little computational overhead, and still give good results. As discussed above, extensive evaluations of conventional dispatching rules are now available in the context of FMS. There is much scope for developing and evaluating heuristics for other operational problems specific to FMS.

2.4. Control theoretic approach

      Gershwin et al. (1986) present a control theoretic perspective on the production control aspects of FMS. Kimemia and Gershwin (1983) presented a closed loop hierarchical formulation of the FMS scheduling problem. Akella et al. (1984) describe the performance of a simulated model of an actual facility using this hierarchical policy. A FMS is considered where parts are manufactured to meet a certain demand which could be varying over time. There is a penalty for exceeding the demand as well as not meeting it. Thus it would be best to produce exactly at the same rate as the demand; but this cannot be done on account of the failure of the machines. Stochastic machine failures are considered, which are smoothed by providing buffers of the parts. The heart of this control theoretic scheduling policy

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is to maintain a steady safety buffer of the parts produced in the FMS, as long as it is feasible to do so. A characteristic of the framework is that it is constrained to find a solution within the production capacity of the FMS. For each machine state, a capacity state can be defined which is the set of possible production rate vectors. For each machine state, a safety buffer level is defined for each part type. At any point in time, the production rate vector is found by solving a linear program to minimize the production costs. Their hierarchy is based on the frequency of events. Decisions about events of higher frequency is made at a lower level of hierarchy. Three levels of hierarchy are suggested. The frequency of events at a particular level is an order of magnitude smaller than that at a lower level. The top level of the hierarchy calculates the safety buffer levels for each machine state. At the middle level, calculations need to be done more frequently. From the parameters given by the top level, the vector of cost coefficients is calculated, and the linear program is solved. This is to be done on-line. This results in a vector of production rates. The lowest level of the hierarchy dispatches parts in such a way that the flow rates established at the middle level are achieved.

      A rigorous formulation of the above hierarchical framework is provided by Gershwin (1989). The simulation results of Akella et al. show that their hierarchical scheduling methodology produces high output with low work in process. It is able to track the demand on the system very closely while coping with disruptions due to machine failure. As can be seen, the closed loop control policy is tailored for a dedicated FMS producing a particular part mix. The tooling of the FMS, buffer capacity and other constraints are not considered. It is assumed that the input of a part is a sufficient control decision, and the (alternate) routing, possible deadlocks, blocking, etc. need not be considered. Further, the possible effect of long total processing times of parts in the FMS on the feedback loop is ignored.

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      Han and McGinnis (1989) present a discrete time control method for a FMS cell. Their objective is to minimize the stockout cost under time-varying demand from downstream cells. A single-stage cell with one or more workstations working in parallel is considered. Machine failures, limited buffer capacities, and varying inputs from upstream cells are considered. The control scheme periodically solves an optimization model to determine the flow of parts.

2.5. Simulation based approach

      Recently some authors have presented discrete event simulation as a scheduling tool. Basically, simulation is proposed as a tool to evaluate the dispatching rules. This is not an entirely new approach: the study by Conway (1965, 1965b) was based on simulation. What is new is that the authors suggest using data from the actual FMS for simulation. Thus a simulation model of the 'real production system' is built. The simulation model is initialized to the exact current state of the factory. The dispatching rules are then tested on this model.

      Davis and Jones (1989) propose concurrent simulation to carry out production scheduling. In their scheme, multiple simulators of a production facility are initialized to the latest state of a FMS. These simulators are stopped after some time. The simulations are then analyzed as terminating simulations to decide on the best rule to use.

      Synergism between expert systems and simulation is used in an on-line scheduling system called ESS (Expert System Scheduler). Jain et al. (1989) describe the development of a scheduling system which communicates on-line with the factory control system, generating schedules in real-time. The scheduling decisions are based on the expertise of an experienced scheduler. The system is based on LISP, and uses object-oriented concepts for both the expert systems and simulation. It is possible to run the simulation backward in time to obtain starting

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time-windows for jobs. The major reason for implementing backward simulation was implementation of JIT concepts. With this concept the job can be started at the latest possible time. Conflicts are resolved by shifting individual jobs in the schedule forward or backward. The system reacts interactively with the user, and permits solicitation of more information by the user, or changing of the schedule. At the time this article was written, the system had been controlling production at an automated manufacturing facility for several months.

      Wu and Wysk (1989) report on a multi-pass expert control system (MPECS) which uses discrete-event simulation for on-line control and scheduling in flexible manufacturing systems. In their system, simulation is used to evaluate dispatching rules. An expert system is employed to compile the set of candidate dispatching rules (Wu and Wysk, 1988). This expert system has a learning module to learn from past decisions. The expert system generates the candidate set on the basis of current system objectives, system status, and the characteristics of ongoing operations. A 'Flexible Simulation Mechanism' (FSM) collects all the data on the current system status. A simulation model is then generated based on this data. A series of simulation runs is carried out starting from the current state using each of the candidate dispatching rules for the next short time period (dt), selected by the user. FSM provides performance measures for each of the runs. The rule that results in the best performance is used to generate a series of commands to the real-time control system of the FMS. The FMS is then run for time dt under the 'best' dispatching rule.

Compared to single-pass heuristic scheduling, Wu and Wysk report an improvement of 2.3%-29.3% under different simulation windows (= dt) and measures of performance. Selection among waiting jobs for operation in a machine is, however, just one of the decisions that need to be made on the shop floor. Although Wu and Wysk's control system addresses flexible manufacturing, it is not clear how or

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if other decisions in FMS, e.g. routing selection, tool change, AGV selection, etc. are handled in this system.

      Ishi and Talavage (1991) propose a time-series based algorithm for determining the length of the simulation window. This is done on the basis of the system state which is evaluated by a measure similar to the utilization of the FMS. Strategies are proposed to select a dispatching rule avoiding the problem of censored data with arbitrary simulation windows. Improvements in performance measures of up to 16.5% are reported.

      Simulation is certainly more tractable than mathematical programming formulations of FMS operating problems. With simulation, there is no concern about feasibility, since there is no need to make any unnecessary simplifying assumptions. The simulation model can be built as close to reality as one needs to. Simulation can work as a decision support tool when there is the possibility to simulate under different decision alternatives. When considered as a candidate system for on-line control, response time of the scheduling system is a major concern. The response time would also depend on the number of candidate rules evaluated. This issue can only be resolved by further investigations into this new approach.

2.6. Artificial intelligence based approach

      Artificial intelligence (AI) appears to be particularly suited to solving operation problems of FMS because AI was developed to solve similar problems - problems involving a large search space, and where human expertise can find reasonable solutions pretty fast. Many researchers have sought to utilize this similarity. So far, two techniques of AI have found use in the FMS literature: Expert Systems and Planning. Expert systems attempt to emulate a human expert. Planning, also called problem solving, concerns itself with situations where

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there is a goal, and different actions have to be planned to achieve the goal. Steffen (1986) has presented a survey of AI based scheduling systems. These systems were developed to schedule production systems, not necessarily a FMS. Kusiak and Chen (1988) have also reviewed a number of AI-based scheduling approaches. Many authors have written on use of AI in manufacturing (Bullers et al. 1980, Fox et al. 1982, Bourne and Fox 1984, Bensana et al. 1988; Chiodini 1986). Although these concern themselves with scheduling production in general, they are relevant to FMS operation.

      Hall (1984) proposes use of if-then rules for process determination, sequencing and scheduling. However, no description of the system or the results obtained are given. Sauve and Collinot (1987) describe an object-oriented system to represent FMS which produces daily off-line schedules using knowledge about constraints and flexibility factors. This system also provides for on-line control which analyses effects of disturbances upon the daily schedule and responds with a local modification of the schedule.

      Bruno et al. (1986) present a rule-based system to schedule production in a FMS. They use expert systems to capture knowledge about the domain, and queuing network analysis for performance evaluation. The expert system uses rules to select production lots to introduce into the FMS. Primarily, the lots are selected on the basis of the dispatching rule of critical ratio. A lot with highest priority may not be scheduled if a constraint is violated. Production constraints such as release time, needed fixtures, maintenance, etc. are checked. Capacity constraints such as system congestion and throughput are checked by a heuristic based on the mean value analysis of closed queuing network. This heuristic calculates the machine utilization, average queue lengths, and lot throughputs. A simulation model is used to obtain the system state trajectory using the rule base and the performance analyzer. This trajectory is the resulting schedule. It is well

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known that mean value analysis calculates steady state performance. However, a FMS is a dynamic entity where the operating conditions are continually changed by the very actions of the scheduler and by the vagaries of nature. Thus a concern is the validity of the results of mean value analysis for use in decisions about production lot introduction.

      A nonlinear planning algorithm for FMS scheduling is proposed by Shaw (1988). This approach is based on the A* search, where one starts from an initial state and by applying successive operators (from a rule base), the goal state is finally reached. In this methodology, the jobs are individually scheduled using this search procedure. These schedules are not feasible, due to the simultaneous contentions on the resources. A plan-revision procedure is used to resolve the contentions. Shaw found that a) good heuristic knowledge is important for improving the computation efficiency of the scheduling algorithm; b) a global heuristic is better than a local heuristic; and c) a domain specific heuristic is better than a general heuristic. Unlike many other FMS scheduling methodologies, this methodology explicitly considers alternate job routing, and incorporates it in the optimization. Although it will use AI heuristics to limit the search, the search space is still very large and may make it prohibitively expensive to use in practical scheduling problems.

      Park et al. (1989) describe a Pattern Directed Scheduler (PDS) which learns the selection of best dispatching rule from simulation. Simulation was performed under varying combinations of FMS attributes such as buffer size, relative machine workload, and machine homogeneity. The resulting mean tardiness was used to develop a decision tree for selection of a scheduling rule. The performance of the PDS was found almost identical to that of the best dispatching rule.

      O'Grady et al. (1987) have described highly centralized and highly decentralized modes of intelligent control of FMS cells. O'Grady and Lee (1988)

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have proposed a multi-blackboard/actor framework (PLATO-Z) for the control of a FMS cell. This system would then be part of a hierarchical control scheme of the FMS. PLATO-Z has four blackboards whose functions are: scheduling, operation

dispatching, monitoring, and error handling. The blackboard system was

originally proposed in the HEARSAY-I speech understanding project (Barr and Feigenbaum, 1981). It has multiple 'knowledge sources' (KS) , which are expert systems, each with their own field of expertise. KS's are activated under specified conditions. A 'scheduler', which is itself a specialized knowledge source, sequences the different knowledge sources. These KS's work cooperatively to solve the problem at hand. KS's communicate with each other through generally accessible messages - hence the name 'blackboard'. Blackboard architecture based planners are particularly suitable (Young 1988) for factory scheduling: 1) they can be driven by external events posted on the blackboard; 2) independent knowledge sources lend themselves to ease of modifications. The knowledge sources called on by the blackboard in PLATO-Z are not just rule-based. They could be heuristic algorithms and optimizing procedures. The FMS is monitored in detail: part status, machine-status, material handling, buffer capacity. This approach is particularly attractive since it supports a distributed control scheme.

      Chryssolouris et al. (1988) report on the performance of a decision-making framework (MADEMA) as compared to traditional dispatching rules in a simulated environment. MADEMA uses decision analysis techniques of determining the feasible alternatives, determining relevant criteria, and determining the consequences of the alternatives. It then uses rules to select the best alternative. The alternate routing is determined within the framework through a process planning interface. The simulation results showed that MADEMA performed better than the best dispatching rule.

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      Kusiak (1986) presents a FMS scheduling system which uses a rule-based Expert System. This system follows priority rules to schedule jobs normally, but when a job cannot be scheduled because of resource conflicts, decision tables are used to select alternative machines, tools, fixtures, material handlers. In order to resolve resource conflicts, Kusiak (1989) proposes a knowledge and optimization-based scheduling system (KBSS). KBSS has an inference engine that can draw upon a knowledge base, an algorithm (optimization) base, and a database.

      Chandra and Talavage (1991) describe a FMS where a part goes to a general queue after finishing an operation. When a machine is idle, it picks up a part from this queue using an intelligent dispatcher. This scheme gave better performance than common dispatching rules. Maley et al. (1988) report on an object-oriented planning module which can capture dynamic data, simulation information, and

past history to 'learn'. It can also use optimization or heuristics to

schedule/control an FMS. Bu-Hulaiga and Chakravarty (1988) present another object-oriented framework which collects data in real-time from the factory floor, checks for variance from production targets, and suggests feasibility of re-tooling if there is a variance.

      So far, use of AI approach to FMS operation problems has addressed general problems, but restricted in size. AI techniques have shown good results for domain-specific problems. The need exists for applying these techniques to particular case-studies of FMS operations to determine the desirability and feasibility of this approach.

      The classification of the literature based on the methodology followed is done in Table 1.

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Table 1. Classification from the Methodology Viewpoint

Methodology Publication

Mathematical programming

Multi-criteria decision making

Stecke 1983; Shanker and Tzen 1985; Kimemia and Greshwin 1985; Berrada and Stecke 1986; Sarin and Chen 1987; Lashkari et al. 1987; Avonts and Van Wassenhove 1988; Hwan and Shogun 1989; Shanker and Srinivasulu 1989; Wilson 1989; Hutchison et al. 1989; Jaikumar and Van Wassenhove 1989; Han et al. 1989; Ram et al. 1990; Co et al. 1990; Chen and Chung 1991

O'Grady and Menon 1987; Lee and Jung 1989; Ro and Kim 1990; Kumar et al. 1990

Heuristics Nof et al. 1979; Stecke and Solberg 1981; Buzacott 1982;

Iwata et al. 1982; Wilhelm and Sarin 1985; Shanker and Tzen 1985; Denzler and Boe 1987; Co et al. 1988; Choi and Malstrom 1988; Donath and Graves 1988; Slomp et al. 1988; Jaikumar and Van Wassenhove 1989; Chang et al. 1989; Chang and Sullivan 1990; Mukhopadhyay et al. 1991; Sabuncuoglu and Hommertzheim 1992

Control

theoretic

Simulation

based

Artificial intelligence

Kimemia and Greshwin 1983; Akella et al. 1984; Han and McGinnis 1989

Wu and Wysk 1989; Davis and Jones 1989; Jain et al. 1989; Ishi and Talavage 1991

Bruno et al. 1986; Kusiak 1986; Sauve and Collinot 1987; O'Grady et al. 1987; Shaw 1988; Chryssolouris et al. 1988; Wu and Wysk 1988; Maley et al. 1988; Bu-Hulaiga and Chakravarty 1988; O'Grady and Lee 1988; Kusiak 1989; Park et al. 1989; Chandra and Talavage 1991;

3. Application area of the research

      In the previous section, we considered the literature from the viewpoint of the methodological approach employed. Another perspective is that of the type of targeted FMS. FMS's may be classified on the basis of their complexity (Dupont 1982) or on the basis of the diversity of the machined parts (Rachamadugu and Stecke 1989). The dedicated FMS problem assumes a fixed part mix. The part mix

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is selected from the total production requirement of the company. When the machines in the FMS are grouped, and loaded with the parts, the operation of the parts is allocated to the machines. Then until the production allocation is changed again, the FMS is operated in the same way as a job shop since the allocation of operation and tooling of the machines is taken care of. If the parts visiting the machine are not selected in advance, the operations need to be allocated as the parts arrive and the machines need to be tooled correspondingly. This type of FMS is called random FMS. From the viewpoint of variety of parts handled, the FMS literature may be classified broadly as being applicable to:

1. Dedicated FMS

2. Random FMS

3. Flexible Assembly Systems

A flexible assembly system is limited to the assembly of very few product

types. A dedicated FMS is configured to machine few pre-selected parts, whereas the random FMS handles a wider variety of parts, its configuration (tool-mounting) changing as needed. Most of the early literature was focused on the part selection problem of dedicated FMS. There has been a wide interest in the loading problem of random FMS. A classification of literature on this basis is given in Table 2.

4. Planning horizon

      Researchers have looked at the scheduling and control problems from different temporal viewpoints. Some have looked at the long-term planning of FMS, while others have addressed real-time issues of controlling FMS. The following is a convenient taxonomy to classify the literature from this viewpoint.

1. Planning problems

2. Scheduling problems

3. Realtime control problems

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Table 2. Classification on the Basis of Application Area

Application Publication

area

Nof et al. 1979; Stecke and Solberg 1981; Buzacott 1982; Stecke 1983; Kimemia and Gershwin 1983; Akella et al. 1984; Kimemia and Gershwin 1985; Wilhelm and Sarin 1985; Berrada and Stecke 1986; Sarin and Chen 1987; Denzler and Boe 1987; Lashkari et al. 1987; O'Grady and Menon 1987; Slomp et al. 1988; Avonts and Van Wassenhove 1988; Choi and Malstrom 1988; Lee and Jung 1989; Wilson 1989; Kumar et al. 1990; Ro and Kim 1990; Ram et al. 1990; Ishi and Talavage 1991; Chen and Chung 1991

Iwata et al. 1982; Shanker and Tzen 1985; Bruno et al. 1986; Kusiak 1986; Sauve and Collinot 1987; O'Grady et al. 1987; Shaw 1988; O'Grady and Lee 1988; Co et al. 1988; Chryssolouris et al. 1988; Park et al. 1989; Kusiak 1989; Hwan and Shogun 1989; Han et al. 1989; Davis and Jones 1989; Hutchison et al. 1989; Jaikumar and Wassenhove 1989; Shanker and Srinivasulu 1989; Wu and Wysk 1989; Chang et al. 1989; Jain et al. 1989; Chang and Sullivan 1990; Co et al. 1990; Mukhopadhyay et al. 1991; Chandra and Talavage 1991; Sabuncuoglu and Hommertzheim 1992

Donath and Graves 1988; Graves 1988

       Planning problems are long term problems including loading, grouping, selection of parts for manufacturing in a FMS, etc. Most of the literature on dedicated FMS is on planning problems. Resource allocation problems with smaller time horizon are the scheduling problems. Except for the heuristic approaches, few authors have worked in this area. Still fewer authors have written on the real-time problem of dynamically controlling an FMS. Table 3 presents a classification of literature on this basis.

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Table 3. Classification on the Basis of Planning Horizon

Time Publication

horizon

Stecke 1983; Shanker and Tzen 1985; Berrada and Stecke 1986; Lashkari et al. 1987; O'Grady and Menon 1987; Sarin and Chen 1987; Avonts and Van Wassenhove 1988; Hwan and Shogun 1989; Wilson 1989; Jaikumar and Wassenhove 1989; Lee and Jung 1989; Ro and Kim 1990; Ram et al. 1990; Kumar et al. 1990; Chen and Chung 1991; Co et al. 1991

Nof et al. 1979; Iwata et al. 1982; Shanker and Tzen 1985; Bruno et al. 1986; Sauve and Collinot 1987; Denzler and Boe 1987; Shaw 1988; Co et al. 1988; Choi and Malstrom 1988; Chryssolouris et al. 1988; Kusiak

1986 and 1989; Shanker and Srinivasulu 1989;

Hutchison et al. 1989; Jaikumar and Wassenhove 1989; Chang et al. 1989; Jain et al. 1989; Chang and Sullivan 1990; Chandra and Talavage 1991; Mukhopadhyay et al. 1991; Sabuncuoglu and Hommertzheim 1992

Stecke and Solberg 1981; Buzacott 1982; Akella et al. 1984; Kimemia and Gershwin 1985; Wilhelm and Sarin 1985; Sauve and Collinot 1987; O'Grady et al. 1987; O'Grady and Lee 1988; Slomp et al. 1988; Donath and Graves 1988; Bu-Hulaiga and Chakravarty 1988; Davis and Jones 1989; Park et al. 1989; Han et al. 1989; Wu and Wysk 1989; Ishi and Talavage 1991;

5. FMS factors considered

      There is great divergence in the literature in the type of FMS considered. For most of the writers, the flexibility in routing seems to be the main feature of FMS. Many other authors have included the tool-slots of the workstations in their discussions. Some authors have ignored both of these flexibilities. Similar diversity exists in the consideration of pallets, material transporters etc. Very few authors have considered all the facets of FMS simultaneously. Based on this consideration, Table 4 depicts a classification of the available literature.

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Table 4. Factors Considered in the Literature

Reference Route Tool Part Machine Buffer Pallets

flexi slots tran avail- spaces

bility sport abillity

Kimemia and Gershwin 1985; Wilhelm and Sarin 1985; Shaw 1988; Chryssolouris et al. 1988; Donath and Graves 1988; Chang et al. 1989; Avonts and Van Wassenhove 1988; Chandra and Talavage 1991

Nof et al. 1979; Stecke and Solberg 1981; Stecke 1983; Shanker and Tzen 1985; Berrada and Stecke 1986; O'Grady and Menon 1987; Sarin and Chen 1987; Bu-Hulaiga and Chakravarty 1988; Hwan and Shogun 1989; Han et al. 1989; Hutchison et al. 1989; Jaikumar and Wassenhove 1989; Shanker and Srinivasulu 1989; Jain et al. 1989; Kumar et al. 1990; Ram et al. 1990; Co et al. 1990; Chen and Chung 1991

Y N N N N N

Y Y N N N N

Lashkari et al. 1987; Wilson 1989 Y Y N N N Y

Davis and Jones 1989; Ishi and N N Y N N N

Talavage 1991

Sauve and Collinot 1987 Y Y N N Y N

Bruno et al. 1986; Choi and Y N N Y N Y

Malstrom 1988

Park et al. 1989 Y N N N Y N

Kusiak 1986 and 1989; Y Y Y N N Y Mukhopadhyay et al. 1991;

Iwata et al. 1982; O'Grady and Lee Y Y Y N Y N

1988; O'Grady et al. 1987

Chang and Sullivan 1990; Y N Y N N N

Buzacott 1982; Ro and Kim 1990 Y N Y N Y N

Slomp et al. 1988 Y N Y N Y N

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Akella et al. 1984 N N Y Y N N

Denzler and Boe 1987; Lee and Y N N N N Y

Jung 1989

Co et al. 1988; Wu and Wysk 1989 N N N N N N

Sabuncuoglu and Hommertzheim N N Y N Y N

1992

Han and McGinnis 1989 N N N Y Y N

6. Conclusions for future research

      Great strides have been made in the scheduling and control literature of FMS. There is now a mature literature using different methodological approaches. Future work needs to be done in investigating the use of the methodologies in the practical arena, in making the control systems more user-friendly, and in developing more comprehensive control systems.

      FMS control problems are very complex and difficult. Rather than attempting to get the optimum solutions of the problem formulations, research should be done on interactive scheduling and control of FMS where there is human input in the loop. Godin (1978) presents a review of interactive scheduling. Adelsberger and Kanet (1989) provide a more recent review of the state of art in interactive scheduling. A decision support system approach including interactive scheduling has a lot of promise for application in the operations of FMS. Samadi et al. (1990), describe one such management tool that provides information as well as suggestions to help in operating a manufacturing system. Modern workstations provide a splendid opportunity for the development of FMS control decision support systems using the graphics capabilities, and underlying heuristics or rule-based systems.

      FMS is different things to different researchers. Quite often only the alternate operations aspect is emphasised. It is time to move on to further developing comprehensive control schemes which take care of the complex

31

interaction of the multiple resources in an FMS: transporters, CNC machines, robots, tools, fixtures, pallets. This could be done using hierarchical or heterarchical schemes.

      Discrete-event simulation is another area which has the potential to make major contributions to FMS operation. Simulation can be used to model FMS quite comprehensively, and may be used to evaluate control policies, heuristics, and rules. Distributed processing makes the use of simulation feasible. There are some published papers using a simulation approach, but usually they do not provide comprehensive modelling of FMS.

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Avonts, L.H. and Wassenhove, L.N., 1988, The part mix and routing mix problem in FMS: a coupling between an LP model and a closed queuing network. International Journal of Production Research, 26, 1891-1902.

Akella, R., Choong, Y., and Gershwin, S.B., 1984, Performance of hierarchical production scheduling policy. IEEE Transactions on Components, Hybrids, and Manufacturing Technology, CHMT-7, 215-217.

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Bensana, E., Bel, G., and Dubois, D., 1988, OPAL: A multi-knowledge-based system for industrial job-shop scheduling. International Journal of Production Research, 26, 795-819.

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34

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Davis, W.J., and Jones, A.T., 1989, On-line concurrent simulation in production scheduling. Proceedings of the Third ORSA/TIMS Conference on Flexible Manufacturing Systems, 253-258.

Donath, M., Graves, R.J., and Carlson, D.A., 1989, Flexible assembly systems: the scheduling problem for multiple products. Journal of Manufacturing Systems, 8, 27-33.

Donath, M.W., 1988, A scheduling methodology for flexible manufacturing systems. Unpublished Ph.D. Thesis, University of Massachusetts.

Dupont-Gatelmand, C., 1982, A survey of flexible manufacturing systems. Journal of Manufacturing Systems, 1, 1-16.

Fox, M.S., Allen, B., and Strohm, G., 1982, Job-shop scheduling: an investigation in constraint-directed reasoning. Proceedings of the National Conference on Artificial Intelligence, 155-158.

Gere, W.S., 1966, Heuristics in job shop scheduling. Management Science, 13, 167-190.

Gershwin, S.B., 1989, Hierarchical flow control: a framework for scheduling and planning discrete events in manufacturing systems. Proceedings of the IEEE, 77, 195-209.

35

Graves, R.J., 1988, A hierarchical system for scheduling and near real-time control of material flow in flexible assembly. Proceedings, 1988 International Industrial Engineering Conference, Institute of Industrial Engineers, Norcross, Georgia.

Gupta, Y.P., Gupta, M.C., and Bector, C.R., 1989, A review of scheduling rules in flexible manufacturing systems. International Journal of Computer Integrated Manufacturing, 2, 356-377.

Hall, M.D., and Putnam, G., 1984, An application of expert systems in FMS. Autofact 6, Society of Manufacturing Engineers.

Han, M.H., and McGinnis, L.F., 1989, Flow control in flexible manufacturing: minimization of stockout cost. International Journal of Production Research, 27, 701-715.

Han, M., Na, Y.K., and Hogg, G.L., 1989, Real-time tool control and job dispatching in flexible manufacturing systems. International Journal of Production Research, 27, 1257-1267.

Hutchison, J., Leong, K., Snyder, D., and Ward, F., 1989, Scheduling for random job shop flexible manufacturing systems. Proceedings of the Third ORSA/TIMS Conference on Flexible Manufacturing Systems, 161-166.

Hwan, S.S., and Shogun, A.W., 1989, Modelling and solving an FMS part selection problem. International Journal of Production Research, 27, 13491366.

Ishi, N., and Talavage, J.J., 1991, A transient-based real-Time scheduling algorithm in FMS. International Journal of Production Research, 29, 25012520.

Iwata, K., Murotsu, A., Oba, F., and Yasuda, K., 1982, Production scheduling of flexible manufacturing systems. Annals of the CIRP, 31, 319-322.

36

Jaikumar, R., and Van Wassenhove, L.N., 1989, A production planning framework for flexible manufacturing systems. Journal of Manufacturing Operations Management, 2, 52-79.

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Kimemia, J., and Gershwin, S.B., 1985, Flow optimization in flexible manufacturing systems. International Journal of Production Research, 23, 8196.

Kimemia, J.G., and Gershwin, S.B., 1983, An algorithm for the computer control of production in flexible manufacturing systems. IIE Transactions, 15, 353362.

Kumar, P., Tewari, N.K., and Singh, N., 1990, Joint consideration of grouping and loading problems in a flexible manufacturing system. International Journal of Production Research, 28, 1345-1346.

Kusiak, A., 1985, Flexible manufacturing systems: a structured approach. International Journal of Production Research, 23, 1057-1073.

Kusiak, A., 1986, FMS scheduling: a crucial element in an expert system control architecture. IEEE 1986 International Conference on Robotics and Automation, 653-658.

Kusiak, A., and Chen, M., 1988, Expert systems for planning and scheduling manufacturing systems. European Journal of Operational Research, 34, 113130.

Kusiak, A., 1989, KBSS: A knowledge and optimization based system for manufacturing scheduling. International Industrial Engineering Conference and Societies' Manufacturing and Productivity Symposium Proceedings, 694699.

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Lashkari, R.S., Dutta, S.P., and Padhye, A.M., 1987, A new formulation of operation allocation problem in flexible manufacturing systems: mathematical modelling and computational experience. International Journal of Production Research, 25, 1267-1283.

Lee, S.M., and Jung, H., 1989, A multi-objective production planning model in a flexible manufacturing environment. International Journal of Production Research, 27, 1981-1992.

Maley, J.G., Ruiz-Meir, S., and Solberg, J.J., 1988, Dynamic control in automated manufacturing: a knowledge integrated approach. International Journal of Production Research, 26, 1739-1748.

Montazeri, M., and Van Wassenhove, L.N., 1990, Analysis of scheduling rules for an FMS. International Journal of Production Research, 28, 785-802.

Moreno, A.A., and Ding, F., 1989, Goal oriented heuristics for the FMS loading (and part type selection) problems. Proceedings of the Third ORSA/TIMS Conference on Flexible Manufacturing Systems, 105-110.

Mukhopadhyay, S.K., Maiti, B., and Garg, S., 1991, Heuristic solution to the scheduling problems in flexible manufacturing system. International Journal of Production Research, 29, 2003-2024.

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O'Grady, P.J., and Menon, U., 1987, Loading a flexible manufacturing system. International Journal of Production Research, 25, 1053-1068.

O'Grady, P.J., Bao, H., and Lee, K.H., 1987, Issues in intelligent cell control for flexible manufacturing systems. Computers in Industry, 9, 25-36.

38

O'Grady, P., and Lee, K.H., 1988, An intelligent cell control system for automated manufacturing. International Journal of Production Research, 26, 845-861.

Park, S.C., Raman, N., and Shaw, M.J., 1989, Heuristic learning for pattern directed scheduling in a flexible manufacturing system. Proceedings of the Third ORSA/TIMS Conference on Flexible Manufacturing Systems, 369-376.

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Rachamadugu, R., and Stecke, K.E., 1989, Classification and review of FMS scheduling procedures. Working Paper #481 C, The University of Michigan, Ann Arbor, Michigan.

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39

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40

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41

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Chapter 7

Position Classification and Compensation Scheme for Faculty Positions in State Universities and Colleges

Prior to the issuance of PD No. 985, State Universities and Colleges (SUCs) which were exempted from the coverage of the National Position Classification and Compensation Plans adopted individual staff credentials and qualifications, position classification and pay plans. The disparities in pay and compensation among similar comparable positions brought about by the different schemes adopted by the various SUCs gave rise to demoralization and dissension among the ranks of faculty members and further complicated the process of compensation administration in SUCs.

When the SUCs were placed within the ambit of PD No. 985, the need to rationalize the academic ranks/salaries/advancement of faculty members in SUCs became apparent due to the application of varied faculty evaluation instruments. As early as 1982, the Philippine Association of State Universities and Colleges (PASUC), together with the DBM, started deliberating on a scheme of upgrading/promoting qualified and deserving faculty members through a process of objective evaluation. This paved the way to the development and adoption of a Common Criteria for Evaluation (CCE) across programs and disciplines which aimed to rationalize academic ranks and salaries.

National Compensation Circular (NCC) No. 33 was issued on January 2, 1985 with retroactive effect on July 1, 1984. This Circular established the position classification and compensation scheme for faculty positions in SUCs. Since then, amendments of certain provisions including improvements of the CCE have been introduced through NCC No. 68, NCC No. 69 and the latest, National Budget Circular (NBC) No. 461. NBC No. 461 is a revision and an update of NCC No. 69 which was exclusively for the faculty positions in SUCs. Under NBC No. 461, Commission on Higher Education (CHED)-supervised higher education institutions (HEIs), Technical Education and Skills Development Authority (TESDA)-supervised Technical Education Institutions (TEIs) and SUCs are covered.

7.1 Coverage

The Position Classification and Compensation Scheme For Faculty Positions (PCCSFP) covers all teaching positions involved in instruction, research and extension activities in all SUCs, CHED-Supervised HEIs and TESDA-Supervised TEIs.

7-1

Manual on Position Classification and Compensation

7.2 Common Criteria for Evaluation

As part of the PCCSFP, a CCE is established which shall be the primary basis for recruitment, classification and promotion of a faculty. The CCE is a set of factors consisting of services and achievements which establishes the relative performance of a faculty in the institution for the period of evaluation through the application of a point system in determining faculty rank and sub-rank. The new CCE which was developed by the CHED and PASUC places more emphasis on advancement and performance rather than on educational qualifications.

7.2.1 The CCE Concept and Objectives

To implement a standardized PCCSFP, it is imperative for all faculty to pass through a CCE that can distinguish the different faculty ranks within institutions, across institutions and across disciplines and fields. The CCE has the following objectives:

7.2.1.1 To standardize faculty ranks among institutions;

7.2.1.2 To rationalize the salary rate appropriate to a faculty rank;

7.2.1.3 To have an instrument for generating the faculty profile across SUCs, HEIs and TEIs;

7.2.1.4 To serve as basis for policy decisions for accelerated faculty development; and

7.2.1.5 To motivate a faculty to upgrade his/her rank and compensation by improving his/her academic qualifications, achievements and performance.

7.2.2 The Point System

The CCE point system in determining faculty rank and sub-rank is as follows:

7.2.2.1 Major Factors and Maximum Points

Factors

Maximum

Number of

Points

Educational Qualification

85

7-2

Classification and Compensation Scheme for Faculty Positions

Experience and Professional Services

25

Professional and Honors

Development, Achievement

90

TOTAL

200

The specific factors and guidelines for determining credit points are in Annex A of this Chapter.

7.2.2.2 Point Allocation Under NBC No. 461

Faculty Rank

Sub-Rank

SG

Point Bracket

Instructor

I

12

65 – Below

II

13

66 – 76

III

14

77 – 87

Assistant

Professor

I

15

88 – 96

II

16

97 – 105

III

17

106 – 114

IV

18

115 – 123

Associate

Professor

I

19

124 – 130

II

20

131 – 137

III

21

138 – 144

IV

22

145 – 151

V

23

152 – 158

Professor

I

24

159 – 164

II

25

165 – 170

III

26

171 – 176

IV

27

177 – 182

V

28

183 – 188

VI

29

189 – 194

College/University Professor

30

195-200

7.2.2.2.1 The highest rank that can be allowed in HEIs and TEIs is Associate Professor V.

7.2.2.2.2 The quota for the rank of Professor shall be 20% of the total number of faculty positions of each SUC.

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Manual on Position Classification and Compensation

7.3 Qualitative Contribution Evaluation

7.3.1 In addition to the CCE, promotions to higher rank and sub-rank shall be subject to Qualitative Contribution Evaluation (QCE). QCE is the process of determining the eligibility of a faculty candidate for the particular rank and sub-rank indicated by the CCE.

7.3.2 Qualitative Contribution (QC) is the continuous improvement towards excellence by a faculty member in all four (4) functional areas of the institution, namely: instruction, research, extension and production.

7.3.2.1 For those seeking promotion to the higher sub-rank of Instructor and Assistant Professor, the QC shall be on Teaching Effectiveness.

7.3.2.2 For those seeking promotion to the Associate Professor rank, the QC shall be in any two (2) functional areas chosen by the candidate prior to any assessment year.

7.3.2.3 For those seeking promotion to the Professor rank, the QC shall be in any three (3) functional areas chosen by the candidate prior to any assessment year.

7.3.3 For the QC of Instructors and Assistant Professors, a common evaluation instrument is prepared by a joint committee of CHED, PASUC and TESDA. The evaluation is done by the faculty concerned, his/her peers, his/her supervisor and his/her student beneficiaries.

7.3.4 For the QC of Associate Professors and Professors, a common evaluation instrument is prepared by a joint committee of CHED and PASUC. The evaluation is done by the ratee’s client, by the direct supervisor, by the stakeholders in the completed projects, and by his/her external and internal communities.

7.4 Accreditation

Accreditation is a screening process for validating the eligibility of a faculty candidate to the rank of Associate Professor or Professor. The process involves written exams and interviews, particularly on substantive issues/ questions related to the field of specialization/ discipline of the candidate.

7-4

Classification and Compensation Scheme for Faculty Positions

7.5 Determination of Appropriate Faculty Rank and Salary

7.5.1 A faculty member who is assigned on the basis of the CCE and QCE to a sub-rank higher than his/her present rank, or subsequently promoted through presidential discretion, shall be given the rank and salary corresponding to that higher rank.

7.5.2 A faculty member who merited a higher rank based on the CCE but assigned a lower rank based on the QCE shall be given the rank and salary corresponding to that lower rank.

7.5.3 In the initial implementation of NBC No. 461, a faculty member who is assigned on the basis of the CCE and QCE to a sub-rank lower than his/her present rank shall retain his/her present rank and salary.

7.6 Presidential Discretion

The Head of the SUC, HEI or TEI, may subsequently grant promotions to faculty members for meritorious performance, provided that the aggregate number of sub-ranks involved in all such promotions shall not exceed 15% of the total number of current authorized full-time faculty members annually, provided further that such upward movements shall be limited to the highest sub-rank of the assigned rank as indicated in the CCE. Upward movements to Professor ranks in SUCs and to Associate Professor ranks, in HEIs and TEIs shall similarly be subject to prior evaluation by the Accreditation Committee, to the requirement for appointment to such ranks, and to the quota system prescribed for Professors, in the case of SUCs.

7.7 Appointment to Ranks Below Professor

7.7.1 Instructor I – Entry level, total of CCE points is 65 or less.

7.7.2 Appointment to the ranks of Instructor II to Assistant Professor IV shall be subject to the following requirements:

7.7.2.1 CCE points of at least 66 for the higher sub-rank of the Instructor position and at least 88 for the Assistant Professor position;

7.7.2.2 Earned MA degree for Assistant Professor II to IV; and

7-5

Manual on Position Classification and Compensation

7.7.2.3 QC in instruction, otherwise known as Teaching Effectiveness.

7.7.3 Appointment to the rank of Associate Professor shall be subject to the following requirements:

7.7.3.1 CCE points of at least 124;

7.7.3.2 Earned MA degree;

7.7.3.3 QC in at least 2 of the 4 functional areas; and

7.7.3.4 Accreditation by a committee of experts constituted by PASUC for candidates entering the Associate Professor rank for the first time; in the case of those in HEIs and TEIs.

7.8 Appointments to Professor Ranks

7.8.1 The minimum criteria for appointment to full Professor ranks are as follows:

7.8.1.1 Education - This refers to the relevant doctoral academic degree from a college or university of recognized standing either locally or abroad. However, in highly meritorious and extremely exceptional cases as in areas of specialization or fields of discipline where there is a dearth of doctoral programs or the same are not readily available, the foregoing doctoral degree requirement may be waived.

7.8.1.2 Productivity - This refers to significant outputs, contributions and applications and/or use of research results in commercial or industrial projects in relevant fields of applied and natural sciences and includes the following:

7.8.1.2.1 Scientific articles in publications of international circulation, and other works of similar nature;

7.8.1.2.2 Discoveries, inventions and other significant original contributions;

7.8.1.2.3 Books, monograms, compendiums and major bodies of published work;

7-6

Classification and Compensation Scheme for Faculty Positions

7.8.1.2.4 Transformation of research recommendations to public policy benefiting the country’s training of science graduates or significant contribution to manpower development and/or science and technology, practical application of research results in industrial or commercial projects and/or undertakings; and

7.8.1.2.5 Such other criteria which the Accreditation Committee may require as may be warranted by new developments in science and technology.

7.8.1.3 Professional standing - This refers to the level of acceptance and recognition in the academic community in terms of professional, moral and ethical integrity.

7.8.2 The appointment to Professor ranks shall be subject to the following requirements:

7.8.2.1 CCE points of at least 159;

7.8.2.2 Earned doctorate, in the case of Professors IV to VI; where a doctorate is not normally part of career preparation, or where such doctoral program is rare as determined by CHED, the doctoral requirement may be waived, provided that the candidate has an appropriate master’s degree, and has earned 20 points in the following areas:

7.8.2.2.1 Books, monograms, compendiums, and major bodies of published work;

7.8.2.2.2 Scientific articles in publications of international circulation, and other works of similar nature;

7.8.2.2.3 Discoveries, inventions and other significant original contributions;

7.8.2.2.4 Research recommendations transformed to public policy benefiting the country;

7.8.2.2.5 Supervision, tutoring or coaching of graduate scientists and technologists; and

7-7

Manual on Position Classification and Compensation

7.8.2.2.6 Research results applied or utilized in industrial and/or commercial projects or undertaking.

7.8.2.3 QC in at least 3 of the 4 functional areas; and

7.8.2.4 Accreditation by a committee of experts constituted by PASUC for candidates entering the Professor rank for the first time.

7.8.3 Limitations

The following guidelines set the limitations for appointment to Professor ranks:

7.8.3.1 The number of Professor positions shall not exceed 20% of the total number of faculty positions in the SUC concerned; and

7.8.3.2 An applicant who fails in the accreditation process including those who qualify as Professors but are in excess of the quota for Professor ranks shall be appointed to the position of Associate Professor V.

7.9 Appointments to College/University Professor Ranks

7.9.1 The following are deemed qualified for appointment as College/ University Professors:

7.9.1.1 Deserving faculty members, occupying Professor positions who satisfy the qualification for accreditation under item 7.9.5 hereof and duly accredited by the PASUC Accreditation Committee;

7.9.1.2 SUC Presidents and Vice-Presidents or their equivalents who opt to receive the basic salary pertaining to their assigned academic rank under the CCE, and those who opt to return to teaching due to their resignation/retirement not for cause before the expiration of their fixed terms of office provided that they have complied with the requirements prescribed for College/University Professors; and

7.9.1.3 SUC Presidents/Vice-Presidents who opt to return to teaching after the expiration of their fixed terms of office

7-8

Classification and Compensation Scheme for Faculty Positions

may be appointed as College/University Professors subject to the provisions of NBC No. 461, insofar as pertinent, in addition to the slots available for deserving faculty members.

Thereafter, any vacancy arising from the retirement/ resignation of a faculty member appointed as

College/ University Professor, shall not be filled until

such time that the SUC President/Vice-President similarly appointed as College/University Professor has retired/resigned from the government service.

7.9.2 The following are the requirements for appointment as College/University Professor:

7.9.2.1 CCE points of at least 195;

7.9.2.2 Earned doctorate;

7.9.2.3 Professorial accreditation, in case of a faculty;

7.9.2.4 A pass from a Screening Committee, duly constituted by PASUC; and

      7.9.2.5 QC in at least 3 out of the 4 functional areas. 7.9.3 Limitations

The following guidelines set the limitations for appointment as College or University Professor:

7.9.3.1 Only one position of College Professor, per college, is authorized for every 6 years, the total of which shall not exceed the number of authorized colleges and external campuses of the respective SUC;

7.9.3.2 Only one position of University Professor, per University, is authorized for every 6 years, the total of which shall not exceed 5% of the total number of accredited full professors in the university concerned; and

7.9.3.3 The classification of existing College Professor positions at SG-29 whose incumbents were appointed based on the previous point allocation under NCC No. 69 shall be

coterminous with the incumbents. Hence, upward

movements of incumbents of positions of College

7-9

Manual on Position Classification and Compensation

Professor, SG-29, to the new rank of College/University Professor, SG-30, is not automatic. The salary grade of incumbents thereof who were accredited under NCC No. 69 shall remain at SG-29 until they qualify as College/University Professor based on the point allocation under NBC No. 461.

7.9.4 Screening Process

Upon recommendation by the institution head concerned, all candidates for the rank of College/University Professor shall undergo screening by an independent body, to be organized by the Philippine Association of State Universities and Colleges (PASUC).

7.9.5 Qualifications for Accreditation as College/University Professor

7.9.5.1 He/She must be an outstanding scholar and scientist as shown in the quality of his/her publications and researches in his/her principal field of study and in allied fields; or he/she must have manifested outstanding performance in his/her executive leadership role.

7.9.5.2 He/She must have expert knowledge in one field or division and familiar with at least one other subject within another division.

7.9.5.3 He/She must be known for intellectual maturity and objectivity in his/her judgment.

7.9.5.4 He/She must have a high reputation among his/her colleagues and other scholars for his/her mastery of the subject of his/her specialization.

7.9.5.5 Recognition and esteem could be manifested in the following ways:

7.9.5.5.1 His/her contributions to the advancement of his/her fields of specialization are recognized by colleagues, here and abroad.

7.9.5.5.2 He/She is published in the most respected learned journals in his/her field of specialization.

7.9.5.5.3 His/Her works are worldly acclaimed and

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Classification and Compensation Scheme for Faculty Positions

provoke spirited discussions among scholars, often from various disciplines.

7.9.5.5.4 He/She is often invited to other universities and scholarly gatherings for the originality of his thoughts.

7.9.5.5.5 He/She is accorded various forms of honors (awards, chairs, titles, etc.).

7.10 Conversion of Teaching and Teaching-Related Positions in CHED-supervised HEIs and TESDA-supervised TEIs Integrated into SUCs

7.10.1 To preclude position downgrading implications, the existing teaching/teaching related positions integrated with the staffing pattern of newly converted SUCs shall be initially converted/retitled to their lateral equivalent SUC faculty positions based on salary grades without the need for prior evaluation under NBC No. 461.

Examples:

From To

Secondary School Principal II, SG-19 Associate Professor I, SG-19

Master Teacher II, SG-17 Assistant Professor III, SG-17

Head Teacher III, SG-15 Assistant Professor I, SG-15

7.10.2 All positions of Teacher I, SG-10, Teacher II, SG-11, and Teacher III, SG-12, shall be automatically converted/retitled to Instructor I, SG-12.

7.10.3 The initial faculty ranks shall serve as bases for future movements/promotions to higher level positions. Should the ensuing evaluation under NBC No. 461 result in the downgrading of the initial ranks, the faculty concerned shall retain his/her assigned rank and salary grade at conversion until he/she qualifies for a higher rank.

7.10.4 Teaching positions handling laboratory classes in teacher education courses may be converted/retitled to faculty positions provided they serve as critic teacher in such teacher education courses and each attends to at least three (3) practicum students at the senior level.

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Manual on Position Classification and Compensation

7.11 Role of Agencies in the Implementation of NBC No. 461

7.11.1. Role of SUCs, HEIs and TEIs

The heads of SUCs, HEIs and TEIs shall submit the Personal Services Itemization and Plantilla of Personnel (PSIPOP).

reflecting the modifications in rank/sub-rank and the corresponding salary adjustments of faculty members concerned together with the CCE Computer Print-out and pertinent evaluation documents.

7.11.2. Role of DBM

The DBM Regional Offices (ROs) shall verify and post-audit the PSIPOP. The DBM ROs shall then prepare the Notice of Organization, Staffing and Compensation Action (NOSCA) reflecting the changes in the rank/sub-rank and salaries of faculty members concerned in the respective institution.

7.12 Evaluation Cycle

As a matter of policy, the evaluation may be undertaken every odd year for SUCs. In the case of HEIs and TEIs, the evaluation may be undertaken every even year.

7.13 Additional Compensation for Faculty

7.13.1 Honoraria for Teaching Overload

Faculty members are entitled to honoraria for services rendered in excess of the regular teaching load. Honoraria shall be based on the Prime Hourly Teaching Rate (PHTR) which shall be computed as follows:

7.13.1.1 For undergraduate program

AR AR

PHTR = T = x 1.25 = 0.000781 AR

W 1600

Where:

AR = annual salary rate of each faculty proposed to be paid honoraria

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Classification and Compensation Scheme for Faculty Positions

W = Total teaching hours (40hrs/week multiplied by 40 weeks or 1600 hrs.)

T = 1.25 or 125% of the faculty’s remuneration for

services in excess of 6 hours of actual teaching per

day but not more than 2 hours

7.13.1.2 For graduate program

7.13.1.2.1 For faculty members with Bachelor’s degrees and with special vocational preparation

AR

PHTR = x 1.5 = 0.0012 AR

1,296

7.13.1.2.2 For faculty members with Master’s degrees

PHTR = 0.0014 AR

7.13.1.2.3 For faculty members with Doctorate Degrees

PHTR = 0.0015 AR

7.13.1.3 Reduced Teaching Load for Faculty Assigned

with Workload Other than Teaching

In the determination of the load of a faculty who is given assignments other than teaching, the following allowable percentage weights are adopted:

* 25% of the official time of faculty members concerned shall be credited to actual teaching load; and

* 75% of the official time of faculty members concerned shall be allotted for workload other than teaching in connection with research and extension functions, or as a Dean/Department Head or Director.

7-13

              Manual on Position Classification and Compensation 7.13.2 Representation and Transportation Allowances (RATA)

Faculty members who are designated as Vice- Presidents/Deans/ Directors/Department Heads are authorized RATA based on their rank equivalence at the rates provided in the annual General Appropriations Act (GAA).

7.13.2.1 Vice-Presidents

SUC Level

Rank Equivalence

4

Bureau Director

3

Assistant Bureau Director

2

Bureau Regional Director

1

Bureau Assistant Regional Director

7.13.2.2 Deans equivalent to Assistant Bureau Regional Director

7.13.2.2.1 Designated Dean of the Graduate School with at least a Master of Arts/Master of Science Program with 15 faculty members.

7.13.2.2.2 Designated Deans of Colleges with at least four (4) degree programs and a teaching complement of 40 full-time faculty members.

In case the SUC cannot meet the minimum number of programs required, it may still be entitled to a Dean, if it meets the following:

No. of

Programs

No. of Full-Time

Faculty Members

4

40

3

50

2

60

1

70

7.13.2.3 Directors/Department Heads equivalent to Chiefs of Division

7.13.2.3.1 Designated Director of Research Services, with at least P500,000 appropriation for

research service function; when no

authorized research function in the GAA, the

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Classification and Compensation Scheme for Faculty Positions

SUC to have at least 10 research projects with a total cost of P500,000 per annum.

7.13.2.3.2 Designated Director of Extension Services, with at least P500,000 appropriation for extension service function; when no authorized extension services function in the GAA, the SUC to have at least 10 extension services projects with a total cost of P500,000 per annum.

7.13.2.3.3 Designated Director of Auxiliary Services, provided the SUC has a yearly income from its operations of at least P60,000 and at least 7 personnel involved in such income generating projects.

7.13.2.3.4 Designated Director of each satellite campus/ branch/center/institute, duly mandated by law, provided each campus/branch/center/ institute has a complete administrative staff, i.e., at least a budget officer, an accountant/bookkeeper, an administrative officer/administrative assistant, a supply officer/property custodian, a cashier/ disbursing officer and other support positions such as clerks, janitors and security guards and at least 1,000 students in the tertiary level.

7.13.2.3.5 Designated Director of Student Affairs Services for SUCs with at least 4,000 college students.

7.13.2.3.6 Designated Department Heads of different departments/colleges, each one having at least 4 degree programs with each program differentiated from each other by 33% (the distinction of the programs to be certified by the CHED).

7.13.3 Compensation of faculty/non-faculty members designated as Vice- Presidents (VP)

7.13.3.1 Faculty and non-faculty members who are designated as VPs shall be entitled to the difference between their

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Manual on Position Classification and Compensation

present salaries and the 1st step of the salary grade of the VP positions corresponding to the level of the SUCs concerned. Said salary differentials shall form part of their actual salaries as designated VPs. The year-end benefits (YEB) and retirement and life insurance premiums (RLIP) shall be adjusted accordingly during their periods of designation.

7.13.3.2 A designated VP who is already receiving a salary higher than the 1st step of a permanent VP position shall only be entitled to the corresponding RATA for the position. In no case shall the designee’s basic salary plus the salary differential exceed the hiring rate prescribed for a permanent VP position for the particular SUC level.

7.13.3.3 The aggregate salary received during the designation cannot be used as previous salary for purposes of future appointment. It shall not be considered for purposes of the computation of terminal leave benefits (TLB).

7.13.3.4 During the period of designation, the VP shall continue to be entitled to step increment in his/her regular position but not as VP. In case his/her step increment in his/her lower position overtakes the 1st step of the VP position, he/she shall be allowed to receive the higher salary.

7.13.3.5 In the event that the designation, being of a temporary nature, is revoked by the Governing Board of the SUC concerned, he shall revert to his/her salary in his/her regular position plus any step increment he/she earned during the period of designation.

7.13.4 Night Pay of Faculty/Non-Faculty of the Polytechnic University of the Philippines (PUP)

7.13.4.1 The PUP is authorized by law to compensate its faculty and non-faculty including those from outside of the University for night services rendered on top of their regular services.

The night service is considered as a separate and distinct program from the regular 8-hour service. The night pay does not partake of the nature of overtime

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Classification and Compensation Scheme for Faculty Positions

pay which is not part of basic salary. The night pay partakes more of the nature of basic salary, as a matter of right for recompense of services rendered in the night program of the University.

7.13.4.2 The night pay, therefore, is integrated into the basic salaries of the University’s employees for purposes of

retirement benefits. This authority, however, is

applicable only to PUP.

7.13.5 Step Increment of Faculty Members

7.13.5.1 In cases of promotion or movements from one rank/sub- rank to another, the step increment earned by a faculty member in his/her previous faculty rank cannot be carried over to his/her salary in the higher level faculty rank. His/Her next step increment shall be reckoned from the date of his/her appointment to the higher level faculty rank.

7.13.5.2 In case of conversion of a teaching position to a faculty rank, the step increment earned by a teacher in his/her previous position cannot also be carried over to the converted faculty rank. His/Her step increment shall be reckoned from the date of his/her appointment to the newly converted faculty rank.

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Manual on Position Classification and Compensation

Annex A

Specific Factors and Guidelines for Determining Credits Points Under the Common Criteria for Evaluation

1. Educational Qualifications 85 pts.

1.1 Highest relevant academic degree or educational attainment with the following maximum points credits

1.1.1 Doctorate Degree 85

1.1.2 Master’s Degree 65

1.1.3 LLB and MD 65

The MD shall be considered a Doctorate degree if the holder is teaching in a College of Medicine

1.1.4 Diploma course (above a bachelor’s degree).. 55

1.1.5 Bachelor’s degree (4 years) 45

In the case of a Bachelor’s degree which is more than 4 years, additional credit of 5 points is given for every year over 4 years

1.1.6 Special Courses

* 3-year post secondary course 30

* 2-year post secondary course 25

1.2 Additional equivalent and relevant degree earned

1.2.1 Additional Master’s degree 4

1.2.2 Additional Bachelor’s degree 3

An additional equivalent and relevant degree earned related to the present position refers to another degree on the same level as the advanced degree that the faculty has already earned.

Relevance is the applicability of the degree to teaching and to the subjects the faculty is teaching, or the duties and functions other than teaching which the faculty performs.

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Classification and Compensation Scheme for Faculty Positions

For example, a holder of an M.S. in Math acquired a degree in M.S. Physics. However, an M.A. holder, who acquired 2 bachelor degrees like A.B., BSE, shall be credited only for his/her M.A. degree.

1.3 Additional credits earned

1.3.1 For every 3 units earned toward a higher approved degree

course (maximum of 10 pts.) 1

2. Experience and Professional Services 25 pts.

The services and experiences of a faculty who is designated to an administrative position like Vice-President, Dean, Director, etc., shall be credited only once, whichever is highest, within the period of his/her designation.

2.1 For every year of full-time academic 1

service in a state institution of higher

learning

Academic service refers to teaching in college or doing research and extension functions.

A year means at least 2 semesters.

Full-time service means the official full-time equivalent load (FTEL) hours of actual teaching or its equivalent in other functions approved by the institution’s Board of Regents/Board of Trustees.

State institution of higher learning refers to a chartered SUC, CHED-Supervised HEI or TESDA-Supervised TEI whose main function and responsibility is tertiary education and which offers degree programs.

2.2 For every year of full-time academic 0.75

service in an institution of higher

learning other than SUCs, CHED

Supervised HEIs and TESDA-Supervised TEIs;

service in a public or private research institution

Academic service refers to teaching in the tertiary level in an institution of higher learning which is not a SUC, CHED-Supervised HEI or TESDA- Supervised TEI, or doing research on a professional level in a research institution.

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Manual on Position Classification and Compensation

2.3 For every year of administrative designation as:

a. President 3.0

b. Vice-President 2.5

c. Dean/Director/School Superintendent 2.0

d. Principal/Supervisor/Department 1.0

Chairperson/Head of Unit

2.4 For every year of full-time industrial/agricultural/teaching experience as:

a. Engineer, Plant/Farm Manager 1.5

b. Technician 1.0

c. Skilled Worker 0.5

2.5 For every year of experience as:

a. Cooperating Teacher 1.5

b. Basic Education Teacher 1.0

3. Professional Development Achievement and Honors 90 pts.

3.1 Innovations, patented inventions, publications and other creative works (maximum of 30 pts.)

3.1.1 For every cost and time-saving 1 to 7

innovation, patented invention

and creative work as well as

discovery of an educational,

technical, scientific and/or

cultural value

Sub-categories under 3.1.1 are as follows:

A. Inventions

These are original patented (or must have patent pending) works which have direct contribution to education, science and technology. The basis for the weight is the patent score.

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Classification and Compensation Scheme for Faculty Positions

Criteria Credits

1. If patented Multiply patent score by

weight assigned according to criterion of utility

2. If patent pending Multiply patent by weight

according to utility

Per invention or discovery the following additional criteria and point allocations are prescribed:

Commercial utility on:

* an international scale 7

* a national scale 5

* institutional level 2

The accrediting bodies for these factors on the international and national scale are:

* Science and technology DOST

* Education DECS/CHED/TESDA

For the institutional level, a University Committee shall be the accrediting body. The patent paper/document must be presented to ascertain patent score. Credit points are divided equally among 2 or more individuals claiming credit for the same invention.

B. Discoveries

A discovery must be the first of its kind or not of common knowledge. It shall be the result or product of the research of an individual or a group of faculty.

Criteria Credits

1. Originality, educational 60% of 7 (0.6 x 7)

impact, documentation

2. Evidence of wide dissemi- 40% of 7 (0.4 x 7)

nation, e.g. exhibits, pub

lications

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Manual on Position Classification and Compensation

Where there are more than one proponent, the points are to be divided equally among them. If only one factor, e.g., (1), is satisfied, credit is awarded only for that factor.

C. Creative work has to satisfy one or more of the following criteria:

1. 

Originality

25% of 1 – 7 pts.

2. 

Acceptability and recognition

25% of 1 – 7 pts.

3. 

Relevance and value

25% of 1 – 7 pts.

4. 

Documentation and evidence of dissemination

25% of 1 – 7 pts.

3.1.2 For every published book, original, edited, or compiled, copyrighted/published within the last 10 years

a. As original author 3 - 7

b. As co-author 2 - 5

c. As reviewer 1 - 4

d. As translator 1 - 4

e. As editor 1 - 3

f. As compiler 1 - 2

The factors and their weights are:

Textbooks, including Science and Technology and references

Role Tertiary High School Elementary

Single author 7 pts. 5 pts. 4 pts.

Co-author 5 3 2

Reviewer 4 2 1

Translator 4 2 1

Editor 3 2 1

Compiler 2 1 1

3.1.3 For every scholarly research/monograph/educational technical articles in a technical/scientific/professional journal

a. International 5

b. National 3

c. Local 2

Classification and Compensation Scheme for Faculty Positions

3.1.4 For every instructional manual/audio-visual material

developed and approved for

use 1-3

Under this item are approved and published sets of complete modules, laboratory manuals, operation manuals, workbooks, teaching guides, including software, prototype

and computer-aided instruction materials. Syllabi, flip

charts, compiled copies of machine-copied documents,

mock-ups are not considered under this item. Those

which can be credited are approved by the department or college for instructional purposes.

Role Credit

Single author or maker Full

Co-author, co-maker Half

For credits to be granted, as sample of the material and a certification by the College/Department as to its usefulness and acceptability for instruction must be presented.

3.2 For expert services, training and active participation in professional/technical activities (Maximum of 30 pts.)

3.2.1 Training and Seminars (Maximum of 10 pts.)

3.2.1.1 For every training course with a duration of at least one year (Pro-rated for less than a year and not to exceed 10 pts.)

a. International 5

b. National 3

c. Local 2

3.2.1.2 For certified industrial, agro-industrial ….1/120h

or fishery training (maximum of 5 pts.)

3.2.1.3 For participation in conferences, seminars, workshops

a. International 3

b. National 2

c. Local 1

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       Manual on Position Classification and Compensation 3.2.2 Expert services rendered (Maximum of 20 pts.)

3.2.2.1 For serving as a short-term consultant/expert in an activity of an educational, technological, professional, scientific or cultural nature (foreign or local) sponsored by the government or other agencies

a. International 5

b. National 3

c. Local 2

3.2.2.2 For services rendered as coordinator, lecturer, resource person or guest speaker in conferences, workshops, and/or training courses

a. International 5

b. National 3

c. Local 2

3.2.2.3 For expert services as adviser in doctoral dissertations, masteral and undergraduate theses (maximum of 10 pts.)

a. Doctoral dissertation 1.00

b. Masteral thesis 0.50

c. Undergraduate thesis 0.25

3.2.2.4 For certified services as reviewer/examiner in the Professional Regulations Commission (PRC) or in

the Civil Service Commission 1

3.2.2.5 For expert services in accreditation work as member of the Board of Directors, member of the Technical Committee or Consultant

Group 1

3.2.2.6 For expert services in trade skill certification….1

3.2.2.7 For every year of service as coach/trainer in

sports or adviser of student organization 1

3.3 Membership in professional organizations/honor societies and honors received (maximum of 10 pts.)

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Classification and Compensation Scheme for Faculty Positions

3.3.1 For current individual membership in relevant professional organization(s)

a. Learned Society

Full member 2

Associate member 1

b. Honor Society 1

c. Scientific Society 1

d. Professional

Officer 1

Member 0.5

3.3.2 For undergraduate academic honors earned:

Summa Cum Laude 5

Magna Cum Laude 3

Cum Laude 1

3.3.3 Scholarship/Fellowship - This may be degree or non-degree granting.

a. International, competitive

Doctorate 5

Masteral 4

Non-Degree 3

b. International, non-competitive

Doctorate 3

Masteral 2

Non-Degree 2

c. National/Regional, competitive

Doctorate 3

Masteral 2

Non-Degree 1

d. National/Regional, non-competitive

Doctorate 2

Masteral 1

e. Local, competitive or non-competitive

3.4 Awards of distinction received in recognition of achievements in relevant areas of specialization/profession and/or assignment of the faculty concerned

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Manual on Position Classification and Compensation

a. International 5

b. National/Regional 3

c. Local 2

3.5 Community outreach (maximum of 5 points)

3.5.1 For every year of participation in service-oriented

projects in the community 1

3.6 Professional examinations

3.6.1 For every relevant licensure and other professional examinations passed (maximum of 10 pts.)

a. Engineering, Accounting, Medicine,

Law, Teacher’s Board, etc 5

b. Marine Board/Seaman Certificate;

Master Electrician Certificate, Master Plumber Certificate, Plant Mechanic Certificate;

Professional Radio Operator

Certificate 2

c. Other trade skill certificate 1

Management and Program Analysis Series, GS-0343 TS-98 August 1990

Position Classification Flysheet for

Management and Program Analysis Series,

GS-0343

Table of Contents

SERIES DEFINITION 2

EXCLUSIONS 2

OCCUPATIONAL INFORMATION 3

TITLES 5

GRADING POSITIONS 5

U.S. Office of Personnel Management 1

Management and Program Analysis Series, GS-0343 TS-98 August 1990

SERIES DEFINITION

This series includes positions that primarily serve as analysts and advisors to management on the evaluation of the effectiveness of government programs and operations or the productivity and efficiency of the management of Federal agencies or both. Positions in this series require knowledge of: the substantive nature of agency programs and activities; agency missions, policies, and objectives; management principles and processes; and the analytical and evaluative methods and techniques for assessing program development or execution and improving organizational effectiveness and efficiency. Some positions also require an understanding of basic budgetary and financial management principles and techniques as they relate to long range planning of programs and objectives. The work requires skill in: application of factfinding and investigative techniques; oral and written communications; and development of presentations and reports.

This standard supersedes the standard for the Management Analysis Series, GS-0343, issued February 1972 (TS-9) and the standard for the Program Analysis Series, GS-0345, issued October 1965 (TS-58).

EXCLUSIONS

1. Positions primarily engaged in conducting, supervising, or managing the line program activities or functions of the employing agency. Depending on the specific knowledges and skills required, such positions should be classified either in the appropriate subject-matter series, the Program Management Series, GS-0340, or the Miscellaneous Administration and Program Series, GS-0301.

2. Analytical positions which have as their paramount qualification requirement specialized subject-matter knowledge and skills equivalent to those required of a fully-trained employee in the particular subject-matter occupations. Such positions should be classified in the appropriate specialized series, or if none is established, in the Miscellaneous Administration and Program Series, GS-0301.

3. For other excluded work, see the classification standards for:

Information Technology Management Series, GS-2210;

Management Clerical and Assistance Series, GS-0344;

Budget Analysis Series, GS-0560;

Industrial Engineering Series, GS-0896;

Financial Analysis Series, GS-1160; and

Operations Research Series, GS-1515.

U.S. Office of Personnel Management 2

Management and Program Analysis Series, GS-0343 TS-98 August 1990

OCCUPATIONAL INFORMATION

This series includes positions formerly classified in the Management Analysis Series, GS-0343, and the Program Analysis Series, GS-0345. This new series combines in one occupation positions which perform similar duties and require many of the same, or closely related, knowledges and skills. The intent in establishing this series is to cover staff administrative analytical and evaluative work related to program operations, and management and organizational efficiency and productivity. Staff positions which require full competence in a particular specialized or subject-matter field for satisfactory performance of the work are excluded from this series.

The work of this occupation is typically performed in a staff capacity in that the results of the work support the accomplishment of the principal mission or line program(s) of the agency or organizational component in which the positions are located. In some cases, particularly in the larger agencies, the distinction may not always be readily apparent. For example, the mission or line work of an organizational component may be the development of staffing standards to be used throughout the agency. Positions involved in this work may be considered as performing the line work of the immediate organizational component. However, since the results of the work (i. e., the staffing standards) support accomplishment of the overall programs and mission of the agency, the positions are in fact performing staff work for the agency.

Positions in this series serve as staff analysts, evaluators, and advisors to management on the effectiveness and efficiency with which agencies and their components carry out their assigned programs and functions. Such positions may be found at any organizational level within Federal agencies. The primary purpose of the work is to provide line managers with objectively based information for making decisions on the administrative and programmatic aspects of agency operations and management. Positions in this series are concerned with a wide variety of assignments. Listed below are some illustrations of the nature of the work and the intended coverage of this series. This list should not be considered as a definitive catalog of all of the specific kinds or combinations of work performed by positions in this series.

- analyzing and evaluating (on a quantitative or qualitative basis) the effectiveness of line program operations in meeting established goals and objectives;

- developing life cycle cost analyses of projects or performing cost benefit or economic evaluations of current or projected programs;

- advising on the distribution of work among positions and organizations and the appropriate staffing levels and skills mix;

- advising on the potential benefits/uses of automation to improve the efficiency of administrative support or program operations;

- evaluating and advising on the organization, methods, and procedures for providing administrative support systems such as records, communications, directives, forms, files, and documentation;

U.S. Office of Personnel Management 3

Management and Program Analysis Series, GS-0343 TS-98 August 1990

- researching and investigating new or improved business and management practices for application to agency programs or operations;

- analyzing management information requirements to develop program or administrative reporting systems including the systems specifications, data gathering and analytical techniques, and systems evaluation methodology;

- analyzing new or proposed legislation or regulations to determine impact on program operations and management;

- developing new or modified administrative program policies, regulations, goals, or objectives;

- identifying data required for use in the management and direction of programs;

- developing data required for use in the management and direction of programs;

- developing management and/or program evaluation plans, procedures, and methodology;

- conducting studies of employee/organizational efficiency and productivity and recommending changes or improvements in organization, staffing, work methods, and procedures;

- developing procedures and systems for establishing, operating, and assessing the effectiveness of administrative control systems such as those designed to prevent waste, loss, unauthorized use, or misappropriation of assets;

- performing management surveys to determine compliance with agency regulations, procedures, sound management practices, and effective utilization of staff;

- developing workload based staffing standards to determine organizational manning levels;

- analyzing and evaluating agency functions and activities being considered for conversion to contract operations;

- identifying resources (staff, funding, equipment, of facilities) required to support varied levels of program operations;

- reviewing administrative audit and investigative reports to determine appropriate changes or corrective action required;

- analyzing and evaluating proposed changes in mission, operating procedures and delegations of authority.

U.S. Office of Personnel Management 4

Management and Program Analysis Series, GS-0343 TS-98 August 1990

TITLES

Nonsupervisory positions primarily concerned with analyzing, evaluating, and/or improving the

efficiency of internal administrative operations, organizations, or management are titled

Management Analyst.

Nonsupervisory positions primarily involved in planning, analyzing and/ or evaluating the effectiveness of line or operating programs are titled Program Analyst.

Positions which involve a mix of these functions, where neither is predominant are titled Management and Program Analyst.

Agencies may supplement the basic position titles by adding parenthetical titles, where necessary, to identify duties and responsibilities which reflect specific knowledge and skills required in the work.

Supervisory is prefixed to the title of positions which meet the criteria in the General Schedule Supervisory Guide. (In Department of Defense components, titling instructions in other guides should be used.)

Management Analysis Officer, Program Analysis Officer, and Management and Program Analysis Officer titles are established for positions which have responsibility for establishing, planning, and directing programs in their respective functional specializations.

GRADING POSITIONS

Nonsupervisory positions at grade GS-9 and above are evaluated by reference to the Administrative Analysis Grade Evaluation Guide. Due to the diversity of assignments in this occupation, users should not seek a one-to-one correspondence between the duties of a particular position and the factor level descriptions and work illustrations in the guide. Instead, users should strive to match the intent of the various factor levels and seek to locate concepts and examples which are comparable.

For trainee and developmental positions GS-5 and GS-7, follow the guidance provided in the Administrative Analysis Grade Evaluation Guide.

Evaluate supervisory positions by the criteria in the General Schedule Supervisory Guide. (In Department of Defense components, criteria in other guides should be used.)

U.S. Office of Personnel Management 5

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Module

1

Introduction

Version 2 EE IIT, Kharagpur 1

Lesson

1

Introduction to Industrial

Automation and Control

Version 2 EE IIT, Kharagpur 2

Lesson Objectives

* To define Automation and Control and explain the differences in the sense of the terms

* To explain the relation between Automation and Information Technology

* To underline the basic objectives of a manufacturing industry and explain how automation and control technologies relate to these

* To introduce the concept of a Product Life Cycle and explain how Automation and Control technologies relate to the various phases of the cycle

* To classify Manufacturing plants and categorise the different classes of Automation Systems that are appropriate for these

Understanding the Title of the Course

Let us first define the three key words in the title, namely,

Industry

In a general sense the term “Industry” is defined as follows.

Definition: Systematic Economic Activity that could be related to

Manufacture/Service/ Trade.

In this course, we shall be concerned with Manufacturing Industries only.

Automation

The word ‘Automation’ is derived from greek words “Auto”(self) and “Matos” (moving). Automation therefore is the mechanism for systems that “move by itself”. However, apart from this original sense of the word, automated systems also achieve significantly superior performance than what is possible with manual systems, in terms of power, precision and speed of operation.

Definition: Automation is a set of technologies that results in operation of machines and systems without significant human intervention and achieves performance superior to manual operation

A Definition from Encyclopaedia Britannica

The application of machines to tasks once performed by human beings or, increasingly, to tasks that would otherwise be impossible. Although the term mechanization is often used to refer to the simple replacement of human labour by machines, automation generally implies the integration of machines into a self-governing system.

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Point to Ponder: 1

A. Why does an automated system achieve superior performance compared to a manual one?

B. Can you give an example where this happens?

Control

It is perhaps correct to expect that the learner for this course has already been exposed to a course on Control Systems, which is typically introduced in the final or pre-final year of an undergraduate course in Engineering in India. The word control is therefore expected to be familiar and defined as under.

Definition: Control is a set of technologies that achieves desired patterns of variations of operational parameters and sequences for machines and systems by providing the input signals necessary.

Point to Ponder: 2

A. Can you explain the above definition in the context of a common control system, such as temperature control in an oven?

B. Is the definition applicable to open-loop as well as closed loop control?

It is important at this stage to understand some of the differences in the senses that these two terms are generally interpreted in technical contexts and specifically in this course. These are given below.

1. Automation Systems may include Control Systems but the reverse is not true. Control Systems may be parts of Automation Systems.

2. The main function of control systems is to ensure that outputs follow the set points. However, Automation Systems may have much more functionality, such as computing set points for control systems, monitoring system performance, plant startup or shutdown, job and equipment scheduling etc.

Automation Systems are essential for most modern industries. It is therefore important to understand why they are so, before we study these in detail in this course.

Point to Ponder: 3

A. Can you give an example of an automated system, which contains a control system as a part of it?

B. What are the other parts of the system?

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Industrial Automation vs. Industrial Information Technology

Industrial Automation makes extensive use of Information Technology. Fig. 1.1 below shows some of the major IT areas that are used in the context of Industrial Automation.

Fig. 1.1 Major areas of IT which are used in the context of Industrial Automation.

Point to Ponder: 4

A. Try to find an example automated system which uses at least one of the areas of Industrial IT mentioned in Fig. 1.1 (Hint: Try using the internet)

However, Industrial Automation is distinct from IT in the following senses

A. Industrial Automation also involves significant amount of hardware technologies, related to Instrumentation and Sensing, Actuation and Drives, Electronics for Signal Conditioning, Communication and Display, Embedded as well as Stand-alone Computing Systems etc.

B. As Industrial Automation systems grow more sophisticated in terms of the knowledge and algorithms they use, as they encompass larger areas of operation comprising several units or the whole of a factory, or even several of them, and as they integrate manufacturing with other areas of business, such as, sales and customer care, finance and the entire supply chain of the business, the usage of IT increases dramatically. However, the lower level Automation Systems that only deal with individual or , at best, a group of machines, make less use of IT and more of hardware, electronics and embedded computing.

Point to Ponder: 5

A. Can you give an example of an automated system, some of whose parts makes a significant application of Industrial IT?

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B. Can you give an example of an automated system, none of whose parts makes a significant application of Industrial IT?

Apart from the above, there are some other distinguishing features of IT for the factory that differentiate it with its more ubiquitous counterparts that are used in offices and other business.

A. Industrial information systems are generally reactive in the sense that they receive stimuli from their universe of discourse and in turn produce responses that stimulate its environment. Naturally, a crucial component of an industrial information system is its interface to the world.

B. Most of industrial information systems have to be real-time. By that we mean that the computation not only has to be correct, but also must be produced in time. An accurate result, which is not timely may be less preferable than a less accurate result produced in time. Therefore systems have to be designed with explicit considerations of meeting computing time deadlines.

C. Many industrial information systems are considered mission-critical, in the sense that the malfunctioning can bring about catastrophic consequences in terms of loss of human life or property. Therefore extraordinary care must be exercised during their design to make them flawless. In spite of that, elaborate mechanisms are often deployed to ensure that any unforeseen circumstances can also be handled in a predictable manner. Fault-tolerance to emergencies due to hardware and software faults must often be built in.

Point to Ponder: 6

A. Can you give an example of an automated system, which is reactive in the sense mentioned above?

B. Can you give an example of an automated system, which is real-time in the sense mentioned above

C. Can you give an example of an automated system, which is mission-critical in the sense mentioned above

Role of automation in industry

* Manufacturing processes, basically, produce finished product from raw/unfinished material using energy, manpower and equipment and infrastructure.

* Since an industry is essentially a “systematic economic activity”, the fundamental objective of any industry is to make profit.

* Roughly speaking,

     Profit = (Price/unit – Cost/unit) x Production Volume (1)

So profit can be maximised by producing good quality products, which may sell at higher price, in larger volumes with less production cost and time. Fig 1.2 shows the major parameters that affect the cost/unt of a mass-manufactured industrial product.

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Cost/unit

Material Energy Manpower Infrastructure

                   Fig. 1.2 The Components of per unit Manufacturing Cost Automation can achieve all these in the following ways,

? Figure 1.4 shows how overall production time for a product is affected by various factors. Automation affects all of these factors. Firstly, automated machines have significantly lower production times. For example, in machine tools, manufacturing a variety of parts, significant setup times are needed for setting the operational configuration and parameters whenever a new part is loaded into the machine. This can lead to significant unproductive for expensive machines when a variety of products is manufactured. In Computer Numerically Controlled (CNC) Machining Centers set up time is reduced significantly with the help of Automated Tool Changers, Automatic Control of Machines from a Part Program loaded in the machine computer. Such a machine is shown in Figure 1.3. The consequent increase in actual metal cutting time results in reduced capital cost and an increased volume of production.

Point to Ponder: 7

A. With reference to Eq. (1), explain how the following automation systems improve industrial profitability.

a. Automated Welding Robots for Cars

b. Automated PCB Assembly Machines

c. Distributed Control Systems for Petroleum Refineries

Fig. 1.3 A CNC Machine with an Automated Tool Changer and the Operator Console with Display for Programming and Control of the Machine

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Production Volume

Production Material Idle Quality Assurance Time

Time Handling Time

Time

Fig. 1.4 The major factors that contribute to Overall Production Time

* Similarly, systems such as Automated Guided Vehicles, Industrial Robots, Automated Crane and Conveyor Systems reduce material handling time.

* Automation also reduces cost of production significantly by efficient usage of energy, manpower and material.

* The product quality that can be achieved with automated precision machines and processes cannot be achieved with manual operations. Moreover, since operation is automated, the same quality would be achieved for thousands of parts with little variation.

* Industrial Products go through their life cycles, which consists of various stages.

> At first, a product is conceived based on Market feedbacks, as well as Research and Development Activities.

> Once conceived the product is designed. Prototype Manufacturing is generally needed to prove the design.

> Once the design is proved, Production Planning and Installation must be carried out to ensure that the necessary resources and strategies for mass manufacturing are in place.

> This is followed by the actual manufacture and quality control activities through which the product is mass-produced.

> This is followed by a number of commercial activities through which the product is actually sold in the market.

> Automation also reduces the over all product life cycle i.e., the time required to complete (i) Product conception and design (ii) Process planning and installation (iii) Various stages of the product life cycle are shown as in Figure 1.5.

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Fig. 1.5 A Typical Industrial Product Life Cycle

Economy of Scale and Economy of Scope

In the context of Industrial Manufacturing Automation, Economy of Scale is defined as follows.

Economy of Scale

Definition: Reduction in cost per unit resulting from increased production, realized through operational efficiencies. Economies of scale can be accomplished because as production increases, the cost of producing each additional unit falls.

Obviously, Automation facilitates economy of scale, since, as explained above, it enables efficient large-scale production. In the modern industrial scenario however, another kind of economy, called the economy of scope assumes significance.

Economy of Scope

Definition : The situation that arises when the cost of being able manufacture multiple products simultaneously proves more efficient than that of being able manufacture single product at a time.

Economy of scope arises in several sectors of manufacturing, but perhaps the most predominantly in electronic product manufacturing where complete product life cycle, from conception to market, are executed in a matter of months, if not weeks. Therefore, to shrink the time to market drastically use of automated tools is mandated in all phases of the product life cycle. Additionally, since a wide variety of products need to be manufactured within the life period of a factory, rapid programmability and reconfigurability of machines and processes becomes a key requirement for commercial success. Such an automated production system also enables the industry to exploit a much larger market and also protects itself against fluctuations in demand for a given class of products. Indeed it is being driven by the economy of scope, and

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enabled by Industrial Automation Technology that Flexible Manufacturing (i.e. producing various products with the same machine) has been conceived to increase the scope of manufacturing.

Next let us see the various major kinds of production systems, or factories, exist. This would be followed by a discussion on the various types of automation systems that are appropriate for each of these categories.

Point to Ponder: 8

A. Can you give an example of an industry where economy of scope is more significant than the economy of scale?

B. Can you give an example of an industry where economy of scale is more significant than the economy of scope?

C. Can you give an example of an industry where both economy of scope, and economy of scale are significant?

Types of production systems

Major industrial processes can be categorized as follows based on their scale and scope of production.

> Continuous flow process: Manufactured product is in continuous quantities i.e., the product is not a discrete object. Moreover, for such processes, the volume of production is generally very high, while the product variation is relatively low. Typical examples of such processes include Oil Refineries, Iron and Steel Plants, Cement and Chemical Plants.

> Mass Manufacturing of Discrete Products: Products are discrete objects and manufactured in large volumes. Product variation is very limited. Typical examples are Appliances, Automobiles etc.

> Batch Production: In a batch production process the product is either discrete or continuous. However, the variation in product types is larger than in continuous-flow processes. The same set of equipment is used to manufacture all the product types. However for each batch of a given product type a distinct set of operating parameters must be established. This set is often referred to as the “recipe” for the batch. Typical examples here would be Pharmaceuticals, Casting Foundries, Plastic moulding, Printing etc.

> Job shop Production: Typically designed for manufacturing small quantities of discrete products, which are custom built, generally according to drawings supplied by customers. Any variation in the product can be made. Examples include Machine Shops, Prototyping facilities etc.

The above types of production systems are shown in Figure 1.6 categorized according to volumes of production and variability in product types. In general, if the quantity of product is more there is little variation in the product and more varieties of product is manufactured if the quantity of product is lesser.

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Variety

Fig. 1.6 Types of Production Systems

Types of Automation Systems

Automation systems can be categorized based on the flexibility and level of integration in manufacturing process operations. Various automation systems can be classified as follows

> Fixed Automation: It is used in high volume production with dedicated equipment, which has a fixed set of operation and designed to be efficient for this set. Continuous flow and Discrete Mass Production systems use this automation. e.g. Distillation Process, Conveyors, Paint Shops, Transfer lines etc.

A process using mechanized machinery to perform fixed and repetitive operations in order to produce a high volume of similar parts.

> Programmable Automation: It is used for a changeable sequence of operation and configuration of the machines using electronic controls. However, non-trivial programming effort may be needed to reprogram the machine or sequence of operations. Investment on programmable equipment is less, as production process is not changed frequently. It is typically used in Batch process where job variety is low and product volume is medium to high, and sometimes in mass production also. e.g. in Steel Rolling Mills, Paper Mills etc.

> Flexible Automation: It is used in Flexible Manufacturing Systems (FMS) which is invariably computer controlled. Human operators give high-level commands in the form of codes entered into computer identifying product and its location in the sequence and the lower level changes are done automatically. Each production machine receives settings/instructions from computer. These automatically loads/unloads required tools and carries out their processing instructions. After processing, products are automatically transferred to next machine. It is typically used in job shops and batch processes where

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product varieties are high and job volumes are medium to low. Such systems typically use Multi purpose CNC machines, Automated Guided Vehicles (AGV) etc.

??? Integrated Automation: It denotes complete automation of a manufacturing plant, with all processes functioning under computer control and under coordination through digital information processing. It includes technologies such as computer-aided design and manufacturing, computer-aided process planning, computer numerical control machine tools, flexible machining systems, automated storage and retrieval systems, automated material handling systems such as robots and automated cranes and conveyors, computerized scheduling and production control. It may also integrate a business system through a common database. In other words, it symbolizes full integration of process and management operations using information and communication technologies. Typical examples of such technologies are seen in Advanced Process Automation Systems and Computer Integrated Manufacturing (CIM)

As can be seen from above, from Fixed Automation to CIM the scope and complexity of automation systems are increasing. Degree of automation necessary for an individual manufacturing facility depends on manufacturing and assembly specifications, labor conditions and competitive pressure, labor cost and work requirements. One must remember that the investment on automation must be justified by the consequent increase in profitability. To exemplify, the appropriate contexts for Fixed and Flexible Automation are compared and contrasted.

Fixed automation is appropriate in the following circumstances.

A. Low variability in product type as also in size, shape, part count and material

B. Predictable and stable demand for 2- to 5-year time period, so that manufacturing capacity requirement is also stable

C. High production volume desired per unit time

D. Significant cost pressures due to competitive market conditions. So automation systems should be tuned to perform optimally for the particular product.

Flexible automation, on the other hand is used in the following situations.

A. Significant variability in product type. Product mix requires a combination of different parts and products to be manufactured from the same production system

B. Product life cycles are short. Frequent upgradation and design modifications alter production requirements

C. Production volumes are moderate, and demand is not as predictable

Point to Ponder: 9

A. During a technical visit to an industry how can you identify the type of automation prevailing there from among the above types?

B. For what kind of a factory would you recommend computer integrated manufacturing and why?

C. What kind of automation would you recommend for manufacturing

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a. Light bulbs

b. Garments

c. Textile

d. Cement

e. Printing

f. Pharmaceuticals

g. Toys

Lesson Summary

In this lesson we have dealt with the following topics:

A. Definition of Automation and its relations with fields of Automatic Control and Information Technology: It is seen that both control and IT are used in automation systems to realize one or more of its functionalities. Also, while Control Technology is used for operation of the individual machines and equipment, IT is used for coordination, management and optimized operation of overall plants.

B. The role played by Automation in realizing the basic goal of profitability of a manufacturing industry: It is seen that Automation can increase profitability in multiple ways by reducing labour, material and energy requirements, by improving quality as well as productivity. It is also seen that Automation is not only essential to achieve Economy of Scale, but also for Economy of Scope.

C. Types of Factories and Automation Systems that are appropriate for them: Factories have been classified into four major categories based on the product volumes and product variety. Similarly Automation Systems are also categorized into four types and their appropriateness for the various categories of factories explained.

Exercises

A. Describe the role of Industrial Automation in ensuring overall profitability of a industrial production system. Be specific and answer point wise. Give examples as appropriate.

B. State the main objectives of a modern industry (at least five) and explain the role of automation in helping achieve these.

C. Explain with examples the terms “economy of scale” and “economy of scope”. How does industrial automation help in achieving these? Cite examples.

D. Differentiate between a job shop and a flow shop with example what are their ‘process plant’ analogues? Give examples.

E. Run any internet search engine and type “History of Automation” to prepare a term paper on the subject.

F. There are some aspects of automation that have not been treated in the lesson. Consult references and prepare term papers on the impact of automation on

a. Environmental Appropriateness for Industries

b. Industrial Standardisation Certification such as ISO 9001

c. Industrial Safety

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G. Locate the major texts on Manufacturing Automation

H. From the internet find alternate definitions of the terms : Industry, Automation and Control

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Answers, Remarks and Hints to Points to Ponder

Point to Ponder: 1

A. Why does an automated system achieve superior performance compared to a manual one?

Ans: Because such systems can have more precision, more energy and more speed of operation than possible manually. Moreover using computing techniques, much more sophisticated and efficient operational solutions can be derived and applied in real-time.

B. Can you give an example where this happens?

Ans: This is the rule. Only few exceptions exist. How many of the millions of industrial products could be made manually?

Point to Ponder: 2

A. Can you explain the above definition in the context of a common control system, such as temperature control in an oven?

Ans: Consider a temperature-controlled oven as found in many kitchens. A careful examination of the dials would show that one could control the temperature in the oven. This is a closed loop control operation. One can also control the time for which the oven is kept on. Note that in both cases the input signal to the process is the applied voltage to the heater coils. This input signal is varied as required to hold the temperature, by the controller.

B. Is the definition applicable to open-loop as well as closed loop control? Ans: Yes

Point to Ponder: 3

C. Can you give an example of an automated system, which contains a control system as a part of it?

Ans: Many examples can be given. One of these is the following:

In an industrial CNC machine, the motion control of the spindle, the tool holder and the job table are controlled by a position and speed control system, which, in fact, uses a separate processor. Another processor is used to manage the other automation aspects.

Another example is that of A pick and place automated robot is used in many industrial assembly shops. The robot motion can be programmed using a high level interface. The motion of the robot is controlled using position control systems driving the various joints in the robotic manipulator.

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D. What are the other parts of the system?

Ans: The other functional parts of the CNC System include:

The operator interface, the discrete PLC controls of indicators, lubricant flow control, tool

changing mechanisms.

Point to Ponder: 4

Try to find an example automated system which uses at least one of the areas of Industrial IT mentioned in Fig. 1.2. (Hint: Try using the internet)

Ans: Distributed Control Systems (DCS) used in many large Continuous-Flow processes such as Petroleum Refining and Integrated Steel Plants use almost all components of Industrial IT

Point to Ponder: 5

A. Can you give an example of an automated system, some of whose parts makes a significant application of Industrial IT?

Ans: Distributed Control Systems (DCS) used in many large Continuous-Flow processes such as Petroleum Refining and Integrated Steel Plants use almost all components of Industrial IT

B. Can you give an example of an automated system, none of whose parts makes a significant application of Industrial IT?

Ans: An automated conveyor system used in many large Discrete Manufacturing Plants such as bottled Beverage Plants use no components of Industrial IT.

Point to Ponder: 6

A. Can you give an example of an automated system, which is reactive in the sense mentioned above?

Ans: Any feedback controller, such as an industrial PID controller is reactive since it interacts with sensors and actuators.

B. Can you give an example of an automated system, which is real-time in the sense mentioned above

Ans: Any feedback controller, such as an industrial PID controller is real-time, since it has to compute its output within one sampling time.

C. Can you give an example of an automated system, which is mission-critical in the sense mentioned above

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Ans: An automation system for a Nuclear Power Plant is mission critical since a failure is unacceptable for such a system.

Point to Ponder: 7

A. With reference to Eq. (1), explain how the following automation systems improve industrial profitability.

d. Automated Welding Robots for Cars

e. Automated PCB Assembly Machines

f. Distributed Control Systems for Petroleum Refineries

Ans: Some of the factors that lead to profitability in each case, are mentioned.

a. Automated Welding Robots for Cars

Increased production rate, Uniform and accurate welding, Operator safety.

b. Automated PCB Assembly Machines

Increased production rate, Uniform and accurate placement and soldering

c. Distributed Control Systems for Petroleum Refineries

Energy efficiency, Improved product quality

Point to Ponder: 8

A. You give an example of an industry where economy of scope is more significant than the economy of scale?

Ans: One such example would a job shop which manufactures custom machine parts by machining according to customer drawings. Another example would be a factory to manufacture Personal Computer components

B. Can you give an example of an industry where economy of scale is more significant than the economy of scope?

Ans: One such example would be a Power plant. Another one would be a Steel Plant.

Point to Ponder: 9

A. During a technical visit to an industry how can you identify the type of automation prevailing there from among the above types?

Ans: Check for the following.

* Whether automatic control exists for majority the equipment

* Whether supervisory control is manual, partially automated or largely automated

* Whether operator interfaces are computer integrated or not.

* Whether communication with individual control units can be done from supervisory interfaces through computers or not

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* Whether any information network exists, to which automation system and controllers are connected

* Product variety, product volumes, batch sizes etc.

* Whether the material handling systems are automated and if so to what extent.

The type of automation system can be determined based on these information, as discussed in the lesson.

B. For what kind of a factory would you recommend computer integrated manufacturing and why?

Ans: For large systems producing sophisticated and expensive products in large volumes having many subunits to be integrated in complex ways.

C. What kind of automation would you recommend for manufacturing

a. Light bulbs

Ans: Fixed

b. Garments

Ans: Flexible

c. Textile

Ans: Programmable

d. Cement

Ans: Programmable

e. Printing

Ans: Flexible

f. Pharmaceuticals

Ans: Flexible

g. Toys

Ans: Flexible

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Instructions for the Prisma application form:

International Grants 2017, Visiting Researcher In

General information

Please observe that all character limitations are including spaces. NB! If you intend to copy and paste the text into the application form, we recommend that you, in good time, try to copy it into Prisma to assure that the number of characters specified in your word processer is consistent with the count in Prisma. Underlying formatting may follow and contribute to a different number count in Prisma. If that is the case, unformat your text by, for example, copying it (paragraph by paragraph) into a different software such as Notepad or alike.

Prisma

The application is to be sent in via the application- and review system Prisma. For most of the required steps in Prisma, user manuals are available. We urge you to consult these when writing your application.

* Go to Prisma

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* FAQ Prisma 

Before beginning an application, you must open a personal account in Prisma where your information, such as personal data, CV and publications, is saved. With a personal account, you only have to enter the information once, and it may then be supplementer or edited as needed. The applications you register in Prisma are then linked to your account. Remember to open your personal account in good time before the deadline.

If your employer is affiliated to SWAMID you may use the same log in information in Prisma as used at your work place.

You can read more about SWAMID in the Prisma FAQ.

Basic information

Please observe that spaces are included in all character limitations.

Project title and abstract

Project title in Swedish (max 200 characters)

The project title should indicate the purpose of the visit. The title should be formulated in clear language so that laymen can understand. The project title is often used on its own and should therefore be formulated with care.

Project title in English (max 200 characters)

Translate the title to English.

Abstract in Swedish (max 2 000 characters)

Briefly account for the visiting researcher’s stay. Highlight the specialist knowledge and experience that the visiting researcher’s stay can contribute to your research and research field. The purpose of the abstract is to inform Forte’s evaluators and decision makers about the visiting researcher’s stay. The abstract should be able to stand alone which is why you should make no references to other parts of the application. It is also important that the abstract is written in clear language so that laymen can understand.

Abstract in English (max 2 000 characters)

Translate the abstract to English.

Project time

Start month

Enter “12” here (December 2017).

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Fill in the period that the grant being applied for will be utilized. Please observe that the grant

cannot be used after 31 December 2018.

Classifications

Forte’s main subject areas and sub-areas

Choose a suitable main subject area and a sub-area for your application. Click the “+Save choice“

button. Forte reserves the right to reclassify your application.

Alternative sub-area

You have the option to choose an alternative sub-area for your application. This sub-area can have a different main subject area. Choose a main subject area again, and an alternative sub-area below. Click the “+Save choice“ button. Forte reserves the right to reclassify your application.

2

Coordination area

Forte has a national coordination responsibility for research in several research areas. Choose one of the areas if they apply to your application. If none are applicable for your project, choose the option “no coordination area”.

SCB-codes

In order to facilitate future analysis of research funded by all Swedish research funding agencies, all applications are classified according to Statistics Sweden’s standard. Make a three-level classification of your application. Click the “+Save choice“ button. Forte reserves the right to reclassify your application.

The visiting researcher’s stay

Description of the visiting researcher’s stay (max 15 000 characters)

Outline the reasons for inviting the guest researcher. Include the guest researcher's name, title and current workplace and briefly describe the specialist expertise the guest researcher contributes to the host institution. Describe her or his scientific qualifications and how this knowledge and experience will be utilized in the department's research and in the research field. Present a preliminary program for the visiting researcher’s stay - what the guest researcher will do in terms of research, lectures, presenting at seminars, etc., both at the host institution and any other institutions visited.

The visiting researcher’s CV (max 2 pages)

Upload the visiting researcher’s CV in PDF-format.

The visiting researcher’s publications (max 2 pages)

Upload a list of the visiting researcher’s publications in PDF-format. Limit the list to publications

which are relevant to the researcher’s stay and to those published during the last five years.

Budget

Costs applied for in the Forte grant

Please observe that for budget reasons, all costs must be filled in under the 2017 column,

irrespective of the desired project time.

Total budget

Please observe that the column “Other costs” should not to be filled in.

Administrating organisation

In order for you to apply, your Swedish university or higher education institution (HEI) must have been approved as an administrative organisation and must have opened an organisational account in Prisma. These are listed as choices in Prisma. You can then select your HEI as an administrative organisation and project site. If your Swedish university or HEI is missing as a

3

choice in Prisma, please contant the university or HEI. After the call has closed, your application will automatically be sent to your administrative organisation. The administrative organisation must distribute the application to an authorized representative who must sign the applicaton within 7 days.

Review panels

Do not fill in.

Participants

Do not fill in.

Register

Here you register your application.

5/1/2018 GitHub - stinnux/kanboard-Timetrackingeditor: Allows manual adding and editon of Timetracking Entries

stinnux / kanboard-Timetrackingeditor

Allows manual adding and editon of Timetracking Entries

Branch: master

New pull request

Find file

Clone or download

stinnux New Version with correct dependency

Latest commit 8908f2d 7 days ago

Console

Export Subtask Time Tracking Data via HTML

2 years ago

Controller

Compatible with Kanboard 1.0.38

a year ago

Export

Better html export

a year ago

Filter

first commit

2 years ago

Formatter

first commit

2 years ago

Model

changed upper/lower case to TimeTrackingEditor

2 years ago

Schema

added time_billable to subtasks

2 years ago

Template

Works with 1.0.41

a year ago

Test

started working on tests

2 years ago

Validator

correctly user common validators

a year ago

assets/css

correct link to Timetrackingeditor

2 years ago

Html.php

Better html export

a year ago

Plugin.php

New Version with correct dependency

7 days ago

README.md

Requires Kanboard 1.0.38

a year ago

README.md

Timetrackingeditor

Allows manual adding and editon of Timetracking Entries

Author

Thomas Stinner

Requirements

Kanboard >= 1.0.38 (not testet with older versions)

Installation

You have the choice between 3 methods:

1. Install the plugin from the Kanboard plugin manager in one click

2. Download the zip file and decompress everything under the directory plugins/Timetrackingeditor

https://github.com/stinnux/kanboard-Timetrackingeditor 1/2

5/1/2018 GitHub - stinnux/kanboard-Timetrackingeditor: Allows manual adding and editon of Timetracking Entries

 3. Clone this repository into the folder plugins/Timetrackingeditor

Note: Plugin folder is case-sensitive.

Documentation

With this plugin you are able to edit, remove and manually create entries in the Time Tracking Table.

Just go to a subtask, select "Time Tracking" (only visible if you have entered either an estimate and/or time spent value for the Task). Now you have the opportunity to add/remove/delete entries. You are only allowed to remove and edit your own entries.

You can also add comments to every Time Tracking entry and select if the time is billable or not. Entries that have been selected as billable have a shopping cart symbol.

Additionally you can export all time tracking entries as an HTML table (which makes it easy to import to excel) using the command kanboard export:allsubtaskstimetracking

https://github.com/stinnux/kanboard-Timetrackingeditor 2/2

BSEE eWell and TIMS Web Application Manual

Index

I. Overview of the BSEE eWELL and TIMS Web Systems

II. System Account Type

Administrator Account

General User Account

III. System Account Application Process  Administrator account application process General User account application process

IV. How To 

Obtain Access for a New Company for an Existing User Account

Change an eWell or TIMS Web Password

Terminate an eWell or TIMS Web User account

Unlock a User account

Report a Problem to the BSEE Enterprise IT Service Desk

V. User IDs and Passwords

APPENDICES

Appendix A: eWell and TIMS Web User Access Request Form and Instructions

Appendix B: eWell and TIMS Web Administrator Access Request Form and Instructions

Appendix C: eWell and TIMS Web Disclaimer

Updated on 02/26/2017

I. Overview of the BSEE eWELL and TIMS Web Systems

The Bureau of Safety and Environmental Enforcement (BSEE) has created the eWell Permitting and Reporting System (eWell) in 2004 to provide oil companies an Internet-based tool to obtain permits to conduct well operations. Then in 2015, BSEE implemented a new system called the Technical Information Management System (TIMS Web) to provide additional planning, permitting, and reporting functions. Currently these systems are separate systems with separate functions and require separate usernames and passwords. However, the forms and the process for obtaining accounts are the same. The BSEE is working to consolidate these into a single system within the near future and will normally grant accounts in both systems when you request an account.

The purpose of this manual is to provide information about the process for obtaining accounts and gaining access to these systems. The required forms and detailed instructions are provided as appendices.

Several other topics about user accounts and general operations of the eWell and TIMS Web Systems are discussed in this document.

General Process for Obtaining eWell and TIMS Web Accounts 

The following are the steps in the process to obtain access to the eWell and TIMS Web systems.

1. Determine the type of account you require, either “general user” or “administrator”

2. Complete the correct form and submit to BOEM

3. After your account has been created, Your company administrator will then grant the

security entitlements required to view your company’s data and use the system(s).

II. System Account Types

There are 2 basic types of accounts: (1) Administrator account; and (2) General user account. Each company must have at least one “Administrator.” The Administrator account is given the rights to assign user security roles to themselves and all other employees/users under their company(s). A company can have multiple administrators.

Administrators and users may work directly for the company or may work for a different entity under a contract with the company to serve as an agent.

Administrators –

A company administrator is a company employee or an agent assigned to administer on behalf of the company foreWell and TIMS Web “entitlement rights” (i.e. access or user rights and security roles) for a company.

The administrator duties include reviewing user request forms for completeness and submitting them to BOEM Gulf of Mexico (GOM) OCS Region; maintaining entitlement groups of leases, wells, and users; and entitling groups of users to perform various functions on groups of leases or wells. To become an administrator, a person needs to complete the “eWell and TIMS Web Administrator Access Request Form” under Appendix B.

Updated on 02/26/2017

General Users -

A general user is a person assigned by a company or an agent assigned by company to view company data and conduct business with BSEE/BOEM on behalf of the company in the eWell and TIMS Web systems but who does not have administrator rights. To become a company user, this person needs to complete the “eWell and TIMS Web User Access Request Form” in Appendix A.

  III. System Account Application Process

Administrator

1. A person who requires administrator account must complete the eWell and TIMS Web Administrator Access Request Form in Appendix B, read the disclaimer, sign the form, and then send the signed form to the company’s officer or representative who has signature authority with BSEE and BOEM.

   2. The company official or representative must initial and mark the appropriate items on the form; list on the form the companies whose records the administrator needs access to; then sign the form; and mail the completed/signed form to the following office:

BOEM GOM OCS Region

Adjudication Section

Attention: Section Chief (eWell/TIMS Web User Requests Forms)

1201 Elmwood Park Blvd.

GM 276A

New Orleans, LA 70123-2394

2. The BOEM GOM Adjudication office will verify that the form is marked and signed correctly and forward it to the BSEE Technology Services Division (TSD).. Once the accounts are created, the TSD will notify the new company administrator by certified mail that the user account for each system has been created and temporary passwords assigned.

3. The administrator logs onto each system using their appropriate username and temporary password and then must change the password.

   5. The administrator must then add himself/herself to the correct entitlement user group in order to use the modules in each system.

General User

1. The person who only requires a general user account must complete the “eWell and TIMS Web User Access Request Form” in Appendix A, read the disclaimer, sign the form, and then send the signed form to the company’s officer or representative who has signature authority with BSEE and BOEM.

Updated on 02/26/2017

   2. The company official or representative must initial and mark the appropriate items on the form; list on the form the companies whose records the administrator needs access to; then sign the form; and mail the completed/signed form to the following office:

BOEM GOM OCS Region

Adjudication Section

Attention: Section Chief (eWell/TIMS Web User Requests Forms))

1201 Elmwood Park Blvd.

GM 276A

New Orleans, LA 70123-2394

2. The BOEM GOM Adjudication office will verify that the form is marked and signed correctly and forward it to the BSEE Technology Services Division (TSD).. Once the accounts are created, the BSEE TSD will notify the new company administrator by certified mail that the user account for each system has been created and temporary passwords assigned.

3. The company user signs on and changes the password.

   5. The new general user should then contact their company’s administrator. The company administrator must then add the new general user to the correct entitlement user group in order to use the modules in each system.

IV. How To

Obtain Access to a New Company for an Existing General User or Administrator User account

General users who require access to additional companies must fill out the “eWell and TIMS Web User Access Request Form” in Appendix A and check the “Add Company” box. Administrators who will administer additional companies must fill out the eWell and TIMS Web Administrator Access Request Form” in Appendix B and check the “Add Company” box. The user ID for the person wanting access to the company data must be included. From this point, the process flow is the same for company users and agent users to complete the account application process.

Change an eWell Password

For a user to change a password, the person must call the BSEE Enterprise IT Service desk by telephone at 1-877-256-6260 or send an e-mail at EnterpriseITServiceDesk@bsee.gov to ask for a new password. A service desk ticket will be generated and sent to the appropriate group for processing. Someone from the group will contact you by phone or email to have your password reset. The new password will be e-mailed to the user. Once the user signs on with the new

Updated on 02/26/2017

password, the system requires the user to change the password. Alternatively, the user can reset the password themselves by answering security questions from the application, if they are setup.

Terminate a User

Before a user can be terminated, the company administrator needs to eliminate the user from all user groups in the eWell and TIMS Web Entitlements.. The company administrator then completes a eWell and BSEE TIMS Web User and/or Administrator Request form and checks the Delete User box. Sign the form and mail it to the following office:

BOEM GOM OCS Region

Adjudication Section

Attention: Section Chief (eWell/TIMS Web User Request Forms)

1201 Elmwood Park Blvd.

GM 276A

New Orleans, LA 70123-2394

Once the form is received, the BSEE TSD will terminate the user account and notify the company administrator or representative by certified mail that the user account has been deleted.

Unlock a User account

If a user account is locked, a user notifies the service desk by telephone at 1-877-256-6260 or by e-mail at EnterpriseITServiceDesk*bsee.gov, and the BSEE administrator unlocks the account for the user after he/she answers a question with the correct answer.

Report a Problem to the BSEE Service desk

Service desk hours are 6:30 a.m. to 5:00 p.m., central time, Monday through Friday. A user can call the BSEE Enterprise IT service desk at 1-877-256-6260 during these hours, and a service desk ticket will be generated and routed to the appropriate person. During after hours, , the user can either send an e-mail to the service desk at EnterpriseITServiceDesk*bsee.gov or leave a message on the service desk telephone line, and a service desk ticket will be generated the next day.

When contacting the help desk, please identify yourself as an External eWell and/or TIMS Web user and provide your first and last name, user ID, phone number, email address, and the system you are having issues with. Explain the specific issue with as much detail as possible as best you can. The BSEE TSD will assign a member from the eWell or TIMS Web team to the BSEE Enterprise IT Service desk to resolve any software problems. Service desk problems will be resolved during the regular working hours of the person assigned for the month. The telephone number and e-mail address for the service desk will be included on the eWell Welcome page as well as in the eWell system help.

Updated on 02/26/2017

V. USER IDs AND PASSWORDs

Make sure that your User IDs and password follow the BSEE user ID and password standards listed below. Every three months, a user must change his or her eWell or TIMS Web system password. If a user account has not been used within the last six months, the user account will be locked.

Make sure that your passwords adhere to the following standards:

1. They must be 8 characters long.

2. They must contain at least one character from three of the four following groups:

a. English upper case letters (A, B, C, ...)

b. English lower case letters (a, b, c, ...)

c. Westernized Arabic numerals (0 through 9)

d. Special characters (i.e., ! @ # $ % & * ? { } | : ” < > ? [ ] \ ; ’ , . / ...)

3. They must not contain a user name or any portion of a user name.

4. They must not contain “dictionary” words (words that can be found in an English, French, German, or Spanish language dictionary).

Updated on 02/26/2017

APPENDIX A

eWELL and TIMS Web USER ACCESS REQUEST FORM AND

INSTRUCTIONS

NOTE: This form has two sides. Make sure that you print it on the front and back of one sheet of paper. 

ITEM

DESCRIPTION

Front Side Options

New User

Check this box when the user does not have a user account in the eWell and TIMS Web systems.

Add Company

Check this box when the user has a user account and wants access to a new company.

Delete User

Check this box to delete the user from the system. BSEE will accept notice of deleted users only from the company administrator. The Administrator manages user entitlements in eWell and TIMS Web and must terminate the user’s entitlements to withdraw the user’s authority to access the system before sending in the form.

Existing eWell User Requesting TIMS Web account

Check this box to if the user has an eWell account and is now requires a TIMS Web account to conduct additional functions.

External Reviewer Role

Select this option when user performs reviews on permits and plans. This applies to Federal, State agencies, universities, etc.

Front Side User Information

eWell User ID

If the user has an eWell account and BSEE has assigned a User ID, record the current eWell User ID. Leave this blank is the user is a new user and BSEE has not yet assigned a user ID.

TIMS Web User ID

If the user has an eWell account and BSEE has assigned a User ID, record the current eWell User ID. Leave this blank is the user is a new user and BSEE has not yet assigned a TIMS Web user ID.

Name

Enter the name of the user who wants access to the eWell and TIMS Web systems. The first name, middle initial, and last name are required. Legal names only, no nicknames.

Title

Enter the company title of the person requesting access to the eWell and TIMS Web systems.

Address

Enter the company mailing address for person requesting access to the eWell and TIMS Web systems. This address will be used to correspond with the user.

Phone Number and Fax Number

Enter the numbers for the user who wants access to the eWell and TIMS Web systems.

E-mail Address

Enter the e-mail address of the person requesting access to the eWell and TIMS Web systems. This address will be used to correspond with the user and is a required field.

Identity Verification Questions

Answer one of the questions. When the user calls the BSEE Enterprise IT service desk, this information will be used to verify the caller’s identity before any problem can be addressed.

Consent

Check this box. This verifies that the user has read the certifications and agrees with them.

Signature and Date

The user needs to sign and date the form.

Back Side

BOEM/BSEE Company Number and Name

Enter the companies to whose records the user needs access and for which the signer has signature authority. BSEE and BOEM will verify the signature for each company before granting the user access to the company’s records on the system. If a company is listed for which the signer does not have signature authority, the whole form will be returned and no action will be taken.

Representative

Name

Enter the name of the person with BOEM signature authority. This person’s name must match the name on the BOEM Qualification File for the company.

Representative Title

Enter the title of the person listed for Representative Name. The title must match the title on the BOEM Qualification File.

Representative Signature

The signature authority block must be filled out to receive a new user account or access to a new company. The person with BOEM signature authority must sign and date.

Updated on 02/26/2017

U.S. Department of the Interior

Bureau of Safety and Environmental Enforcement (BSEE)

eWell and TIMS Web User Access Request Form

(This form must be printed on one page only, front and back)

I am requesting the following general user additions or changes:

_______ New User

_______ Add Company

_______ Delete User

_______ Existing eWell User Requesting TIMS Web account

I am requesting the following External Reviewer Role:

______ OSRP External Reviewer

User information: (leave User ID blank for new users)

eWell User ID: TIMS Web User ID:

Name: ______ __ ______

       Prefix First Name MI Last Name Suffix

Title:

Address: Employer Name:

Division:

Street:

City: State:

ZIP:  Country:

Phone Number: Fax Number:

Email:

CERTIFICATION

1. I understand that using the BSEE/BOEM TIMS Web system and eWell system means I will be using BSEE/BOEM Computer Systems, Electronic Mail, Internet connections and associated equipment, software and data. These resources are to be used for official government business only and in compliance with Department of the Interior and bureau policies. Law prohibits any other use of these items (18 USC Sec. 641). Violations of the law can result in loss of system access and criminal penalties.

2. If I am aware of a security breach (password sharing, hacking), I will immediately notify the BSEE Enterprise IT Service Desk.

3. I will select my own password and I will NOT share my password or username with anyone. If I no longer need access to the TIMS Web system and eWell system for any reason, I will ask my company administrator to terminate my entitlements and submit the required form to BOEM to delete my username from the system.

4. I will handle sensitive data appropriately and understand that this information is not to be exchanged, divulged or otherwise compromised in any way unless necessary for official government business.

5. I have read the eWell and BSEE TIMS Web disclaimer and agree to the conditions specified in the document. __ I consent and will adhere to the above conditions.

User Signature:  Date:

Updated on 02/26/2017

Initial the appropriate authorization. Only one block must be initialed.

USER AUTHORIZATION

Please initial if user is employed by a BOEM-Qualified company.

____ I authorize this user to have access to the company’s data based on entitlements granted by the company’s TIMS Web administrator.

AGENT USER AUTHORIZATION

Please initial if user is employed by a third party company acting as an agent.

____ I authorize this user as an agent user who has access to the company’s data based on entitlements granted by the company’s TIMS Web administrator or by the agent company’s TIMS Web administrator.

NON-BOEM-QUALIFIED COMPANY AUTHORIZATION

Please initial if this account is being created for an External Reviewer (e.g., other Federal or State government agency) or a company that is not BOEM qualified (e.g. State-only operators or Service providers), conducting offshore operations under contract for a BOEM-qualified company, or is a State Lease holder with no Federal leases, or providing information to BSEE on a voluntary basis.

____ I authorize this user to have full access to all my company’s data and information stored in the BSEE TIMS Web database.

List all companies for which the user will view or submit data. These must be BOEM-qualified companies for which the representative below has BOEM signature authority, a Non BOEM-Qualified company under contract to a BOEM-qualified company, or a Surety company for which the representative is designated as having Power of Attorney (POA) on the attached POA document.

BSEE/BOEM Company Number Company Name

(Leave blank for Surety or (Must match that on BOEM Qualification File

Non BOEM-Qualified Companies or the attached Surety POA, if applicable)

Representative Name:

(print)

Representative Title:

Representative Signature: Date:

Updated on 02/26/2017

APPENDIX B

eWELL and BSEE TIMS Web ADMINISTRATOR ACCESS REQUEST

FORM AND INSTRUCTIONS

NOTE: This form has two sides. Make sure that you print it on the front and back of one sheet of paper.

ITEM

DESCRIPTION

Front Side Options

New Administrator

Select this option when the user performs administrator role for company. An administrator grants entitlements to other user accounts.

Add Company

Select this option when the user has a user account and wants access to a new company.

Delete Administrator

Select this option to delete the user from the system. BSEE will accept notice of deleted users only from the company administrator. The company administrator must terminate the user’s entitlement record in the eWell database to remove the user’s ability to access the company’s data and information in the system before sending in the form.

Existing eWell User/Administrator or TIMS Web user Requesting TIMS Web Admin account

Select this option if the user has an eWell account and/or has a General User TIMS Web account but now requires an administrator account in TIMS Web

Front Side User Information

Name

Enter the name of the user who wants access to the eWell system. The first name, middle initial, and last name are required fields.

Title

Enter the company title of the person requesting access to the eWell system.

Address

Enter the company mailing address for person requesting access to the eWell system. This address will be used to correspond with the user.

Phone Number and Fax Number

Enter the numbers for the user who wants access to the eWell system.

E-mail Address

Enter the e-mail address of the person requesting access to the eWell system. This address will be used to correspond with the user and is a required field.

Identity Verification Questions

Answer one of the questions. When the user calls the BSEE Enterprise IT service desk, this information will be used to verify the caller’s identity before any problem can be addressed.

Consent

Check this box. This verifies that the user has read the certifications and agrees with them.

Signature and Date

The user needs to sign and date the form.

Back Side

BSEE Company Number and Name

Enter the companies to whose records the user needs access and for which the signer has signature authority. BSEE will verify the signature for each company before granting the user access to the company’s records on the system. If a company is listed for which the signer does not have signature authority, the whole form will be returned and no action will be taken.

Entitlement Authorization

Initial to allow the user to act as an administrator.

Representative

Name

Enter the name of the person with BSEE signature authority. This person’s name must match the name on the BSEE Qualification File for the company.

Representative Title

Enter the title of the person listed for Representative Name. The title must match the title on the BSEE Qualification File.

Representative Signature

The person with BSEE signature authority must sign and date the form.

Updated on 02/26/2017

U.S. Department of the Interior

Bureau of Safety and Environmental Enforcement (BSEE)

eWell and TIMS Web Administrator Access Request Form

(This form must be printed on one page only, front and back)

I am requesting the following administrator additions or changes:

_______ New Administrator

_______ Add Company

_______ Delete Administrator

_______ Existing eWell User/Admin or TIMS Web user Requesting TIMS Web Admin account

Administrator information: (leave User ID blank for new users)

Current eWell User ID: TIMS Web User ID:

Name:

______ __ ______Prefix First Name MI Last Name Suffix

Title:

Address: Employer Name:

Division:

Street:

City: State:

ZIP:  Country:

Phone Number: Fax Number:

Email:

CERTIFICATION

1. I understand that using the BSEE/BOEM TIMS Web system and eWell system means I will be using BSEE/BOEM Computer Systems, Electronic Mail, Internet connections and associated equipment, software and data. These resources are to be used for official government business only and in compliance with Department of the Interior and bureau policies. Law prohibits any other use of these items (18 USC Sec. 641). Violations of the law can result in loss of system access and criminal penalties.

2. If I am aware of a security breach (password sharing, hacking), I will immediately notify the BSEE Enterprise IT Service Desk.

3. I will select my own password and I will NOT share my password or username with anyone. If I no longer need access to the TIMS Web system and eWell system for any reason, I will expire all my entitlements and submit a new form to BOEM to delete my username from the system.

4. I have read the TIMS Web and eWell disclaimer and agree to the conditions specified in the document. __ I consent and will adhere to the above conditions.

Administrator Signature:  Date:

Updated on 02/26/2017

Initial the appropriate authorization. Only one block must be initialed. ADMINISTRATOR AUTHORIZATION

Please initial if administrator is employed by a BOEM-Qualified company.

____ I authorize this administrator to have full access to all my company’s data and information stored in the BOEM/BSEE TIMS Web and eWell databases. The Administrator will be responsible for granting entitlements/roles (View, Enter, Submit, et cetera) to my company’s data stored in the TIMS Web and eWell databases for company users.

AGENT ADMINISTRATOR AUTHORIZATION

Please initial if administrator is employed by a third party company acting as an agent.

____ I authorize this administrator as an agent administrator. As an agent administrator, they will have full access to all my company’s data and information stored in the BOEM/BSEE TIMS Web and eWell databases. The Administrator will be responsible for granting entitlements/roles (View, Enter, Submit, et cetera) to company data stored in the TIMS Web and eWell databases for my company’s users and agents.

NON-BOEM-QUALIFIED COMPANY AUTHORIZATION

Please initial if this account is being created for an External Reviewer (e.g., other Federal or State government agency) or a company that is not BOEM qualified (e.g. State-only operators or Service providers), conducting offshore operations under contract for a BOEM-qualified company, or is a State Lease holder with no Federal leases, or providing information to BSEE on a voluntary basis.

____ I authorize this administrator to have full access to all my company’s data and information stored in the BOEM/BSEE TIMS Web database. The Administrator will be responsible for granting entitlements/roles (View, Enter, Submit, et cetera) to my company’s data stored in the TIMS Web database for company users.

List all companies for which the administrator will grant entitlements/roles. These must be BOEM companies for which the representative below has BOEM signature authority, a Non BOEM-Qualified company under contract to a BOEM-qualified company, or a Surety company for which the representative is designated as having Power of Attorney (POA) on the attached POA document.

BSEE/BOEM Company Number Company Name

(Leave blank for (Must match that on BOEM Qualification File

Non BOEM-Qualified Companies or the attached Surety POA)

Representative Name:

(print)

Representative Title:

Representative Signature:  Date:

Updated on 02/26/2017

APPENDIX C

eWELL and TIMS Web System Disclaimer

Security

BSEE, as developer and manager of the eWell and TIMS Web Systems website, has taken several steps to safeguard the integrity of its telecommunications and computing infrastructure, including but not limited to authentication, monitoring, auditing, and encryption. Security measures have been integrated into the design, implementation, and day-to-day practices of the entire operating environment as part of the BSEE’s continuing commitment to risk management. BSEE does not, however, warrant that the security of information provided via the website is fail proof.

Information presented and collected on this website is shared between BSEE and the company or agent users submitting the data. Restrictions have been put in place to maximize the security of the data. All proprietary information collected will be used only for the purposes for which it was provided and will not be shared with another entity except as prescribed by law. The nonproprietary data submitted will be made available in the BSEE Public Information Office. While BSEE makes every effort to provide accurate and complete information, we provide no warranty, expressed or implied, as to the accuracy, reliability or completeness of furnished data.

For site security purposes and to ensure that this service remains available to all users, this Government computer system employs software programs to monitor network traffic to identify unauthorized attempts to upload or change information, or otherwise cause damage. Unauthorized attempts to upload information or change information on this website are strictly prohibited and may be punishable under the Computer Fraud and Abuse Act. Information may also be used for authorized law enforcement investigations.

What Happens to Information You Submit to Us?

The information you submit to us will be transmitted through secure lines to our departmental database. Any private information will only be used for the purposes for which it was provided and will not be shared with another entity except as prescribed by law.

Cookies

This website uses session cookies only. The site will not store a permanent cookie on your computer. The session cookie is used to store a randomly generated identifying temporary tag on your computer and is stored in memory only.

Personally Identifiable Information

You may choose to provide us a comment or question with your personal information. We use the information to improve our service to you or to respond to your request. BSEE will not distribute the e-mail address for any reason except to respond to your request.

Updated on 02/26/2017

Northwestern University

Office of Human Resources

Exempt or Not Exempt?

The Fair Labor Standards Act (FLSA)

Background and Purpose 

The Fair Labor Standards Act (FLSA) is a U.S. Federal Law enacted in 1938 to prohibit

employers from taking advantage of employees. It includes provisions which:

Prohibit child labor

Set minimum wage

Require overtime pay

Require equal pay (prohibit sex based wage differentials)

Require record keeping

The FLSA is an Employee Protection Act whereby employees are generally presumed not exempt and entitled to overtime pay; however there are exemptions from this law for executive, administrative, professional, and computer employees. The law requires that employees be paid for overtime hours worked at a rate of 1 1/2 times their regular rate of pay (unless the employee is exempt from the provision). Overtime is defined as more than 40 hours worked in a work week. It requires significant record keeping for compliance. Employees may be exempt from this law if they meet 3 tests in regard to:

1. Salary Level

2. Salary Basis

3. Job Duties

The FLSA was updated in 2004.

The information below is designed to assist you in determining whether an employee is exempt from the FLSA.

Test 1: Salary Level 

The employee must be paid at least $455 per week or $910 biweekly or $1971.66 monthly or $23,660 annually. These amounts are actual payments and salary should not be pro-rated for part-time employees.

Test 2: Salary Basis 

Employee must receive a predetermined, fixed salary that is not subject to reduction due to variations in quality or quantity of work performed (except for some very narrow specified circumstances).

Test 3: Job Duties 

Employee must meet all of the criteria specified in one or more of the following exemptions:

Executive

Administrative

Education Establishments and Administrative

Professional – Learned

Professional – Artistic

Computer-Related

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The term “primary duty” is used throughout each of the exemptions. The definition of primary duty is the principal, main, major or most important duty that the employee performs. Determination of an employee’s primary duty must be based on the character of the employee’s job as a whole. Factors to consider include relative importance of the exempt duties as compared with other types of duties; amount of time spent performing exempt work (not more than 20% of an employee’s time may be spent on nonexempt work); employee’s relative freedom from direct supervision; and relationship between the employee’s salary and wages paid to other employees for the kind of nonexempt work performed by the employee. Employees who spend more than 80% of their time performing exempt work will generally satisfy the primary duty requirement.

EXECUTIVE

In order for an employee to be considered exempt as an executive, all of the following 3 criteria

must be met:

1. Primary duty: manages enterprise, or customarily recognized (permanent status)

department or subdivision. Managing includes activities such as:

Interviewing, selecting, training and disciplining employees

Setting and adjusting pay and work hours

Planning and apportioning work among employees

Maintaining production or sales records

Appraising employee’ productivity and efficiency

Handling employee complaints and grievances

Determining the techniques to be used; the type of materials, supplies, machinery,

equipment or tools to be used; or the merchandise to be bought, stocked and sold

Providing for the safety and security of the employees or the property

Planning and controlling the budget

Monitoring or implementing legal compliance measures

2. Customarily and regularly direct the work of at least 2+ full-time employees (FTEs)

3. Hires or fires employees, or whose recommendations as to hiring, firing, advancement, promotion or any other change of status of other employees are given particular weight. Generally, an executive’s recommendations must pertain to employees whom the executive customarily and regularly directs. It does not include occasional suggestions. An employee’s recommendations may still be deemed to have “particular weight” even if a higher level manager’s recommendation has more importance and even if the employee does not have authority to make the ultimate decision as to the employee’s change in status.

Additional Information 

A common question that arises under the executive exemption is how to classify employees who perform both exempt management duties and nonexempt duties. The regulations state that a manager who performs both exempt and nonexempt work at the same time is not automatically disqualified from the executive exemption. Generally, the exempt executives themselves make the decision regarding when to perform nonexempt duties. In contrast, the nonexempt employee generally is directed by a supervisor to perform the exempt work or performs the exempt work for defined time periods. For example, if an assistant manager’s primary duty is management, performing work such as serving customers, cooking food, stocking shelves and cleaning the establishment does not preclude the exemption. An assistant manager can supervise employees and serve customers at the same time without losing the exemption. In contrast, a relief supervisor or working supervisor whose primary duty is

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performing nonexempt work on the production line in a manufacturing plant does not become exempt merely because he occasionally has some responsibility for directing the work of other nonexempt production line employees when, for example, the exempt supervisor is on vacation.

ADMINISTRATIVE

In order for an employee to be considered exempt as an administrative employee, both of the

following 2 criteria must be met:

1. Primary duty: performs office or non-manual work directly related to management or general business operations (tax, finance, accounting, budgeting, auditing, insurance, quality control, purchasing, procurement, advertising, marketing, public &/or government relations, research, safety & health, human resources, labor relations, computer network, database administration, legal & regulatory compliance, etc. Work must be directly related to assisting with the running or servicing of the business and does not include working on a manufacturing production line or selling a product in a retail or service establishment.) of the employer or the employer’s customers.

2. Primary duty includes exercise of discretion and independent judgment with respect to

matters of significance and implies that the employee has authority to make an independent choice, free from immediate direction or supervision. This involves the comparison and the evaluation of possible courses of conduct, and acting or making a decision after the various possibilities have been considered. Two or 3 of the following must apply to answer “yes” to this criteria:

Authority to formulate, affect, interpret, or implement management policies or

operating practices

Carries out major assignments in conducting the operations of the business

Performs work that affects business operations to a substantial degree

Authority to commit the employer in matters that have significant financial impact

Authority to waive or deviate from established policies and procedures without prior

approval, and other factors set forth in the regulation

Authority to negotiate and bind the company on significant matters

Provides consultation or expert advice to management

Involved in planning long- or short-term business objectives

Investigates and resolves matters of significance on behalf of management

Represents the company in handling complaints, arbitrating disputes or resolving

grievances

Additional Information 

Discretion and independent judgment does not include:

Applying well-established techniques, procedures or specific standards described in

manuals or other sources

Clerical or secretarial work

Recording or tabulating data

Performing mechanical, repetitive, recurrent or routine work

Employees are not exempt if they use manuals to apply well-established techniques or procedures within closely prescribed limits.

“Matters of significance” refer to the level of importance or consequence of the work performed. An employee does not exercise discretion and independent judgment with respect to matters of significance merely because the employer will experience financial losses if the employee fails to perform the job properly. Similarly, an employee who operates very expensive equipment

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does not exercise discretion and independent judgment with respect to matters of significance merely because improper performance of the employee’s duties may cause serious financial loss to the employer. The fact that an employee’s decisions are revised or reversed after review does not mean that the employee is not exercising discretion and independent judgment. The exercise of discretion and independent judgment must be more than the use of skill in applying well-established techniques, procedures or specific standards described in manuals or other sources.

Typical Administrative Exempt Jobs: 

An employee who leads a team of other employees assigned to complete major projects Executive assistant or administrative assistant to a business owner or senior executive of a large business who has been delegated authority regarding matters of significance Management consultants who study the operations of a business and propose changes in organization

Human resource managers who formulate, interpret or implement employment policies generally meet the administrative duties requirements

Typical Administrative Nonexempt Jobs: 

Ordinary inspection work involving well-established techniques and procedures

Examiners and graders who perform work involving comparison of products with

established standards

Comparison shoppers who merely report the prices at a competitor’s store

Public sector inspectors or investigators

Personnel clerks who “screen” applicants to obtain data regarding minimum

qualifications and fitness for employment generally are not exempt administrative

employees

EDUCATIONAL ESTABLISHMENTS AND ADMINISTRATIVE

In order for an employee to be considered exempt as an administrative employee within an educational establishment the following criteria must be met:

1. Primary duty: performs administrative functions directly related to academic instruction or training in an educational establishment. Academic administrative functions include operations directly in the field of education, and do not include jobs relating to areas outside the educational field.

Additional Information 

Employees engaged in academic administrative functions include:

Superintendent or other head of an elementary or secondary school system

Any assistants responsible for administration of such matters as curriculum, quality and

methods of instructing, measuring and testing the learning potential and achievement of

students, establishing and maintaining academic and grading standards, and other

aspects of the teaching program

Principal and any vice-principals responsible for the operation of an elementary or

secondary school

Department heads in institutions of higher education responsible for the various subject

matter departments

Academic counselors and other employees with similar responsibilities

Having a primary duty of performing administrative functions directly related to academic

instruction or training in an educational establishment includes, by its very nature, exercising

discretion and independent judgment with respect to matters of significance.

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COMPUTER-RELATED

In order for an employee to be considered exempt as an administrative employee, both of the

following criteria must be met:

1. If compensated on an hourly basis, at a rate not less than $27.63 an hour

2. Employed as a computer systems analyst, computer programmer, software engineer or other similarly skilled worker in the computer field performing the primary duties described below:

Application of systems analysis techniques and procedures, including consulting with users, to determine hardware, software or system functional specifications

Design, development, documentation, analysis, creation, testing or modification of computer systems or programs, including prototypes, based on and related to user or system design specifications

Design, documentation, testing, creation or modification of computer programs related to machine operating systems; or

Combination of aforementioned duties, performance of which requires the same level of skills

Typical Computer-Related Nonexempt Jobs: 

The computer employee exemption does not include employees engaged in the manufacture or repair of computer hardware and related equipment.

Employees whose work is highly dependent upon, or facilitated by, the use of computers and computer software programs (e.g., engineers, drafters and others skilled in computer-aided design software), but who are not primarily engaged in computer systems analysis and programming or other similarly skilled computer-related occupations identified in the primary duties test described above, are also not exempt under the computer employee exemption.

PROFESSIONAL - LEARNED

In order for an employee to be considered exempt as an administrative employee, all of the

following 3 criteria must be met:

1. Primary duty: performs work requiring advanced knowledge:

Predominantly intellectual in character

Includes work requiring the consistent exercise of discretion and judgment

The advanced knowledge is generally used to analyze, interpret or make deductions

from varying facts or circumstances

Not work involving routine mental, manual, mechanical, or physical work

Cannot be attained at the high school level

2. Knowledge must be in field of science or learning (Accounting, Actuarial Computation, Architecture, Biological Sciences, Chemical Sciences, Engineering, Law, Medicine, Pharmacy, Physical Sciences, Teaching, Theology)

3. Knowledge customarily acquired by a prolonged course of specialized intellectual instruction. Specialized academic training is a prerequisite for entering the profession with the best evidence that an employee meets this requirement is possession of the appropriate academic degree.

Additional Information 

The learned professional exemption is not available for occupations that may be performed with:

Only the general knowledge acquired by an academic degree in any field

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Knowledge acquired through an apprenticeship

Training in the performance of routine mental, manual, mechanical or physical

processes

The exemption also does not apply to occupations in which most employees acquire skill by experience.

Exemption is also available to employees in learned professions who:

Have substantially the same knowledge level and

Perform substantially the same work as the degreed professionals,

But attained the advanced knowledge through a combination of work experience and

intellectual instruction

Examples:

Lawyer who did not attend law school

Chemist who does not have a chemistry degree

Typical Learned Professional Exempt Jobs: 

Lawyers

Teachers

Accountants

Pharmacists

Engineers

Actuaries

Chefs

Athletic trainers

Licensed funeral directors or embalmers

Typical Learned Professional Nonexempt Jobs: 

Accounting clerks and bookkeepers who normally perform a great deal of routine work

Cooks who perform predominantly routine mental, manual, mechanical or physical work

Paralegals and legal assistants

Engineering technicians

PROFESSIONAL – ARTISTIC

In order for an employee to be considered exempt as Professional – Artistic, the following

criteria must be met:

1. Primary duty: performs work requiring invention, imagination, originality or talent in a recognized field (music, writing, acting, graphic arts) of artistic or creative endeavor.

Typical Creative Professional Exempt Jobs: 

Actors Musicians

Composers Soloists

Certain painters Writers

Cartoonists Essayists

Novelists

Northwestern University

Office of Human Resources

FLSA WORKSHEET

JOB TITLE: Date of Test: Please answer yes or no (Y/N) to the statements below. Any no (N) answer makes the job non-exempt.

TEST 1: SALARY LEVEL Y/N

Employee’s salary greater than $455/week or $910 biweekly or $1971.66 monthly or $23,660 annually?

TEST 2: SALARY BASIS Y/N

Employee receives a predetermined, fixed salary that is not subject to reduction due to variations in quality or quantity of work performed?

TEST 3: JOB DUTIES (must meet all criteria in at least 1 category below) Y/N

Executive – All of the following 3 criteria must be met: 

1. *Primary duty: manages enterprise, or customarily recognized department or subdivision

2. Customarily and regularly direct the work of at least 2+ full-time employees (FTEs)

3. Hires or fires employees, or whose recommendations are given particular weight

Administrative – Both of the following 2 criteria must be met: 

1. *Primary duty: performs office or non-manual work directly related to management or general business operations (e.g.: tax, finance, accounting, budgeting, auditing, insurance, quality control, purchasing, procurement, advertising, marketing, public &/or government relations, research, safety & health, personnel management, benefits, human resources, labor relations, computer network, Internet and database administration, legal & regulatory compliance, etc.) of the employer or the employer’s customers

2. *Primary duty includes exercise of discretion and independent judgment (authority to formulate, affect, interpret, or implement management policies or operating practices; carries out major assignments in conducting the operations of the business; performs work that affects business operations to a substantial degree) with respect to matters of significance.

Educational Establishments and Administrative 

1. *Primary duty: performs administrative functions (administration of such matters as curriculum, quality and methods of instructing, measuring and testing the learning potential and achievement of students, establishing and maintaining academic and grading standards, and other aspects of the teaching program OR department heads in institutions of higher education responsible for the various subject matter departments OR academic counselors and other employees with similar responsibilities) directly related to academic instruction or training in an educational establishment.

Professional – All of the following 3 criteria must be met: 

1. *Primary duty: performs work requiring advanced knowledge

2. Knowledge must be in field of science or learning (Accounting, Law, Teaching, Engineering, Biological, Chemical or Physical Sciences)

3. Knowledge customarily acquired by a prolonged course of specialized intellectual instruction

Computer-Related – Both of the criteria listed below must be met: 

1. If compensated on an hourly basis, at a rate not less than $27.63 an hour

2. Employed as a computer systems analyst, computer programmer, software engineer or other similarly skilled worker in the computer field performing the primary duties described below:

Application of systems analysis techniques and procedures, including consulting with users, to determine hardware, software or system functional specifications

Design, development, documentation, analysis, creation, testing or modification of computer systems or programs, including prototypes, based on and related to user or system design specifications

Design, documentation, testing, creation or modification of computer programs related to machine operating systems; or

Combination of aforementioned duties, performance of which requires the same level of skills

*Primary duty: looking at the job as a whole - principal, main, major or most important reason that the job exists. Non-exempt work can be no more than 20% of the job.

?vue Sample GUI User Manual

v1.0.8

Table of Contents

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1. What is ?·vue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. About this guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1. System Requirements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2. Qt Installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3. Third-praty Libraries Installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.4. Build ?vue Sample GUI Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3. Windows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.1. System Requirements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2. Qt Installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.3. Build ?vue Sample GUI Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

4. Mac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.1. System Requirements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.2. Qt Installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.3. Build ?vue Sample GUI Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

The Xvue Sample GUI User Manual provides an easy way to build a GUI application to use Xvue SDK on Linux, Mac and Windows.

The Xvue Sample GUI User Manual is still under construction, so the content of this document may change over time.

1. Introduction

This part gives you an overview of the technologies described in this guide.

1.1. What is ð·vue

Quanta Research Institute and MIT researchers collaborated to develop Xvue that amplifies color changes and movements in video that are invisible to the naked eye. The Xvue allows you to 'see' a person’s pulse by magnifying the color of the person’s skin, and to 'see' a person breathing by amplifying the subtle changes in movement.

Magnification enhances small movements in the source video. It works best for periodic changes such as breathing or pulse. This technique is designed to expose very small changes when the rest of the image is very still.

Quanta Research Institute has created this software development kit so that you can experiment with the Xvue on your own videos and webcam.

For more information on the technology, please contact QRI (info@lambda.qrilab.com).

1.2. About this guide

This guide covers the basic instructions needed to build a GUI application to use Xvue SDK. These instructions are intended to be used on Xvue SDK installed machine.

2. Linux

2.1. System Requirements

The ?vue sample GUI relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this ?vue SDK.

2.1.1. Minimum System Requirements

* 2 GHz or faster 64-bit (x64) processor

* 4 GB RAM

* 2 GB available hard disk space

Laptop which has both Intel and NVIDIA graphics cards might have some issues of extension "GLX" missing on display

2.1.2. Environment Tested

* Ubuntu 12.04 64-bit

* Qt 5.5.0 for Linux 64-bit

2.2. Qt Installation

2.2.1. Ubuntu

We recommend to use the Qt 5.5.0 for Linux 64-bit.

1. Download the Qt 5.5.0 for Linux 64-bit installer.

2. Change mode of the installer and launch it with the steps below:

chmod +x qt-opensource-linux-x64-5.5.0-2.run

sudo ./qt-opensource-linux-x64-5.5.0-2.run

3. Follow the instructions to finish installation

2.3. Third-praty Libraries Installation

2.3.1. Ubuntu

1. Please follow the steps below to install third-party libraries:

sudo apt-get install libglu1-mesa-dev libconfig++8-dev

2.4. Build ðvue Sample GUI Application

2.4.1. Ubuntu

1. Download GUI Sample (All Platform) from the ?vue webiste

2. Decompress the tarball

3. Launch Qt Creator from the Dash Home

4. In the Welcome tab, click the Open Project button

5. Open the GekoQt.pro which located in the LambdaVueGUI directory

6. Click the Configure Project button

7. Select Build from the toolbar and click the Build All

The builds should produce no error message. The resulting binary will appear under the same directory of GekoQt.pro called LambdaVueGUI.

Once you compile finished, you can download our sample video from following URL and use this video to test ?vue sample GUI by double click the resulting binary LambdaVueGUI or by typing following at the command line :

./LambdaVueGUI

* sample video : http://lambda.qrilab.com/uploads/tutorial.mp4

3. Windows

3.1. System Requirements

The ?vue sample GUI relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this ?vue SDK.

3.1.1. Minimum System Requirements

* 2 GHz or faster 64-bit (x64) processor

* 4 GB RAM

* 2 GB available hard disk space

3.1.2. Environment Tested

* Windows 7 32-bit / 64-bit

* Qt 5.4.2/5.2.1 with MinGW and OpenGL

In Windows system, you need to build with Qt, MinGW and OpenGL and it can run on 64-bit environment.

3.2. Qt Installation

We recommend to use the Qt 5.4.2 with MinGW and OpenGL or Qt 5.2.1 with MinGW and OpenGL.

1. Download the Qt 5.4.2 with MinGW and OpenGL or Qt 5.2.1 with MinGW and OpenGL.

2. Double click on the downloaded file

3. Follow the instructions to finish installation

3.3. Build ðvue Sample GUI Application

1. Download GUI Sample (All Platform) from the ?vue webiste

2. Decompress the tarball

3. Copy dll files listed below from Qt/MinGW installation path to the LambdaVueGUI directory:

The dll files would located in a path like C:\Qt\Qt5.4.2\5.4.2\mingw49_32\bin

* icuin*.dll

* icuuc*.dll

* icudt*.dll

* libgcc_s_dw2-1.dll

* libstdc++-6.dll

* libwinpthread-1.dll

* Qt5Core.dll

* Qt5Gui.dll

* Qt5OpenGL.dll

* Qt5Widgets.dll

4. Launch Qt Creator from the Start menu

5. In the Welcome tab, click the Open Project button

6. Open the GekoQt.pro which located in the LambdaVueGUI directory

7. Click the Configure Project button

The default installation path of LambdaVue SDK is C:\LambdaVueSDK. If you have changed the installation path, please remember to modify your SDK_ROOT in line 23 of your Geko.pri.

8. Select Build from the toolbar and click the Build All

The builds should produce no error message. The resulting binaries will appear under the same directory of GekoQt.pro called LambdaVueGUI.exe.

Once you compile finished, you can download our sample video from following URL and use this video to test ?vue sample GUI by double click on the LambdaVueGUI.exe.

* sample video : http://lambda.qrilab.com/uploads/tutorial.mp4

4. Mac

4.1. System Requirements

The ?vue sample GUI relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this ?vue SDK.

4.1.1. Minimum System Requirements

* 2 GHz or faster Intel-based processor

* 4 GB RAM

* 2 GB available hard disk space

4.1.2. Environment Tested

* Mac OS X 10.9 or later

* Xcode

Before you install Qt, you need install Xcode first. You can download Xcode from App Store. But it may require you to use the newest version of Mac OS X.

* Qt 5.5.0 for Mac

4.2. Qt Installation

We recommend to use the Qt 5.5.0 for Mac.

1. Download the Qt 5.5.0 for Mac installer.

2. Follow the instructions to finish installation

4.3. Build ðvue Sample GUI Application

1. Download GUI Sample (All Platform) from the ?vue webiste

2. Decompress the tarball

3. Launch Qt Creator from directory where you installed the Qt.

4. In the Welcome tab, click the Open Project button

5. Open the GekoQt.pro which located in the LambdaVueGUI directory

6. Click the Configure Project button

7. Select Build from the toolbar and click the Build All

The builds should produce no error message. The resulting binaries will appear under the same directory of GekoQt.pro called LambdaVueGUI.

Double click the LambdaVueGUI to open the sample application.

Once you compile done, you can download our sample video from following URL and use this video to test ?vue sample GUI

* sample video : http://lambda.qrilab.com/uploads/tutorial.mp4

        SCHOOL OF BIORESOURCES AND TECHNOLOGY BACKGROUND

       Founded in 1993 in response to the country’s transition from a structurally agriculturally-based to an industrially-oriented economy, the school’s aim is to produce outstanding and dedicated graduates who are able to conduct research and tackle challenging questions arising from this transition. The school consists of four divisions that address different aspects to these problems such as agricultural-waste utilization and management, manufacturing of value-added agricultural products by means of biotechnology and production of high-value compounds from microbes. We are also a center for basic and applied research on biomolecules, including starch, lipids and oil, proteins, fragrance and flavors.

      We address questions to improve overall productivity and quality of agricultural products and logistics. With this, we are pursuing our goal to ensure effective and sustainable management of Thailand’s natural bio-resources (including its wild biodiversity) and environment. At present, the school offers advanced courses in Master’s and Doctoral degrees in Biotechnology, Biochemical Technology, Postharvest Technology and Natural Resources Management. Also in response to the importance of multi-disciplinary studies, we, in close collaboration with the Department of Information Technology have launched the first in Thailand, Master’s degree program in Bioinformatics and Systems Biology in 2003.

Leading Research and Innovation

      SBT is a pioneer in the field of Bioresources and Technology in Thailand. Our R&D programs offer advanced training in the areas of Biotechnology, Postharvest Technology, Biochemical Technology and Natural Resource Management with special focus on food, feed, fuel and (bio-) pharmaceutics. We have developed and transferred new technology to many industrial partners. Many servicescenters are located on our campus, including the Pilot Plants Development and Training Institute (PDTI) and National Biopharmaceutical Facility (NBF), the Excellent Center for Waste Water Utilization and Management (ECoWaste). In 2010, KMUTT was recognized by the Commission of Higher Education, Thailand as one of the nine research universities and, SBT is proud of its contribution to this success.

1 School of Bioresources and Technology

Promising people in the house

      SBT is also a home to several globally recognized faculty members and researchers. Over the years, we have produced a number of qualified graduates, and have published over 500 national and international peer-reviewed papers and filed over 20 patents since our founding. Apart from academic responsibility, it is SBT commitment to serve the local community and to contribute to solve the country’s urgent problems.

Innovative study program

      Our unique graduate programs are multi-disciplinary, covering a wide range of research interests. Intensive research is conducted in fully equipped laboratories as well as in real workplace settings. We provide students the opportunity to gain career-broadening experiences while working with the industry/community and through training overseas.

Scholarship opportunities

      SBT attracts highly qualified local as well as international students each year. We guarantee scholarships to qualified new students upon admission.

Learning and living in harmony

      This campus established in 2000, is located in a suburb Bangkhunthian seacoast not far from the downtown of vibrant capital city of Bangkok. Its location is a well-known destination for seafood lovers and other attractions. Bangkhunthian campus is also a green, eco-friendly learning environment. To ensure healthy learning and living, many services such as university housing, high speed internet, transportation, sports complex, health care center and several small restaurants are available on campus.

      Mission: School of Bioresources and Technology (SBT) is a graduate school that engages in high-quality research to create knowledge that serves the country and society. SBT offers academic service and technology transfer for individuals, corporates, and enterprises. SBT is committed to preserving and enriching cultural heritage as the foundation of society.

      Vision: We are committed to producing graduates with knowledge, quality, and ethical standing, creating high-quality bio-based research and science and technology management, and being internationally recognized with regards to effective management systems, in order to serve society.

2 School of Bioresources and Technology

BOARD OF DIRECTOR AND CHAIRMAN

Position

Name-Surname

E-mail

Telephone

No.

Board of Director

Dean

Assoc.Prof.BoosyaBunnag

boosya.bun@kmutt.ac.th

0-2470-7355

Associate Dean for Administration

Assoc. Prof.Dr.Varit Srilaong

varit.sri@kmutt.ac.th

0-2470-7726

Associate Dean for Academic Affairs

Asst.Prof.Dr.SudaratTripetchkul

sudarut.tri@kmutt.ac.th

0-2470-7556

Associate Dean for Research

Assoc.Prof.Dr.Werasak Surareungchai

werasak.sur@kmutt.ac.th

0-2470-7474

Associate Dean for Quality Assurance

Asst. Prof. Dr. Kanokwan Poomputsa

kanokwan.poo@kmutt.ac.th

0-2470-7500

Chairman 

Biotechnology (BIT)

Asst. Prof. Dr. Kanokwan Poomputsa

kanokwan.poo@kmutt.ac.th

0-2470-7500

Biochemistry Technology (BCT)

Assoc .Prof.Dr.Kornkanok Aryusuk

kornkanok.ary@kmutt.ac.th

0-2470-7758

Postharvest Technology (PHT)

Asst. Prof. Dr. Songsin Photchanachai

songsin.pho@kmutt.ac.th

0-2470-7752

Natural Resource Management (NRM)

Asst.Prof.Dr.Sudarut Tripetchkul

sudarut.tri@kmutt.ac.th

0-2470-7556

Bioinformatics and System Biology (BIF)

Asst.Prof.Dr.Marasri Reungjitchachawalya

marasri.rue@kmutt.ac.th

0-2470-7481

3 School of Bioresources and Technology

ADVISOR FOR FIRST YEAR STUDENTS

Program

Degree

Name of Advisor

Biotechnology

Ph.D.

Advisor of Thesis

Biotechnology (Research Program)

M.Sc.

Asst.Prof.Dr.Kanokwan Poomputsa

Biotechnology Business and Management

M.Sc.

Assoc.Prof.Dr.Yuwapin Dandusittapunth

Postharvest Technology

Ph.D.

Advisor of Thesis

Postharvest Technology

M.Sc.

Assoc. Prof.Dr.Varit Srilaong

Natural Resource Management

Ph.D. (IBP)

Advisor of Thesis

Natural Resource Management

M.Sc.

Asst.Prof.Dr.Sudarut Tripetchkul

(Advisor for first year students)

Biochemical Technology

Ph.D

Advisor of Thesis

Biochemical Technology

M.Sc.

Assoc.Prof.Narumon Jeyashoke

(Advisor for first year students)

Bioinformatics and System Biology (BIF)

M.Sc.

Asst.Prof.Dr.Marasri Reungjitchachawalya (Advisor for first year students)

Bioinformatics and System Biology (BIF)

Ph.D

Advisor of Thesis

4 School of Bioresources and Technology

PROGRAM IN SCHOOL OF BIORESOURCES AND TECHNOLOGY

Doctoral Degree:

1. Doctor of Philosophy Program in Biotechnology (International Program)

2. Doctor of Philosophy Program in Biochemical Technology

3. Doctor of Philosophy Program in Postharvest Technology (International Program)

4. Doctor of Philosophy Program in Bioinformatics and Systems Biology (International

Program)

Master Degree:

1. Master of Science Biotechnology Program (International Program)

2. Master of Science Program in Postharvest Technology (International Program)

3. Master of Science Program in Biochemical Technology

4. Master of Science / Master of Engineering and Master of Arts Program in Natural Resource Management

5. Master of Science Program in Bioinformatics and Systems Biology (International Program)

Biotechnology Program

      The Biotechnology graduate program at the School of Bioresources and Technology is a leading interdisciplinary and innovative international graduate program in Thailand aiming to provide students seeking comprehensive training in a variety of biotechnology disciplines in response to the dramatic expansion of biotechnology-oriented industries worldwide. The curriculum is specifically designed to nurture research competency, analytical skills as well as professional attributes of the students. The program prepares students for careers in the established and emerging field of biotechnology by offering study plans spanning all the aspects of biotechnology including traditional research, industrial practice, and bio-entrepreneurship. Graduate students will be provided with solid training in core subjects such as molecular biotechnology, bioprocess engineering, environmental sciences, and biotechnology business initiatives. There are three modules offered which students can choose according to their interests. 1. Biotechnology: concentrating on in-depth research under close supervision of qualified researchers/advisors. 2. Biotechnopreneur Biotechnology Business and Management emphasizing integrating basic/advanced theory, biotechnology relevant technologies together with business initiatives to develop novel biotechnology products which will create value. This module isinternship-based and problem

5 School of Bioresources and Technology

based to meet the needs of the private sector. 3. Biotechnology Practice School: focusing on applying scientific knowledge to solve problem existing in the real factory environments. Students are therefore exposed to working systems the production as well as R&D facilities. This module is problem-based focusing on real problems of the private sector.

Biochemical Technology Program

      The Division of Biochemical Technology (BCT) offers a graduate program leading to a Master of Science and Doctor of Philosophy. Our pioneering program was initiated with the explicit purpose of producing exceptional biochemical technologists capable of conducting research in the field of biochemical technology and working in collaboration with engineers or other personnel to develop new technology for efficient utilization of Thailand’s bioresources. An enhanced learning and training environment has been created for students and researchers at all levels here and through our national and international collaborations all around the world. Students are provided with opportunities to explore various aspects of BCT research which focuses on the biochemical processing of biological materials. Present research activities include lipids, enzymes and proteins, flavors and carbohydrates and nucleic acids. Courses are interdisciplinary and customer-oriented, open to both graduate students as well as any interested individuals engaged in research and manufacture of biochemical products. A number of student scholarships are available each year for qualified candidates. Our graduates have moved on to careers in academics, as well as in industry, government agencies and non-governmental organizations.

Postharvest Technology Program

      Developing countries in the tropics face common problems in regard to postharvest losses of agricultural produce. Loses in product quality and quantity occur at all stages in the postharvest chain, from harvesting handling, storage, and marketing to the final delivery to consumers. Although highly specific to commodity, location, and growing season, about one-half of the total production of agricultural produce is wasted after harvest. The reduction of postharvest losses of agricultural produce is a major strategy to achieve food security and economic prosperity. In response to this need, KMUTT established the postharvest technology program in 1994. The program institutes a more focused and relevant graduate education to develop competent and active manpower resources in postharvest technology. It also conducts vigorous and responsive research and development, with the ultimate goal of applying developing and existing technologies to solve problems of postharvest losses and ensure a stable supply of high quality and safe agricultural products.

6 School of Bioresources and Technology

Natural Resource Management

      Southeast Asia is a region which still retains an abundance of biological as well as agricultural resources however all of these resources are under intense pressure from an expanding human population. The NRM graduate program focuses on research and development that promotes biodiversity conservation and management as well as efficient use of soil, water, and other agricultural resources. We also recognize the importance of natural resource development to regional countries and the need to create an education and research program that enables interdisciplinary study. Due to differences in regional needs, learning and exchanging knowledge through research in collaboration with local people is encouraged. The program also emphasizes the application of scientific concepts and technologies for problem solving and decision making through the use of case studies as well as training in the real world.

Bioinformatics and System Biology

     Bioinformatics and Systems Biology is a relatively new scientific discipline that is concerned with the study of biological information, ranging from the vast and rapidly accumulating genetic information/databases to patterns of protein expression and their links to disease states. Bioinformatics is very much the scientific and technical foundation of the human genome project, and promises to be a central life science of this century. The international Bioinformatics and Systems Biology course at KMUTT is designed for students who desire focused training in the elements of computer science, biology and biochemistry needed for successful careers in this exciting new discipline. Students in our program will receive comprehensive training in genomics, algorithms for sequence analysis, database design and management, software engineering and programming (including web-based development). Each student will apply their skills to a practical project, where they will design and implement a solution to a real-world problem under the guidance of an experienced mentor from industry or academia. The degree program requires a total of 38 credits (for students who have a strong background in both biological and computer sciences).In order to receive a Master’s degree, students must demonstrate mastery of the core subject matter(expected to maintain a minimum grade of "B" in all core courses)and reach a minimum TOEFL test score of 500 (paper) or 173(computer) prior to or on completion of the course. In 24months students are expected to gain the knowledge and skills

7 School of Bioresources and Technology

necessary to enter a career in industry or in research as a bioinformatics or bio-computing specialists.

NUMBER OF CREDITS REQUIRED

Degree

Number of Credits Required

Compulsory

Seminar

Elective

Thesis

Internship

Research

Study

Total

Master Degree

1. Master of Science Biotechnology Program (International Program)

Plan A.2/1 Biotechnology(Thesis 12 Credits)

11

13

12

36

Plan A.2/1 Biopharmaceutical Biotechnology

17

7

12

36

(Thesis 12 Credits)11 Plan A.2/2 (Thesis 24 Credits)

1

24

36

Plan B.Biotechnopreneur

18

15

6

39

Plan B.Biotechnology Business and Management

19

12

6

37

2.Master of Science Program in Postharvest Technology (International Program)

A.2/1 (Thesis 36 Credits)

36

36

A.2/2 (Thesis 12 Credits)

14

12

12

38

B (Special Project Study 6 Credits)

17

15

6

38

3.Master of Science Program in Biochemical Technology

A.2/1 (Thesis 36 Credits)

36

36

A.2/2 (Thesis 12 Credits)

9

16

12

37

4.Master of Science / Master of Engineering and Master of Arts Program in Natural Resource Management

A.2/1 (Thesis 36 Credits)

5

21

12

36

A.2/2 (Thesis 38 Credits)

5

27

6

38

5.Master of Science Program in Bioinformatics and Systems Biology (International Program)

A. (Thesis 38 Credits)

15

2

9

12

38

B (Thesis 38 Credits)

5

2

9

6

6

38

8 School of Bioresources and Technology

Degree

Number of Credits Required

Compulsory

Seminar

Elective

Thesis

Internship

Research

Study

Total

Doctoral Degree

1. Doctor of Philosophy Program in Biotechnology (International Program)

- M.Sc. Background

3

9

36

48

- B.Sc.Background

4

24

48

76

2.Doctor of Philosophy Program in Postharvest Technology (International Program)

- M.Sc.Background

48

48

- M.Sc.Background

6

6

36

48

- B.Sc.Background

16

9

48

74

2.Doctor of Philosophy Program in Biochemical Technology

- M.Sc.Background

3**

48

48

- B.Sc.Background

10

16

48

74

9 School of Bioresources and Technology

ACADEMIC CALENDAR 2015

ACTIVITIES BEFORE THE FIRST SEMESTER

July 2016

- Students consult with advisors for registration.

Wed 6 – Thu7

Fri 8

- Last day to submit for maintaining student status for the first semester.

Mon11 – Sat16

- Days for registration and tuition payment processing via internet;“New ACIS”.

- Days to maintain student status under approval, processing via internet; “New ACIS”.

Fri 22

- Last day for registration payment.

T H E F I R S T S E M E S T E R ( 3 AUGUST - 8 DECEMBER 2015)

August 2016

Mon 1

- Classes begin.

Mon 1 - Fri 5

- Days for late registration processing via internet; “New ACIS”. (Start paying penalty fee with an amount of 50 baht per day including holidays since Mon

1 August 2016).

Mon 1 – Mon 15

- Days to add and change sessions and/or subjects, processing via internet; “New ACIS”.

- Days to request for registration with examination time conflict. (For senior undergraduate students).

Mon 1 Aug –

Tue 30

- Days for late maintain student status.

(Start paying penalty fee with an amount of 50 baht per day including

holidays since

Mon 1 August 2016)

Mon 1Aug –

Fri 16 Sep

- Days to drop, processing via internet; “New ACIS”.

Mon 8Aug –

Mon 16 Sep

- Days for late registration approved by Head of Department, processing via internet; “New ACIS”.

(Start paying penalty fee with an amount of 50 baht per day including holidays since Mon 1 August 2016).

Tue 11

Wai Khru Day, (Classed closed – only undergraduates.)

Mon 15

- Last day to request to transfer courses/credits.

Sat 20

- Compensation classes: Wai Khru Day (Tue 11 August 2016).

Sat 27

- Compensation classes: Her Majesty the Queen’s Birthday (Fri 12 August 2016).

September 2016

Mon 5

-First day for students to evaluate lecturers’ and advisors’ performance (See more details in the New ACIS)

Wed 14

- Last day for late maintain student status1 approved by Dean. (Start

paying pena1 August 2016).

Mon 19 - Tue 27

- Mid-term examination period.

Mon 28 – Tue 1 Nov

- Days for withdrawal with W standing.

October 2016

Mon3 - Fri 14

- Days to submit a graduation request.

Mon 10 Oct

- Fri 11Nov

- Period for students to evaluate lecturers’ & advisors’ performance via

internet; “New ACIS”.

Tue 25

- Last day for registration reimbursement.

Sat 30

- Compensation classes: King Chulalongkorn Memorial Day (Mon 24 October 2016).

November 2016

Mon 7

- Last day to submit the request to maintain student status.

Wed 23 Nov –Fri 2 Dec

- Final examination period.

December 2016

Tue 6

- Classes end.

ACTIVITIES AFTER THE FIRST SEMESTER & BEFORE THE SECOND SEMESTER

December 2016

10 School of Bioresources and Technology

Wed 14

- Last day to submit theory, lab, project and thesis examination results.

Tue 15 – Wed 21

- New graduate students of semester 2 consult with advisors for

registration.

Sat 17

- Examination results announcement.

Sun 18

-Orientation Day for new graduate students

*Mon 19

- Announcement of lecturers’ & advisors’ performance evaluations.

Lecturers and advisors can check the results via the intranet system.

Mon 19 – Fri 23

- Students consult with advisors for registration.

Tue 20 - Sun 25

- Days for registration and tuition payment processing via internet “New

ACIS”.

- Days to maintain student status under approval, processing via internet

Mon 26

- Last day for registration payment.

THE SECOND SEMESTER (4 JANUARY - 22 MAY 2017)

January 2017

Wed 4

- Classes begin.

Mon 4 – Tue 10

- Days for late registration processing via internet; “New ACIS”. (Start paying penalty fee with an amount of 50 baht per day including holidays since Wed 4 January 2017).

Wed 4 – Tue 17

- Days to add and change sessions and/or subjects, processing via internet; “New ACIS”.

- Days to request for registration with examination time conflict. (For

undergraduate students)

Wed 4 Jan – Thu 2 Fed

- Days for late maintain student status.

(Start paying penalty fee with an amount of 50 baht per day including

holidays since Wed 4 January 2017)

Wed 4 Jan – Wed 22 Feb

- Days to drop, processing via internet; “New ACIS”.

Wed 11 Jan – Wed 15 Feb

- Days for late registration approved by Head of Department, processing

via internet; “New ACIS”.

(Start paying penalty fee with an amount of 50 baht per day including

holidays since Wed 4 January 2017)

Wed 18

- Last day to request to transfer courses/credits.

Mon 23

- Last day to submit examination results of semester 1.

Mon 30 Jan – Wed 8 Feb

- Students inform to attend classes in special semester processing via

internet; “New ACIS”.

February 2017

Sat 4

Compensation Classes : Makha Bucha Day

Mon 6

- First day for students to evaluate lecturers’ & advisors’ performance. (See more details in the New ACIS).

Fri 17

- Last day for late maintain student status1 approved by Dean. (Start

paying penalty fee with an amount of 50 baht per day including holidays

since Wed 4 January 2017).

Tue 23 Feb - Fri 3 Mar

- Mid-term examination period.

March 2017

Mon 6 – Fri 31

- Days for withdrawal with W standing via internet; “New ACIS”.

Mon 6 – Fri 17

- Days to submit a graduation request.

Mon 13 Mar - Fri 221 Apr

- Period for students to evaluate lecturers’ & advisors’ performance via

internet; “New ACIS”.

Fri 31

-Last day for registration reimbursement

April 2017

Sat 1

-Compensation Classes : Chakri Memorial Day( Tue 6 April 2017)

Mon 10–Wed 12

- Special Vacations.

Sat 29

-Compensation Classes : Coronation Day ( Fri 5 May 2017)

May 2017

Mon 8 - Fri 19

- Final examination period.

Mon 22

- Classes end.

ACTIVITIES AFTER THE SECOND SEMESTER & BEFORE SPECIAL SEMESTER

May 2017

11 School of Bioresources and Technology

Mon 22 – Fri 26

- Students consult with advisors for registration.

- Days for registration and tuition payment processing via

internet; “New ACIS”.

Mon 29

- Last day for registration payment.

Wed 31

- Last day to submit theory, lab, project and thesis examination results.

June 2017

Sat 3

- Examination results announcement.

*Mon 5

- Announcement of lectures’ & advisors’s performance evaluates.Lectures and advisors can check the results via the intranet system.

S P E C I A L S E M E S T E R (5 June – 28 July, 2017)

June 2017

Thu 1 Jun – Fri 28 Jul

- Practical training period, (not less than 40 working days).

Mon5

- Classes begin

Mon 5 - Fri 9

- Days for late registration approved by Head of Department, processing

via internet; “New ACIS”.

(Start paying penalty fee with an amount of 50 baht per day including

holidays since Mon 5 June 2017)

Mon 5 - Fri 16

- Days to add,drop and change session and /or subjects,processing via

internet; “New ACIS”

-Days for withdrawal with W standing via internet;”New ACIS”

Mon 12 - Fri 16

-Days to submit a graduation request.

Fri 16

- Last day to submit examination results of semester 2.

Mon 19 - Fri 30

-Period for students to evaluate lectures’ & advisors’ performance via internet “New ACIS”

Fri 23

- Last day for registration reimbursement.

July 2017

Fri 28

- Classes end.

ACTIVITIES AFTER SPECIAL SEMESTER

August 2017

Fri 4

- Last day to submit theory, lab, project and thesis examination results.

Tue 8

- Examination results announcement.

Thu 10

- Announcement of lecturers’ & advisors’ performance evaluations. Lecturers and advisors can check the results via the intranet system.

Thu 24

- Last day to submit examination results of special semester S/2016 -Last day to submit practical training results.

Remarks : 

1. Students must register, add, change, drop and withdraw via internet; “New ACIS”.

2. Students who owe tuition fees cannot register in the subsequent semester.

3. Probation students in semester 2/2016 must consult with their advisors about registration during the specified period. Without doing so, they will not allow to register in semester 1/2016

4. Probation students in semester 1/2016 must consult with their advisors about registration during the specified period. Without doing so, they will not allow to register in semester 2/2016

5. Students must pay tuition fees before add, change and drop courses.

6. Students who want to take leave in the next semester have to ask for the faculty’s approval and submit a request in order to be able to maintain student status via internet.

7. Students who do not register or maintain student status by identified date must pay penalty fee with an

amount of 50 baht per day including holidays.

-The first semester, start paying penalty fee from Monday 1 August 2016.

12 School of Bioresources and Technology

-The second semester, start paying penalty fee from Wednesday 4 January 2017.

8. Students who want to transfer courses or credits must request during the specified period

-The first semester, until Friday 15 August 2016.

-The second semester, until Wednesday 18 January 2017.

9. Students who want to reimburse for tuition fees must request during the specified period.

-The first semester, until Tuesday 25 October 2016.

-The second semester, until Friday 23 June 2017.

SBT’S Activities

Activities

Date

1. 

Wai Khru Ceremony

25 August

2. 

Student’s Club

Monthly

(Date to be announced)

3. 

New Year Party & Sports Day

December – January

13 School of Bioresources and Technology

EDUCATION FEES

1.1 Education fees to be collected in each academic year;

1.1.1 Health and life insurance 2,000 THB

1.2 Education fees to be collected in each semester;

1.2.1 Education Service Fee

Regular semester 30,000 THB per semester

Summer Session 15,000 THB per session

1.2.2 Credit fees for coursework/independent study/thesis

Social and Humanities

Regular semester 3,000 THB per credit

Summer session 6,000 THB per credit

1.2.3 Credit fees for coursework/independent study/thesis

Social and Humanities

Regular semester 4,000 THB per credit

Summer session 8,000 THB per credit

1.2.4 If credit fees for coursework/independent study/thesis for full fee programs are higher

than the credit fees as in item 1.2.2 and 1.2.3, the rates of full fee programs shall be paid.

1.3 Education fees to be collected on variable occasions;

1.3.1 The registration for a thesis examination or its equivalent paper

From 6 credits to not over 12 credits 10,000 THB

From 12 credits to not over 36 credits 15,000 THB

Over 36 credits 20,000 THB

      Education fees and credit fees for coursework/independent study/thesis as in the above mentioned rate have been enacted for international students who study non-degree courses, except for exchange students under a cooperative agreement with the university.

      Students, who must study fundamental courses as required by the university, must pay education fees based on this regulation.

      Students who complete all coursework as the program requires, but still work on experiments for a thesis or equivalent paper in the next regular semester or summer session, must pay credit fees for the thesis or equivalent paper – including education fees based on items 1.1-1.2 of this regulation.

14 School of Bioresources and Technology

      Students must pay education fees within the period set by the University. The University will not remit money except when courses are cancelled by the University.

      Rules and regulations according to this regulation are based on an announcement by the University.

      The President shall govern the provisions of this regulation. The judgment of the President is considered final.

15 School of Bioresources and Technology

RULES AND REGULATIONS

Enrollment

      Students accepted to study under an announcement of the university must indicate their intent to enroll and submit in full enrollment documents by a specific date and time. If not, students will be assumed to have declined the study.

Registration

1.1 Registration and study periods

      1.1.1 The registration for coursework in graduate studies and/or thesis is for not less than 6 credits and not more than 15 credits in each semester, except students who have less than 6 credits remaining for registration based on the program structure and are, therefore, allowed to register for less than 6 credits. For summer semesters, students can register for coursework and/or thesis of not more than 6 credits.

    1.1.2 Students who do not follow the registration schedule cannot register in that semester, except if they have sufficient reasons and are specially approved by the faculty to register or to maintain student status.

1.1.3 The study periods of each program are as follows;

o Graduate diploma programs take not more than 3 academic years

o Master’s degree programs take not more than 5 academic yearsDoctoral degree programs take not more than 8 academic years for bachelor degree holders and 5 academic years for master’s degree holders

1.2 Registration offered by other institutions

    1.2.1 Students may register for coursework offered by other institutions under the consent of the department and the approval of the faculty under the following conditions;

(1) The coursework required by the program is not offered by the university in that semester or that academic year for whatever reasons.

(2) The coursework offered in other institutions must be comparable to the areas of coursework offered in the program.

(3) The coursework has to be advantageous to the study or thesis work or independent study of the students.

16 School of Bioresources and Technology

1.2.2 Credits and grade results can be transferred to students’ grade accumulation in the program.

Adding, changing, dropping or withdrawing coursework

Approval from advisors must be obtained prior to adding, changing or dropping. Approval from advisors and instructors must be obtained prior to withdrawing under the following conditions;

1.1 Adding and changing can be done during the first two weeks from the start of each semester.

1.2 Dropping can be done during the first three weeks from the start of each semester.

1.3 Withdrawing can be done until three weeks before the final examination of that course. A “W” will appear on the student’s transcript for that course.

1.4 Adding, changing, dropping or withdrawing cannot be done in summer semesters.

Student absence

1.1 Students will be allowed to take leave under one of the following conditions;

1.1.1 Military recruitment

1.1.2 Other sufficient reasons for which student gain approval to take leave.

1.2 Students can make one leave request per semester and for not longer than two semesters consecutively or one academic year, except with special approval.

1.4 Study absence must be approved by the faculty.

1.5 Education fee payment

1.5.1 Students who take leave before registration are exempt from paying education fees, but they must pay fee for maintaining their student status.

1.5.2 The university will not remit to students who have already registered, but want to take leave at a later date.

1.5.3 For permission for study absence, students who have requested installments of education fee payment must pay the education fee as well.

Termination of student status

Student status will come to an end based on the following conditions;

17 School of Bioresources and Technology

1.Non-registration and/or payment of education fees

    Student status will be ended if students do not register and/or pay education fees during the first six weeks of instruction based on an announcement by the university.

2. Passing due study periods

    Student status will be ended for those who cannot finish the study within the indicated study periods of each program

    Doctoral degree students Plan 1 or Plan 2 who completed earned coursework with a GPA of not less than 3.25, but have passed the due date for study periods with uncompleted thesis and/or academic publication process, can submit a request for study extension per semester under the conditions that not less than 80 percent of thesis must be evaluated, pass the consent of the faculty committee, and be approved by the Academic Council of the university.

3. GPA under criteria

3.1 Student status of regular students will be terminated under the following conditions;

Doctoral degree students have a GPA of less than 2.75 in the first semester

Master’s degree and graduate diploma degree students have a GPA of less than 2.50 in the first semester

3.2 In any semesters, regular students will be on probation under the following conditions;

Doctoral degree students have a GPA of less than 3.25.

Master’s degree and graduate diploma degree students have a GPA of less than 3.00

Student status will be ended during probation if doctoral degree students have a GPAX of less than 3.25, and master’s degree and graduate diploma degree students have a GPAX of less than 3.00.

18 School of Bioresources and Technology

Steps for completing thesis

Conduct of thesis

1. Students must receive approval from advisors to register thesis under the following conditions;

    1.1 Master’s degree students Plan A (2) and doctoral degree students Plan 2 with a bachelor degree can register thesis after being regular students for at least one semester. In addition, they must have passed registered courses of not less than 6 credits and have a GPA of not less than 3.00 for a master’s degree and 3.25 for a doctoral degree.In other cases, the registration for thesis is subject to the consent of the thesis advisor.

    1.2Students can divide the number of credits for thesis registration with the consent of their thesis advisor

    1.3 Students who completely register for all coursework as per the program requirements, but do not finish their thesis, which has to be continued, and is also expected to be finished in the summer session, must register a thesis for that summer; otherwise the faculty will not approve the study results, and the return for registration will not be allowed.

2. Thesis proposal presentation

    2.1 After students register their thesis, they must present their thesis proposal to thesis advisors for editing, and to the department for approval.

    2.2 The department will present the thesis proposal including a list of thesis committee members to the faculty to ask for the approval of the thesis title and appointment to the thesis committee.

3. Defense for thesis proposal and thesis evaluation

    3.1 Students must pass the defense for thesis proposal, and submit the thesis progress report to the thesis committee every semester.

    3.2 The thesis committee will evaluate a conduct of thesis based on the number of credits that students register in each semester. An “S” grade will be given for the credits of research performance which is considered to be satisfactory. A “U” grade will be given for research performance which has no progress. Students who completely defend and submit their thesis will be given an “S” grade based on the credits of thesis registration.

19 School of Bioresources and Technology

    3.3Students who register their thesis, but do not consistently work on the thesis for two consecutive semesters will be given a “U” grade. The thesis committee may propose to the department to end student status from that current thesis with the approval of the faculty.

4. Changes of thesis title and the number of credits for thesis

    4.1 In case of academic problems or emergency, the changes of thesis title and/or the number of credits for thesis can be made with the consent of the thesis committee. Students must submit a request for change of thesis title enclosing a new thesis proposal. This must pass through the discretion of the thesis committee and gain the consent of the department to ask for approval from the faculty.

    4.2 Students who change thesis proposal must renew registration and payment for that new thesis.

Thesis defense examination

1. Students will be qualified to take a thesis defense examination as follows; 1.1 Be approved by thesis committee to take the thesis defense examination

   1.2 Submit thesis books to thesis examination committee for reading at least two weeks before the date of thesis defense examination

2. The chairman of the thesis committee will propose a list to the thesis examination committee with an examination schedule to ask for approval and an appointment with the faculty.

3. The thesis examination committee has responsibility for an examination. An “S” grade will be given for an examination performance which is considered to be satisfactory. A reexamination on the date set by the thesis examination committee will be given for any examination performance which is considered to be unsatisfactory.

4. Students must submit the complete thesis book which is produced in line with the Thesis Manual of the university plus an electronic version of the thesis based on an announcement by the university within 30 days of passing the examination. If minor contents have to be corrected and the student will miss the deadline, the thesis examination committee may consider allowing the thesis book to be sent after 30 days of passing the examination, but it must be sent within 60 days; otherwise, the thesis defense examination must be renewed.

20 School of Bioresources and Technology

5. Thesis books of a doctoral degree should be written in English unless it has been approved to be written in other languages by the thesis examination committee.

6. The copyright of the thesis belongs to the university.

Graduation 

Students will receive a certificate or a degree from the university if they fulfill the following requirements;

1. Graduate diploma degree students

Students must complete all coursework as per the program requirements with a GPA of not lower than3.00.

2. Master’s degree students

  2.1 Students Plan A: A (1) must present a thesis and pass an oral examination. At least one full paper must be accepted for publication in an international journal.

  2.2 Students Plan A: A (2) must complete all coursework as per the program requirements with a GPA of not lower than 3.00. They must present a thesis, pass an oral examination, and have other publications which are not in the thesis paper.

    2.3 Students Plan B must complete all coursework as per the program requirements with a GPA of not lower than 3.00 plus;

(A) Present an independent study and pass a comprehensive examination or

(B) Present an independent study and pass an oral examination

    2.4 Students must meet the English language proficiency requirements. A department may consider foreign language certificates from other institutions as being acceptable for this requirement. For master’s degree students in the field of Applied Linguistics, the department will consider the conditions of passing of foreign language assessment.

3. Doctoral degree students

    3.1 Students in Plan B must complete all coursework as per the program requirements with a GPA of not lower than 3.25.

3.2 Students pass a qualifying examination as follows;

    (1) Bachelor degree holders must pass a qualifying examination within 4 semesters after the first enrollment.

21 School of Bioresources and Technology

(2) Master’s degree holders must pass a qualifying examination within 3 semesters after the first enrollment.

(3) Two attempts for a qualifying examination within the set date of items 3.2(1) 3.2(2) can be made.

3.3 Students must present a thesis which expresses a new discovery, new idea, creative ideas or critique with original ideas with the following conditions;

(1) Not less than two pieces of Full Paper must be accepted for publication in an

international journal where its standard database with referee can be searched, or

(2) Not less than one piece of Full Paper must be accepted for publication in an

international journal where its standard database with referee can be searched, and

A. Not less than two pieces of Full Paper must be accepted for publication in a regional journal or a national journal with referee, or

B. Not less than two pieces of paper must be accepted for presenting in an international conference with oral presentation and full publication in proceedings

C. Not less than one piece of Full Paper must be accepted for publication in a regional journal or a national journal with referee, and not less than one piece of paper must be accepted for presenting in an international conference with oral presentation and full publication in proceedings, or

(3) Other equivalent performance such as goods with patent, innovations, creative

designs or a masterpiece which can be used for business or non-business purposes

3.4 Students must pass an oral presentation based on their thesis as in item 3.3 3.5 Students must meet the foreign language proficiency requirements.

Doctoral degree students must have a good command of a foreign language by passing one foreign language exam. Other additional foreign languages may be required under the discretion of each field of study. The above required language proficiency will not be counted in credits.

22 School of Bioresources and Technology

SCHOLARSHIP FOR INTERNATIONAL STUDENTS

   * Greater Mekong Sub-region: GMS

   * ASEA –UNINET (ASEAN-European Academic University Network)

   * Sahapunya scholarship (For students from Cambodia, Laos, Myanmar and Vietnam) For more information, please download at http://web.kmutt.ac.th/sbt/scholarship.php 

GENERAL FORMS FOR EDUCATION

Please download at http://web.kmutt.ac.th/sbt/download.php

23 School of Bioresources and Technology

LABORATORY IN SCHOOL OF BIORESOURCES AND TECHNOLOGY

Our School of Bioresources and Technology have 21 Laboratories:

Biotechnology Program

1. Algal Biotechnology

2. Animal Cell Culture

3. Solid State Fermentation and Bioprocess Engineering

4. Microbial Fermentation Technology

5. Remediation

6. Sensors Technology

7. Fungal Biotechnology

8. Waste Utilization and Management

9. Biodiversity

Natural Resource Management Program

10. Community Resources Management

11. Conservation Ecology

Postharvest Technology Program

12. Postharvest Biochemistry and Molecular Biology

13. Postharvest Physiology and Pathology

14. Postharvest Quality and Logistic

15. Seed and Grain Technology

Biochemical Technology Program

16. Carbohydrate Technology

17. Enzyme Technology

18. Phytobioactives and Flavor

19. Lipid Technology

20. Gene Technology

Bioinformatics and System Biology Program

21. System Biology and Bioinformatics

24 School of Bioresources and Technology

OFFICE OF DEAN, EMERGENCY CALL &FACILITIES

Name – Surname

Position

Room

Telephone

Responsibilities

Mrs.Nutrada Keawgramloy

Personal

BT 246

7702

* Information, News and

activities to be announced

Ms.Thunyasiri Junnim Mr.Pornchai Cheysanium

Service

BT 246

7703

* Booking classroom and meeting

Mr.Pongthorn Chukam

Building and Ground

BT 246

7704

* Class room and meeting facilities (LCD,Visualizer,computer, Notebook,Sound equipments)

* Request for Building Keycard

* Request for room using after working hours

* Request for Car Sticker

Ms. Aroon Kakhun

Research Support

BT 247

7706

* Outsource Scholarships, e.g.,

King Mongkut’s Diamond Scholarship, The Royal Bangkok Sports Club (RBSC) Scholarship,The Shell Centenary Scholarship Fund (TSCSF)

* Thesis borrowing

* Announcing and

informing about Academic service,Research

Ms. Rungarun Waisayawan

Academic Service

BT 247

7705

* All academic-relevant helps

* Student request forms, e.g., delayed registration,postpone the tuition fee payment, dropping off; Academic information; Proposal examination ;Thesis progression; Thesis defense; Seminar;scholarships

Ms. Pakpinun Thaninsukpisan

Financial

BT 249

7707

* Financial service

25 School of Bioresources and Technology

In case of emergency, call 7399

* Call 7700 for School of Bioresources and Technology Building

* Call 7384 for Central security

Health Service Section (Bangkhunthian) 1st Floor, SBT Building

* Opening hours: Monday - Friday, 9.00 a.m. to 5.00 p.m.

* Treatment by doctors: Monday Wednesday and Friday, 13.00 to15.00 p.m. / Tuesday 10.30a.m. to13.30 p.m. / Thursday 12.30a.m. to14.30 p.m. Library(Bangkhunthian) 2nd School of Architecture and Design

* Opening hours:Monday- Friday, 9.00 a.m. to 5.00 p.m. Saturday, 9.30 a.m. to 4.00 p.m.

26 School of Bioresources and Technology

Volume 2

Page 1

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????????????????????????????????????????????????

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Abigail Barrow, PhD, is the director of the Massachusetts Technology Transfer Center in Boston, Massachusetts. La Royce Batchelor is a professor at the University of North Dakota and a startup coach for the Center of Innovation in Grand Forks. Alex Breger, JD, is project manager at the National Collegiate Inventor and Innovators Alliance in Springfield, Massachusetts. Nathalie Duval-Couetil, PhD, MBA, is an associate professor at Purdue University in West Lafayette, Indiana. Latanya Scott is a licensing associate at H. Lee Moffitt Cancer Center and Research Institute in Tampa, Florida. Jeffrey Skinner, PhD, MBA, RTTP, is the executive director of the London Business School in London, England. Phyl Speser, JD, PhD, RTTP, is CEO of Foresight Science and Technology in Comptche, California. Phil Weilerstein is the director of National Collegiate Inventor and Innovators Alliance in Springfield, Massachusetts.

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An understanding of intellectual property (IP) is an important skill set in today’s increasingly dynamic, information-based economy. Awareness is especially important at academic institutions where many of society’s brightest students first learn about and begin practicing innovation and entrepreneurship. Accordingly, university community members—including students, faculty, alumni, and administrators—should all have reasonable access to IP literacy. For students, this necessarily involves gaining an understanding of their institutional IP policy and how it affects their potential rights and obligations.

While the main purpose of a university’s interaction with students is in the delivery of education, there are times when these students develop intellectual property. These inventions can occur, for example, when students are working on entrepreneurship projects, when they are working in the lab as part of a research experience, or during industry-sponsored Capstone projects. In some cases these inventions have real value,

©2014 Association of University Technology Managers and chapter authors named above. All rights reserved.

Reproduction in whole or in part without written consent of the copyright owners is prohibited. Contact AUTM

regarding reuse of any part of this work. Opinions expressed in this publication by authors are their own and do

not necessarily reflect the opinions of AUTM or the organizations with whom the authors are affiliated. Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

and there are many examples of student activity—including that of undergraduates— resulting in the formation of viable businesses. Unlike faculty and graduate researchers whose contractual relationship with an institution tends be quite formalized,

undergraduates and masters students are not generally regarded as being employed by their university in the traditional sense. Accordingly, student-generated IP lies outside of the clear-cut employment context and raises a unique set of issues concerning ownership and other IP-related rights.

Depending on the policy of the university, newly generated student IP may be construed as belonging to either the institution or the student. In general, IP laws in each country— particularly those whose legal systems are rooted in English Common Law—grant default IP ownership rights to the inventor or author unless he or she knowingly agreed otherwise. For there to be a legally binding contract, there must also be consideration. That is, the university must give something in exchange for the student’s rights to his or her invention. Thus university IP policy, when it comes to students, needs to be carefully thought out, clearly worded, widely disseminated, and fair.

1 According to a recent study, “There is a lack of consensus among institutions on how to manage IP generated by undergraduates.” Not surprisingly, the policy that a given university implements tends to reflect its individual institutional priorities. For example, institutions trying to promote income may implement policies asserting broad ownership over most, if not virtually all, student IP. If this is the case, the institution needs to ensure that it invests in sufficient resources for making students aware of its IP policy and managing that IP to minimize risk and ownership disputes. In contrast, institutions less focused on revenue generation may assert little to no ownership over student IP at all.

This primer is not intended to prescribe how and when a university should claim

ownership of student IP. Rather, the intention is to raise awareness of the key issues and decision points involved in the process. The remaining sections of this chapter will discuss and consider the major issues that an institution ought to consider in the course of developing and promulgating a comprehensive student IP policy that is efficient and consistent with institutional objectives.

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

As part of preparing this document, the authors have sought out and collected various IP policies from around the United States, Canada, and Great Britain to help consider the range of best practices that make for a fair and comprehensive student IP policy. (Some examples of student IP policies can be found at autm.net/policies.) As there is no single best answer, this chapter presents what the Student IP Policy Task Force learned through a set of accompanying scenarios that university Technology Transfer Offices (TTPs) may encounter in practice. (See the appendix.)

The appendix highlights what considerations might apply in these scenarios to help the institutions come to their own conclusions.

?????????????????

Who Is a Student?

For the purposes of this document, a student is considered to be any individual registered in university courses who anticipates earning a degree, diploma, or certificate. He or she may be undergraduate (e.g., BS, BA) or postgraduate (e.g., MA, MSc, MBA, PhD). Some may also operate as employees of the university, while others may not. Analyzing the distinction between students enrolled in programs and courses that are primarily teaching-based versus those that are fundamentally research-based is a key point analyzed more thoroughly throughout the chapter.

The Need for a Specifically Enumerated Student IP Policy

Student involvement in institutional research activities is the most frequent context in which potentially destructive IP ownership issues tend to arise. Universities have an obligation to inject clarity into how their policies address student research participation. The worst outcome for both parties is the emergence of an IP stalemate—with neither the student nor the university feeling confident that they possess sufficient rights for pursuing commercialization. Under these circumstances, the IP and its associated value can diminish or even languish entirely before either party is able to capitalize. This outcome is economically inefficient and potentially risky for the university if, for example, the ownership conflict interferes with its legal obligation for facilitating the national patenting process.2

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

??????????

There are seven key issues that every institution should consider when developing and implementing an institutional IP policy or set of bylaws.

Institutional Objectives

* All universities pursue multiple missions and strategic objectives. The weight that a particular institution attaches to particular objectives relative to others will affect how it structures its student IP policy. For example, does the university care more about optimizing revenue (including income from the appropriation of IP) or innovation in general (including the resulting socioeconomic benefits)? This fundamental institutional priority necessarily factors into the school’s IP policy calculus and is perhaps best-addressed explicitly rather than left to implication. Of course, these two goals are not mutually exclusive, and a well-implemented policy can promote the spread of innovation and help maximize institutional revenue.

* Revenue issues aside, is the university worried about missing out on particularly novel, high-profile, or prestigious inventions? In other words, does the fear of loss outweigh the need for gain? For example, what if there is a big winner and the university (or its officers) are blamed for not having negotiated effectively? The university wants to maintain its appearance as an attractive place for budding entrepreneurs, while simultaneously implementing a policy that minimizes its chances of losing out on especially valuable IP.

Significant-Use Criteria

* To what extent did student work resulting in the creation of new IP involve significant use of university resources? In this instance, significant use means economic

rather than intellectual input, such as use of university facilities, support staff, and consumables. As a mainstay feature of many, if not most, existing institutional IP policies, significant-use language is problematic. It is also legally problematic that there are no known high-court decisions clearly defining this term and its significance with respect to student IP. For example, an inventive contribution may be highly significant in the traditional sense of the word, even though the accompanying use of university resources results in negligible marginal cost to the university. For a more detailed

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

examination of the specific factors and decision points involved in significant-use analysis, refer to the appendix, which discusses student IP scenarios.

Role of Existing Research Programs

* Does the new IP build upon the university’s expertise and technology? This brings

up issues of coownership and the terms on which the university’s research staff and

faculty “gave” the student access to proprietary information and/or provided guidance.

* Does the university wish to subsequently license the IP to a third party? If so, it may want to ensure that it has unencumbered rights to the entire package. Without proper handling from the outset, this particular problem is often exacerbated by the fact that licensing opportunities can arise years later, after the student has already left the institution.

* Is the IP arising from the project already encumbered? For example, does it fall into the definition of foreground under a third-party sponsored research project? In some ways this is the easiest situation to cope with since students can—and often are— legitimately asked to waive IP rights in exchange for the opportunity to work on a Capstone or similar project. (The alternative being a more abstract project with less-direct or formalized access to third-party resources.)

Contractual Enforceability

* What is the likelihood that a university could successfully enforce its asserted IP rights against a student? Although many nations have enacted laws encouraging universities to assert ownership over IP tied to federal funding—such as the Bayh-Dole Act in the United States—the separate issue of contractual enforceability should not be overlooked.3 An ambiguously written or substantively Draconian clause resulting in an ownership dispute could be construed in favor of the less-sophisticated party, which is virtually always the student in this case.4 Language involved in the definition of significant use is a particularly sensitive area and should be vetted carefully for enforceability.

* Does the institution’s IP policy—or the manner in which it is presented or implemented—come off as coercing or duping unsophisticated students into signing away their rights? For example, by their agreeing as a default condition of enrollment to be bound cumulatively by an assortment of institutional terms contained within a

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

single student handbook. It is important not to interpret previous authority supporting institutional administrative policies in general as a rubber stamp of approval over all university IP clauses. With students unlikely to regard academic enrollment as contractual and unable to meaningfully negotiate the terms involved, any ambiguous or unduly burdensome IP provisions risk invalidation or severance and, therefore, pose a risk to the university.

Former Students and Alumni Relations

* The extent to which a university enforces IP rights under old contracts with former students is another important strategic consideration, particularly in the context of alumni relations. Institutions should consider who the former student is and how well-known his or her association is with this invention amongst members of the university community. A university that binds itself into claiming an especially popular or high-profile invention (particularly one that is already viewed as belonging to a particular alumnus) risks potentially harming alumni revenue without generating enough additional licensing revenue to offset the loss.

* Self-limiting the duration of its rights over former students and proactively reaching out to student inventors while they are still enrolled to assess the status of ownership are strategies that institutions can explore to anticipate and mitigate potential alumni issues.

Administrative Overhead

* Does the institution allocate sufficient resources for enforcing compliance with its stated IP policy? Many universities rely exclusively on officers and faculty for self-enforcement of the school’s IP policy. Compliance is likely to suffer unless these procedures are clearly articulated and well-understood by the various parties involved—faculty and students especially.

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

General Strictness: Balancing Research Quality Versus Quantity

* Finally, the balance between maintaining strict IP rules and preserving the quality and depth of institutional research is a critical, overarching concern. An overly strict policy by itself may stifle interaction and sharing between student and research personnel— diminishing the richness and relevance of research projects across the board.

Ironically, in this case the stricter policy—intended to broaden university IP ownership rights—may instead have the opposite effect of harming institutional licensing revenue via a loss in research quality.

??????????????????????????????

When a matriculated individual develops IP on campus, the university must determine whether the person should be treated as a student or as a researcher under the school’s IP policy in that particular instance. This important factual determination is not always as clear-cut as it might initially appear. For example, an undergraduate student might choose to participate in an independent study drawing upon and interfacing with an existing institutional research program. Likewise, a graduate researcher being paid primarily for his or her work in the lab might still be enrolled in traditional academic courses pursuant to his or her doctoral program. Therefore, a proper determination of student status cannot simply depend on the general enrollment status and must necessarily account for the facts of each scenario on a case-by-case basis.

Most institutions focus this analysis on the type of resources the individual accessed in the course of developing the IP at issue. Typically, the distinction is drawn between resources that are teaching- or study-based versus those that are primarily research- or industry-based. Teaching resources include all forms of instructional course content, course references and study materials, personalized instruction provided outside of normal class time, and so on. Once presented to students, the use and derivation rights in all teaching resources are transferred into the public domain, allowing students free access without raising significant IP concerns.5

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Determining student status can be especially difficult in the following three teaching contexts.

* Project work: A student engages in an independent project that substantively involves research but has little to no interaction with existing researchers and research

programs. Should this person be treated as a researcher under the school’s IP policy? See Scenario 3 in the appendix for a more-thorough discussion of the key factors and decision points involved in this type of scenario.

* Extracurricular activity: A student engages in an extracurricular venture entirely outside of his or her defined curriculum but makes incidental use of significant university research resources without formally engaging the university or understanding the consequences. Again, how should this person be considered under the institutional IP policy? See Scenario 1 and Scenario 3 in the appendix for additional discussion of issues likely to arise in this type of situation.

* Mandatory coursework: Especially in upper-level undergraduate classes and graduate programs, students are expected to do independent research as part of the educational process. As this research is a requirement for graduation, it is not clear how the significant-use concept applies. Again, significant use is discussed further in the appendix.

In contrast, research-based resources include all embedded programs and projects specifically structured and geared toward producing novel data and concomitant IP. Universities tend to actively pursue ownership, protection, and commercialization of research-generated IP, and researchers are less likely to retain any ownership interests regardless of their level of involvement or use of resources.

Furthermore, even if a school’s policy formally designates a participating researcher as a student in some instance, the student’s use of lab and other research equipment could very well trigger the school’s significant-use provision—thereby leading the university to assert that the student has relinquished his or her ownership rights anyway.

Conceptually, the types of IP issues surrounding graduate researchers are no different from those of undergraduate students carrying out research projects. In practice, however:

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

* IP issues in the graduate context are more likely to arise simply because of the duration of the project, the access granted to such students, and the amount of information—both tacit and codified—exchanged as the student becomes a trusted and integral member of a research team.

* Graduates tend to be older and more mature, making them more likely to understand the complex legal and commercial interests attached to their research. Specifically, this enables them to better comprehend and anticipate IP issues and make tradeoffs between various projects with different IP structures, etc.

* The graduate admissions process and dialogue at many institutions is more likely to address the applicable field of research and associated IP issues directly. Graduates are therefore more likely to understand complex IP arrangements, and—from a contractual standpoint—reach a meeting of the minds with the university.

For these reasons, universities often implement distinct IP policies applicable to research students (usually doctoral degrees) versus those enrolled in teaching programs (usually undergraduate and master’s degrees). Essentially, it is the nature of the activity, the involvement of the university—both via physical and intellectual resources—and the embeddedness of the research project that should be material, not simply whether the student is being paid by the university in some employment capacity.

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Preexisting Student Intellectual Property

When a student enters the university with preexisting IP, he or she should be encouraged to disclose its existence before any further research or development work is undertaken using university resources. If the invention was not disclosed before additional

development work is undertaken, then the student may be asked to show evidence of when and where the invention was made.

Capstone Design Courses

One area where student IP issues are often more prevalent is in the context of Capstone design projects. These projects typically involve collaborating with a private industry sponsor, granting the student valuable access to industry resources, but consequently inserting an additional party into the IP equation beyond just the student and the

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

university. Since Capstone projects more closely resemble the type of inventive work undertaken in actual industry settings, they are significantly more likely to result in the generation of commercially valuable IP than traditional undergraduate coursework. While many institutions have already enumerated an IP policy specifically addressing Capstone projects, the need for continued university awareness and leadership in this area is critical. Similar forms of independent study—such as service learning, senior theses, dissertations, etc.—can raise comparable IP issues and are discussed in the next section.

Capstone project structure and administration can vary significantly from institution to institution—creating a wide range of potential project scenarios and making it difficult to articulate a singular, uniform policy. The set of issues involved is perhaps best-understood in terms of two extremes. On one hand there is the classic Capstone scenario: a formalized arrangement in which students actively partner with an industry sponsor for a sustained period of time, using significant sponsor resources, and eventually producing a deliverable tied to actual industry products and services.

In the classic scenario, universities usually offer, and sponsors typically expect,

unencumbered ownership over any resulting IP in return for their contributions to the project. Given the significance of the legal interests involved, universities offering these sorts of sponsored Capstone projects need—and in many cases already have—dedicated policies formalizing the respective rights between student, sponsor, and institution.

Service Learning Projects and Other Independent Studies

In addition to Capstone projects, institutions are also increasingly offering science, technology, engineering, and mathematics students the ability to participate in

experiential education through community-oriented service learning and other hands-on projects. Unlike Capstone education—which tends to be quite formalized and industry-oriented—service learning occupies the other end of the spectrum, with projects varying greatly in scope and structure depending on the institution. Students may seek out some degree of collaboration with a third party or use varying degrees of industry or university resources, but typically these projects culminate in a purely academic report or evaluation as opposed to an industry-tied deliverable.

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Service learning projects raise threshold issues of: (1) how the project should be

categorized under the school’s IP policy and (2) whether the enumerated category sufficiently addresses the types of IP issues likely to arise in that project context. Such projects can take on numerous forms in which the student may or may not be working alongside other researchers, receive academic credit for the project, or develop a working relationship with an industry member or other third party. University technology transfer offices have a professional duty of care to ensure there is clear understanding concerning the presence or lack of institutional involvement in these types of relationships.

For example, a senior thesis in computer science might not engage any formal sponsorships—distinguishing it from the usual Capstone project and tempting the university to treat it as generic student IP. Suppose, however, that the same project implicates a host of copyright issues that neither the student nor the university have specifically contemplated or previously addressed in the technology transfer context. Without a project-oriented IP policy in place, ironing out the legal details for every iteration of student project on a case-by-case basis would be prohibitively expensive for most technology transfer offices.

Furthermore, with less than 30 percent of surveyed institutions promulgating IP handbooks or similar material to their students—an increase in university-led outreach and education apprising students of their IP rights is one area that could enhance student involvement in the technology transfer process at relatively low cost.6 This approach is discussed in greater detail in the following section.

Makerspaces

Many universities and colleges are creating and investing significant resources in the creation of Makerspaces. These facilities contain equipment (including 3D printers, laser cutters, and, in some cases, machine tools), workbenches, and a supply of materials to enable students to design and build small project prototypes and models that may or may not be part of their formal educational curricula. Access to these spaces is usually made available to all students, staff, and faculty at the institution and sometimes to students of other local institutions and members of the community as well.

©2014 Association of University Technology Managers Effective August 2014

Volume 2

Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

In many, but not all cases, institutions have a clear policy that any IP developed within the Makerspace belongs to the inventors and not to the institution. As with many other policies, it is up to an individual institution to decide on the specific IP policy for these activities and ensure that participants are aware of the policy.

Similar concerns exist with respect to other student-focused entrepreneurship programs, such as student incubator and accelerator programs where new inventions may be discovered as part of a student’s engagement. Again the tendency at institutions has been not to claim any IP ownership, but whatever the decision has been on IP ownership it is important to have a clear policy that is well-publicized.

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If students are not familiar with their institution’s IP policy yet voluntarily consent to it anyway, the policy risks not being fully legally binding on the student. To minimize this risk, effective student outreach is critical. Effective outreach in the student IP context has two key components: (1) spreading substantive awareness about the policy in general and (2) obtaining and documenting informed consent from students—especially those likely to be involved in IP-generating activities.

There are many mediums for available for disseminating a student IP policy including:

* posting it

* on school or departmental websites

* in newspapers and other publications

* on departmental and dorm bulletin boards

* having it read

* by faculty in classes

* by administrators during welcome and other periodic check-in events

* on the radio or TV on campus stations

* in student governance and club sessions

* making it required reading

* in student orientation booklets

* in handouts for classes that include research projects

* in consent forms for participating in internships, co-ops, lab and teaching assistantships, fellowships, and other work or practical experiences programs

©2014 Association of University Technology Managers Effective August 2014

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Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

This list is by no means exhaustive.

As with any outreach effort, placing the message in communication channels already monitored by the targeted audience is helpful and usually most-effective. The structure of these channels will vary from campus to campus, making it important for each institution to conduct its own independent analysis. Obviously, it helps to make the informed consent process as easy and inviting to students as possible.

Both web- and print-based documents can serve as integral components of an ideal outreach strategy. For example, when students register for classes, the student IP policy could pop up similar to an end-user licensing agreement, which must be read and clicked on before the student navigates away from the page.7 In addition, faculty could be asked to reference the policy in their syllabi and indicate how students can access it more fully.

For students who are also research or teaching assistants, IP policies are often embedded as a clause in their employment contract or a signed addendum. From the university perspective, these students may be treated no differently than any other employee being asked to surrender prospective IP rights in exchange for employment, and the significance of what they are purportedly agreeing to may not be made clear to the student at the time of signing. Structuring the student IP policy similarly to the faculty/employee IP policy is one way to increase faculty outreach—as faculty members will be in a better position to advise students on IP issues using their knowledge of the policy. However, institutions should take care that IP clauses in student employment contracts are sufficiently

conspicuous or else students may not know to approach faculty for guidance in the first place.

One issue with traditional media is the lack of bidirectional, question-and-answer type dialogue. Thus, a periodic seminar on commercialization of student inventions at the beginning of each semester, quarter, and so on, could be another valuable outreach tool. A repeat forum of this sort helps ensure that those who are most likely to encounter IP issues have an opportunity to contemplate and ask clarifying questions about the policy and innovation in general. It also helps to have one person in your technology transfer office designated as the lead for student IP-related issues. This person’s job could include maintaining an online frequently asked questions database to accompany the policy,

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

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providing official interpretations of the policy upon request, and acting as the point person for student IP questions and issues in general.

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Increased proliferation of student IP literacy remains a challenging, but feasible goal. The standard hands-off approach is not an optimal solution for maximizing the economic and societal value of student innovation as a whole. Through open communication, hard work, and modest policy revisions, the entire technology transfer industry can mutually benefit from a race-to-the-top to maximize student IP. Furthermore, this issue largely transcends individual institutional revenue models and financial priorities—presenting a unique opportunity for industrywide cooperation and improvement.

For more information contact the Association of Technology Managers (www.autm.net) or the National Collegiate Innovators and Inventors Alliance (www.nciia.org).

Acknowledgements: The Student IP Task Force would like to acknowledge the

organizational and editorial contributions of co-author Alex Breger, project manager at the National Collegiate Innovators and Inventors Alliance.

?????

1. Nathalie Duval-Couetil, Jessamine Pilcher, Phil Weilerstein, and Chad Gotch,

“Undergraduate Involvement in Intellectual Property Protection at Universities: View from Technology Transfer Professionals,” The International Journal of Engineering Education 30-1 (2014) 60–71.

2. This is particularly relevant in countries with legislation requiring universities to facilitate the tracking and capture of (usually via patenting) institutionally generated IP. See e.g. Bayh-Dole Act, 35 U.S.C. § 200-212 (2012) (containing relevant technology transfer statute under U.S. law). Other countries including Brazil, China, Japan, and most EU members have also enacted similar legislation. “Bayh-Dole Act,” AUTM, accessed January 24, 2014, http://www.autm.net/Bayh_Dole_Act1.htm.

3. See e.g. 35 U.S.C. § 202(a)–(c) (permitting nonprofit institutions to retain ownership over IP created pursuant to federal funding; resulting in promulgation of many modern-day institutional IP policies). See also Chou v. University of Chicago,

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

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254 F.3d 1347, 1356-57 (3d Cir. 2001) (holding matriculation as academic student sufficient to bind student to general institutional IP policies despite lack of separately signed agreement).

4. See Stanford v. Roche, 131 S. Ct. 2188 (2011) (upholding third-party ownership claim over subject IP where institution’s ambiguous language of assignment failed to effectuate transfer of rights from student to university).

5. See e.g. 17 U.S.C. § 107 (2012) (setting forth U.S. copyright fair-use provisions and referencing House Judiciary Committee, House Report No. 94-1476 indicating clear legislative intent that fair use apply in academic teaching contexts).

6. Duval-Couetil et al., supra note 1.

7. See 15 U.S.C. § 7001 (mandating that electronic signatures be given full contractual effect). The E-Sign Act in the U.S. and others like it around the world help ensure that “click” signatures and other forms of digital contracting remain a viable mode of exchanging contractual rights and obligations.

©2014 Association of University Technology Managers Effective August 2014

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

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The following scenarios were developed to illustrate situations in which students may develop or participate in the development of new intellectual property (IP) and how technology transfer offices (TTOs) and other academic administrators may approach a determination on ownership. All presumptions grouped with a particular conclusion must be true in order for the associated conclusion to apply.

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An individual or group of registered students who conceive and develop a new business idea in their dorms. The idea may or may not be inspired or draw upon their course material or assignments (all of which are assumed to be public domain) and makes only incidental use of university resources, therefore resulting in zero or negligible additional cost to the university.

Decision Point 1: Students conceive and develop an idea independent of their formal

studies, drawing on their own insights and technical skills. The students make no specific use of university resources while developing their idea.

Presumption 1a: The students receive no guidance or input from any staff, faculty, or administrators at their university.

Conclusion 1: Defaults to wholly student-owned IP. The university takes no equity and is willing to execute an IP waiver if requested.

Decision Point 2: Students make incidental use of university resources to develop their idea, for example the use of generic equipment, laboratories, computers, meeting rooms, and other publicly accessible resources.

Presumption 2a: The students receive no guidance or input from any staff, faculty, or administrators at their university, except for assistance from technical staff with respect to the generic resources used.

Presumption 2b: The university incurs zero or negligible marginal costs in relation to the resources used.

Conclusion 2: Defaults to wholly student-owned IP. The university takes no equity and is willing to execute an IP waiver if requested.

©2014 Association of University Technology Managers Effective August 2014

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Decision Point 3: Students draw upon the time, knowledge, or networks of technology transfer (or other professional, nonacademic) staff at the university.

Presumption 3a: The TTO does not incur any direct expenditures (patent, legal, consultancy, proof of concept, etc.) in the course of administering such advice. Presumption 3b: At the time of providing said advice, the TTO does not enter into any formal arrangement with the student pertaining to IP ownership, IP rights, or a monetary sum sought in consideration.

Conclusion 3: Defaults to wholly student-owned IP. The university takes no equity and is willing to execute an IP waiver if requested.

Decision Point 4: Students draw upon the expertise and knowledge of a faculty member at the university.

Presumption 4a: Faculty input amounted to no more than advice/consulting on where to locate information or other general considerations pertaining to development. The faculty member’s communications cannot have conveyed ideas constituting an inventive step.

Conclusion 4a: Seek verification and waiver from the relevant faculty member. Otherwise defaults to wholly student-owned IP. The university takes no equity and is willing to execute an IP waiver if requested.

Presumption 4b: Faculty communications amounted to assistance that may constitute an inventive step or otherwise create a joint author or inventor situation. However, such assistance did not draw upon existing intellectual property arising from the faculty member’s ongoing research.

Conclusion 4b: Co-ownership by faculty may be warranted. The university may

wish to waive any rights to IP generated by students or faculty. However, the

students should be advised to seek assignment of IP from the faculty member.

Decision Point 5: Students engage in significant use of university resources while

conceiving of or developing their invention or work of authorship.

Presumption 5a: The resources used are only those commonly accessed as part of the normal educational or dormitory-living experience.

Conclusion 5a: The university should view any emergent IP as a normal

consequence of the educational experience and disclaim any ownership interest.

©2014 Association of University Technology Managers Effective August 2014

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Presumption 5b: These resources used result in additional expenditure by the university, but do not constitute the use of any proprietary equipment, specialized resources, or other intellectual property.

Conclusion 5b: The university may consider charging a reasonable sum for the use of resources.

Presumption 5c: The students make use of or incorporate the university’s intellectual property.

Conclusion 5c: A formal IP license to the new venture will be required in

anticipation of future due diligence by an investor. This will necessitate formal involvement by the TTO. Such input may warrant the university seeking a stake in the new venture as well.

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Students conceive and develop an idea as part of their formal studies—typically an assignment or project for credit. They may receive input (guidance, technical advice, referrals, etc.) from faculty supervisors, and the development of the idea may result in some direct costs to the university (e.g., inexpensive consumables, laboratory time, significant computing resource, etc.). However, this input and resource allocation are no greater than that budgeted for any student coursework or project activity.

Decision Point 1: Students conceive and develop an idea as part of their formal studies: an assignment or project. Students were assigned the task of developing said idea by an instructor to fulfill a course requirement.

Presumption 1: The instructor provides little to no direct input toward the initial idea conception, only a general topic or problem to be addressed by the student.

Conclusion 1: Defaults to wholly student-owned IP. The university takes no equity and is willing to execute an IP waiver if requested.

Decision Point 2: Students may receive input (e.g., guidance, technical advice, referrals, etc.) from faculty supervisors. Improvements or modifications to the idea may have resulted directly from suggestions by the faculty supervisors, resulting in potential for co-ownership of the final IP.

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

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Presumption 2a: Faculty input amounted to no more than advice/consulting

regarding where to locate information or considerations for development, not

complementary ideas to make the idea more functional or improved.

Conclusion 2a: Defaults to wholly student-owned IP. The university takes no equity and is willing to execute an IP waiver if requested.

Presumption 2b: Faculty input amounted to assistance above and beyond what was required or necessary for completion of the assignment.

Conclusion 2b: Co-ownership by faculty member may be warranted, and the institution would therefore likely have rights assigned to it in accordance with its employee IP policy.

Decision Point 3: Development of the idea may result in some direct costs (e.g.,

inexpensive consumables, laboratory time, significant computing resources, etc.).

Concrete “threshold value” for when resources used for IP development are significant to the institution.

Presumption 3a: Resources used are no greater than budgeted for any student coursework, and those resources are provided to the student in exchange for the students’ payment of tuition, fees, etc.

Conclusion 3a: Default to student-owned IP since the institution did not

contribute resources to the student above and beyond what was allocated to him or her for completion of his or her coursework

Presumption 3b: Resources used include extra laboratory time above and beyond that allotted for coursework or specialized lab time requiring additional training or supervision by institutional employees were needed to develop the IP.

Conclusion 3b: This may constitute use of significant resources by the institution’s standards. Establishing a threshold value for resources that contributed to the IP is key in this situation. If significant resources were in fact used, then the institution should have a proportionate amount of rights assigned to it.

Overall Conclusion: In most cases, if institutional input is minimal or in accordance with the expected resources available to students for completion of coursework, then this scenario describes student-owned IP. Key considerations in this case are primarily financial and should include: (1) whether IP was generated as a part of routine coursework and (2) the measurable extent of institutional resources accessed (as

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represented by workspace, employee time/effort, reagents, additional funding, etc.) by student that are deemed included with student tuition and fees.

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Students embark upon an independent research project not part of their studies. This project necessitates significant use of university equipment and is likely to draw on the expertise and knowhow of faculty and other research staff. Students may be treated like any other researcher drawing on specialized technical assistance and facilities of the university as well as consumables. One way or another the university adds significantly to the success of the project by subsidizing it and may even (if only incidentally) make an inventive contribution.

Decision Point 1: Who are the formal inventors or authors? Who as part of the project

substantially participated in the conception of the invention or expression of the creative work?

Presumption 1a: The project was developed jointly by the student and members of faculty or research staff. This will result in co-ownership of the IP.

Conclusion 1a: The university will have some rights to the IP from the assigned rights of the co-inventors/coauthors. The TTO will proceed with protecting the IP, but will need to enter into an agreement with the student either getting him or her to assign his or her rights to the institution or developing an interinstitutional agreement (IIA) that gives the university the lead in protecting and licensing.

Presumption 1b: The invention or work of authorship was developed solely by the

student.

Conclusion 1b: The university may or may not elect to pursue ownership

depending on how significant the institutional resources used in the development of the IP are judged to be. (See Decision Point 2 for further discussion.) The university may decide not to take any ownership and will not pay to prosecute a patent or otherwise commercially exploit the IP. Furthermore, the university needs to ensure that it does not automatically assume any liability despite its intent not to pursue ownership. The student may be directed to other university resources that will help him or her pursue commercialization of his or her IP.

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Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Decision Point 2: How significant was the university contribution?

Presumption 2: The resources used in the development of the IP were significant vis-à-vis the use of expensive equipment not normally available to students, support staff time to assist with experimentation, or institutional trade secrets.

Conclusion 2: The university can assert that it has rights related to the project because of its investment of significant resources. However, the university should ensure that it was made clear to the student in advance of using institutional resources that such use would enable the university to claim ownership over resulting intellectual works. This would have enabled the student to decide not to undertake the project using the same extent of university resources.

Decision Point 3: What does the student want to do?

Presumption 3a: The student wants to retain his or her ownership of the invention or work of authorship.

Conclusion 3a: This situation may be problematic if the university wants to enforce its ownership rights as a result of its investment of significant resources. The institution will need to reach an agreement (usually in the form of an IIA) with the student so that the parties’ respective IP rights can be settled. This is especially important if there are co-inventors/coauthors who have already assigned their rights to the university. The absence of an agreement here creates a risk for both parties going forward even if the institution has a broad significant-use policy in place.

Presumption 3b: The student wants to assign his or her rights to the university. Conclusion 3b: The student will be treated as a co-inventor/coauthor. As part of assigning his or her rights to the university, there will be a negotiated share of revenues resulting from the IP that will be assigned to the student. The university will proceed with protecting and licensing the technology.

Decision Point 4: What does the TTO want to do?

Presumption 4a: The TTO decides that there is value in the inventive or creative work and that it has a significant claim of full ownership over the IP.

Conclusion 4a: The university negotiates with the student to assign IP rights to the university, and then pays to protect the technology, manage all ensuing licenses, and distribute royalties to the inventors and other stakeholders.

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA; Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Presumption gib: The TTO decides that there is little value in the invention and decides not to assert ownership.

Conclusion gib: The technology belongs to the student, and the student is free to protect and commercialize the technology.

Overall Conclusion: There are many factors involved in this scenario, and the TTO ultimately has to make a decision involving how strong of a claim it has over the IP relative to how much it wants to invest protecting and licensing it. The TTO also needs to ensure that students entering into this type of situation are briefed before any significant inventive actions are taken. In the ideal outcome, the student will assign the IP to the institution and enjoy a share of any revenue stream. In the worst outcome, the student and the university enter into a very public battle over rights which—despite potentially being winnable in court—will do little to aid the public image and reputation of the institution.

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Students are invited by a faculty member or other university employee to play a part in an existing project or research program—which may be funded from external sources (and may be formally sponsored by an industry member). The student is not forced to accept the project, but sees it as a groundbreaking opportunity that will increase employability and marketable skills. He or she will probably generate new IP, and such IP may be the subject of a patent filing on which the student would be co-inventor. To make a full contribution and maximize learning, the university intends for the student to be an integral member of the research team—privy to confidential information and other knowhow available to the project. Ideally, there should be a clause in the student handbook or guide setting

forth the circumstances under which students can reasonably expect the university to pursue ownership of student-generated IP.

Decision Point 1: Who are the inventors or authors? Who as part of the project actually participated in the conception of the invention or expression of the creative work?

Presumption 1a: The student is an integral part of the team and contributed

significantly to the conception of the idea or expression of the creative work.

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Conclusion 1a: The student is a co-inventor or coauthor.

Presumption 1b: The student is an integral part of the team, but does not contribute

significantly to the conception of the invention or expression of the creative work. Conclusion 1b: Legally, the student is merely an observer with respect to the act of invention, in which case the student is not a co-inventor. The same considerations that apply in determining whether a faculty member is an inventor also applies in the student scenario. A student’s subjective belief regarding

whether he or she should be listed as a co-inventor is only relevant if objective data supports that he or she actually made a substantial contribution. The fact that students often think mere participation in an R&D project automatically conveys co-inventor status highlights the need for additional IP outreach and literacy targeted at students who participate in research projects.

Decision Point 2: Who owns the IP?

Presumption 2a: The student is not being hired to work on the project by either the company or the university. If this were a work for hire, then the terms of the employment contract would affect ownership of the IP—for example, if the contract explicitly stated that all inventions belonged to the employer.

Presumption 2b: The invitation to work on the project is not made contingent on signing away rights to the IP, in which case a contract would likely exist supported by mutual consideration between the university and the student. If nothing explicit is said, there is no reason for the student to infer they are relinquishing rights simply by accepting the invitation.

Presumption 2c: There are significant university or third-party sponsor resources used to conduct the work—such as lab equipment, computers, databases, chemicals, etc. The key issue here is whether the student is participating as a normal part of his or her educational experience or if the activity in question lies outside the typical educational package. Since students attend the university primarily to get an education, the presumption has to be that in the absence of other evidence, nonformalized research activities are a normal part of their educational experience. This presumption is strengthened by the fact the student’s participation was invited by the university or one of its representatives. All people in universities are not equal. Faculty and staff members have a certain power over students, both in terms of

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grades and contacts/references for future graduate education and employment. Such invitations may not be easy to turn down.

Presumption 2d: The university agreed with a sponsor or funding agency to

relinquish its rights or provide a no-cost license/right of first refusal over any resulting inventions. Here, the third-party agency can claim assignment of ownership or other IP rights from the university, but not until the university has secured title from all co-inventors/coauthors. These rights can be secured through employment contracts or purchased directly from the inventors. For students, if a good case can be made that the project was not part of the normal educational experience, then an advance participation contract might be appropriate. Consult counsel before implementing one of these as such contracts are governed by local law.

Conclusion 2: English Common Law and other prevailing legal systems presume that inventors retain ownership of their inventions unless they choose to relinquish it. Therefore, the university must be able to show that the inventor not only had reason to relinquish his or her rights, but did in fact do so. Explicit documentation with a clear informed consent on the part of the potential inventor is necessary to demonstrate this. This principle was central to the holding of the U.S. Supreme Court’s landmark decision Stanford v. Roche. (See Stanford v. Roche: Supreme Court Clarifies Intellectual Property Ownership by Kimberly Honeycutt, PhD, in Volume 2 of the Technology Transfer Practice Manual.)

The need for explicit documentation and informed consent is especially important when students are involved. First, since the students are attending school to get an education, virtually every part of the university or college experience can arguably be seen as part of the normal educational experience. Participating in a research project (whether internally funded, externally funded, or merely a faculty member or teaching assistants’s self-funded work) must therefore presumptively be treated as part of the student’s usual educational experience unless there is strong evidence of informed consent to the contrary. Furthermore, since students are generally unsophisticated parties in the legal sense, any contractual arrangement with them should be treated carefully (especially contracts of adhesion with which the student has had no say in negotiating—e.g., the blanket undergraduate enrollment agreement signed by every student upon enrollment). A specific, written contract in plain, easily understood language is highly recommended if you need to vest ownership in the institution or third party.

©2014 Association of University Technology Managers Effective August 2014

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Managing Student Intellectual Property Issues at Institutions of Higher Education: An AUTM Primer

Abigail Barrow, PhD; La Royce Batchelor; Alex Breger, JD; Nathalie Duval-Couetil, PhD, MBA;

Latanya Scott; Jeffrey Skinner, PhD, MBA, RTTP; Phyl Speser, JD, PhD, RTTP; and Phil Weilerstein

Decision Point 3: What should the TTO be doing?

Presumption 3a: The TTO has developed and posted on its website material for students and third-party collaborators clarifying how the institution treats students with respect to IP. The TTO should make sure that collaborators, project leaders, university employees, and all participating students know how to access this

information, have accessed and read the information, and do in fact agree with the terms. Ideally, students should be encouraged to send back documentation and other feedback acknowledging that they have read and understood the applicable IP policy. If they do not understand or agree with its terms, students should be encouraged to ask questions or raise concerns. Unlike, for example, the dense and layered end-user license agreements often presented in the context of new software, IP documentation presented to students should be in plain and easy-to-understand language, and a receipt of acknowledgment should be a condition precedent for moving forward with the project.

Presumption 3b: The project is already off and running by the time the TTO finds out about it. The TTO should ensure that everyone involved understands and agrees with the university’s policy on student participation in creating IP. If there is disagreement and participants will not agree to sign informed consent, the appropriate person(s) in university management should be informed that a potential liability issue has emerged. Presumption 3c: Student IP is created, and all parties unambiguously agree that the university owns it. It is treated like any other IP created at the institution.

Presumption 3d: Student IP is created, but the student has not consented to relinquish rights to the university. The TTO needs to communicate to the student how it sees the student’s rights relative to the university’s. Ideally it would negotiate with the student to obtain rights in exchange for the typical inventor’s share of revenues. The usual policy for treatment of outside co-inventors on inventions made with university employees should apply.

Overall Conclusion: Inventors automatically own their invention unless they agree to part with their ownership rights. The key difference when dealing with student inventors versus other institutional inventors is that students are: (1) there primarily to learn and (2) in an unequal power relationship relative to faculty and other university personnel. These issues mean that clear policies and informed consent are critical before asserting

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university ownership of student-generated IP (especially where university ownership is necessary to honor and effectuate a contractual conveyance to a third-party sponsor/ funder).

It is insufficient for the university to depend solely upon catchall significant-use language to secure IP ownership. This is precisely because of how difficult it is to say what is or isn’t outside the scope of the normal educational experience at an institution of higher education. So while a formal policy on student IP is highly advisable, good policies without adequate and thorough dissemination mean little on their own. Neither does the fact a student has read the applicable IP policy—achieving true informed consent status requires a proactive stance. The university needs reasonable IP policies, and students should agree with those policies before they are permitted to participate in research projects. As demonstrated in Stanford v. Roche, an ounce of prevention is worth a pound of cure.

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Proceedings of the 2014 Winter Simulation Conference

A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds.

          ENHANCING THE ANALYTIC UTILITY OF THE SYNTHETIC THEATER OPERATIONS RESEARCH MODEL (STORM)

Mary L. McDonald

Stephen C. Upton

Christian N. Seymour

Thomas W. Lucas

Susan M. Sanchez

Paul J. Sanchez

Harrison C. Schramm

Jerry R. Smith

Operations Research Department Assessment Division (OPNAV N81)

Naval Postgraduate School Navy Headquarters Staff

Monterey, CA 93943, USA Washington, DC 20350, USA

EXTENDED ABSTRACT

The Assessment Division, Navy Headquarters Staff (OPNAV N81) uses large-scale simulation to analyze how budgeted capabilities and capacities map to risk in various scenarios. The Navy, along with the Air Force and Marine Corps, use the Synthetic Theater Operations Research Model (STORM) to assess risk in an integrated, campaign setting. Ultimately, analyses performed in STORM inform the decisions made by the Services for future resource planning. STORM is a large, stochastic campaign-level simulation that requires many inputs for a given scenario and generates an enormous amount of output data, which then needs to be turned into an analysis product. We are developing tools and methods that: (1) reduce the amount of manpower and time required to complete STORM output post-processing, (2) determine, in a sequential dynamic manner, a sufficient number of replications to perform, (3) support STORM verification and validation, and (4) boost the speed and precision with which analysts are able to gather insights from a set of simulation runs.

   A current impediment to fast and efficient use of STORM is the sheer volume of data it generates. There are many objects and events in a campaign, and STORM output can include a complete trace of the model state over the simulated campaign. Consequently, STORM routinely creates gigabytes of output from a single replication. When multiple replications are required, even more data is generated. Moreover, this output is typically not in a form that can immediately be used for analysis. Thus, some type of post-processing, e.g., filtering and transformation, is required to produce a reduced set of data that is suitable for subsequent analysis. This reduced set may still task computational resources, e.g., memory and disk space, so other techniques may be needed, such as dynamic processing of streamed output, in order to successfully conduct a full analysis of the data. One component of our research is to determine how STORM post-processing can best be improved. The research involves identifying potential data generation and storage efficiencies, automating post-processing tasks, making use of distributed computation where possible, and reducing manpower requirements when using STORM. An additional benefit is the ability to accommodate larger run sizes than are currently feasible.

   Since STORM is stochastic, a determination must be made as to the number of independent replications to perform for each set of inputs. Replication allows analysts to better estimate output measures (e.g., blue systems lost), evaluate the variance of responses, and determine the distributions of outcomes (Lucas 2000). As more replications are made, these estimates become more precise. In addition, taking more replications increases our statistical power in detecting alternatives and increases

978-1-4799-7486-3/14/$31.00 ©2014 IEEE 4136

McDonald et al.

the chances of identifying rare, but perhaps critical, events. The number of runs required depends on the variability of the response and the statistical power desired (Law 2007). Since the variability of the response is usually unknown prior to running the experiments, ideally, the number of runs taken should be determined dynamically.

   For the reasons alluded to above, STORM runs are expensive, and current practice is for analysts to perform a predetermined, fixed number of replications for a given set of inputs. Depending on the characteristics of the scenarios being modeled, this may or may not be a sufficient sample size. In scenarios that have low variance and high signal strength, we may need fewer than the predetermined replications. In other scenarios, the ability to take more replications could be of enormous value. One objective of our research is to dynamically (sequentially) calculate appropriate sample sizes for the metrics of interest.

   Sometimes the most difficult aspect of gaining insights from a high-dimensional set of output is ‘putting it all together’ to form a coherent narrative that describes: (1) which major entities and platforms initiated key actions, (2) what happened or failed to happen, (3) when and where key combat events occurred, and, probably the most difficult to ascertain, (4) why major events or outcomes occurred or didn’t occur. Of the gigabytes of output that STORM produces, a ‘feature extraction’ process is first needed to determine the functions of the data that are most relevant and meaningful to the campaign analyst. Once key data is acquired from the raw data stream, we experiment with new methods for visualization and analysis of the simulation output data. This process supports the verification and validation process in that it can identify both ‘bugs’ in simulation code as well as unintended defects in the many combat plans that must be created by analysts as part of the scenario development process. Once the scenario has been satisfactorily, analysis and visualization techniques can be used to facilitate a quick understanding of the Who-What-When-Where-Why-How of a simulation event stream, and help analysts assess which scenario variations may be the most fruitful for further study with a focused subset of excursions. In our presentation, we will summarize work conducted to date and show examples of the analysis and visualization techniques that have been developed.

REFERENCES

Kleijnen, J. P. C., S. M. Sanchez, T. W. Lucas, and T. M. Cioppa. 2005. “A User’s Guide to the Brave New World of Designing Simulation Experiments.” INFORMS Journal on Computing 17(3): 263289.

Law, A. M. 2007. Simulation Modeling and Analysis. 4th ed. New York, NY: McGraw-Hill.

Lucas, T. W. 2000. “The Stochastic Versus Deterministic Argument for Combat Simulations: Tales of When the Average Won’t Do!” Military Operations Research: Special Issue on Warfare Analysis and Complexity—the New Sciences, 5(3): 9-28.

Lucas, T. W., and S. M. Sanchez. 2006. “The Brave New World of Designing Simulation Experiments for Defense and Homeland Security Applications.” In 2006 Joint Statistical Meetings Proceedings, 1212-1218.

Sanchez, S. M., T. W. Lucas, P. J. Sanchez, C. J. Nannini, and H. Wong. 2012. “Designs for Large-Scale Simulation Experiments with Applications to Defense and Homeland Security.” In Design and Analysis of Experiments, Volume III, edited by K. Hinkelmann, 413-441. Hoboken, NJ: Wiley.

4137

Conjectures regarding empirical managerial

accounting research$

Jerold L. Zimmerman*

University of Rochester, William E. Simon Graduate School of Business Administration,

Rochester, NY 14627, USA

Received 23 October 2000; received in revised form 13 February 2001

Abstract

  The empirical managerial accounting literature has failed to produce a substantive cumulative body of knowledge. This literature has not matured beyond describing practice to developing and testing theories explaining observed practice, like other areas of accounting research. While the lack of publicly available data is a popular reason for this literature’s underdeveloped state, it is not the only one. Other conjectures include: its inductive approach, researchers’ incentives, its use of non-economics-based frameworks, the lack of empirically testable theories, and its emphasis on decision making, not control. © 2001 Elsevier Science B.V. All rights reserved.

JEL classification: M41

Keywords: Empirical management accounting

1. Introduction

  Ittner and Larcker (2001) review the empirical research in managerial accounting. They cast their net widely, beyond the mainstream accounting

 $The John M. Olin Foundation and the Bradley Policy Research Center at the University of Rochester provided financial support. Comments from Liz Demers, Joel Demski, Kathy Jones, Scott Keating, S.P. Kothari, Dave Larcker, Andrew Leone, Cheryl McWatters, Ross Watts, and Joanna Wu are gratefully acknowledged.

*Corresponding author. Tel.: +1-716-275-3397; fax: +1-716-442-6323. E-mail address: zimmerman@simon.rochester.edu (J.L. Zimmerman).

0165-4101/01/$-see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 4 1 0 1 ( 0 1 ) 0 0 0 2 3 - 4

412 J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427

journals and discuss papers in practitioner-oriented journals and non-North American journals.1 In fact most of the papers they cite are in these non-mainstream journals. In addition to suggesting a variety of research opportunities and discussing important methodology issues, Ittner and Larcker (IL) offer several general observations regarding the empirical managerial accounting literature.

(T)he research is driven by changes in practice. y(M)any papers are motivated purely by the fact that a certain topic has received considerable attention in the business press, with little effort to place the practice or study within some broader theoretical context.

(W)e are left with an underdeveloped body of research that fails to build on prior studies to increase our understanding of the topic, leaves many important research topics unexplored, and lacks the critical mass of related studies needed to reconcile conflicting results to reach consensus on the performance benefits from various manufacturing performance measurement practices.

I agree with these generalizations. After reading their review of this literature, I am left wondering what we have learnt. What generalizations can be drawn? What null hypotheses have been rejected? What burning, unanswered questions remain? Where are the intriguing anomalies? Or, in the parlance of

an old fast food restaurant ad for hamburgers, “Where’s the beef?”The failure to produce a substantive body of knowledge is not IL’s fault. The

authors have faithfully discharged their responsibilities to survey the literature. The failure lies with the literature itself. My comments focus on trying to understand the current state of affairs in empirical managerial accounting research. Why do so few generalizable findings exist? Why are so many of the studies cited by IL published outside the mainstream, North American accounting journals?

  The empirical managerial literature focuses on describing current accounting practice. Most other accounting research areas also started descriptively, but as empirical findings accumulated, theories were developed to explain what was observed and to predict phenomena yet to be observed. The empirical managerial literature has failed to take this next step. Why? Hopefully, by better understanding the reasons for this literature’s lack of progress, we will avoid making the same mistakes in the future.

  The next section discusses the framework IL use to organize the empirical managerial literature. Section 3 compares the references cited in the IL survey to the other survey papers in this volume. The next two sections describe the

  1 They exclude most behavioral research, experimental and compensation studies, and qualitative case studies.

J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 413

general function of research, and offer some conjectures as to why so little has been learnt from empirical managerial studies. The last section provides some conclusions.

2. Ittner–Larcker’s organizing framework

  IL organize their empirical managerial accounting literature survey using the Value Based Management (VBM) framework. This framework, distilled from the consulting practices of several firms (notably McKinsey and KPMG), consists of the following steps:

* choose internal objectives that enhance shareholder value;

* select strategies and organizational designs to achieve the objectives;

* identify performance variables that create value;

* develop action plans;

* evaluate the success of action plans;

* assess and modify the internal objectives, strategies, plans, and control

systems.

 IL could have chosen a theory-based framework, such as the principal-agent paradigm incorporating the Milgrom and Roberts (1995) complementarities approach. It is well understood among academics that decision right assignments, performance measures, compensation plans, and other policies, are jointly determined, interdependent, and endogenous.2 IL recognize these limitations of the VBM framework, and discuss the methodological issues raised (endogeneity and simultaneity). The primarily normative VBM framework portends the nature of the literature, its focus, and I believe its ultimate success.

 Most of the studies reviewed by IL address practice (ABC, EVA, balanced scorecard). Many of IL’s recommended research topics focus on practice. “Researchers can make a significant contribution by providing evidence on the methods used to set financial and non-financial targets and the performance implications from these choices.”Empirical managerial accounting research has not evolved much beyond description of practice to developing and testing theories suggested from practice. In Section 4, I discuss the general nature of

2See Brickley et al. (2001) for a review.

414 J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427

how knowledge accumulates from theory-based research. I argue that little has been learnt from this literature, partly because researchers’ incentives have shifted towards describing practice rather than developing and testing theories.

 Describing practice, per se, is not unproductive. If the objective of research is producing empirically verifiable theories, a rich description of practice often leads to new theories. For example, finance researchers first documented the random walk of security prices, which lead to the efficient markets hypothesis (see Fama, 1965). However, the risk is that descriptive research can lead to-and even be motivated by-normative consulting engagements, and not to theory development and testing. Descriptive research alone will not build a coherent literature and understanding of managerial accounting practices.

3. Empirical analysis of the empirical managerial accounting literature

 In this section I compare the references cited by IL to those cited by the other eight surveys in this volume. Each reference in nine survey papers is categorized into one of eight categories listed in Table 1.3 I present the following evidence cautiously. No attempt is made to eliminate references that did not summarize research but rather provided background. Drawing inferences from this data assumes that the citation frequencies in the survey papers are unbiased estimates of the citation frequencies in the literature. Clearly, the citation rates in the various survey papers depend on the scope and objective of each survey and the nature of the topic. Given these caveats, the evidence should be viewed as suggestive.

 Table 2 lists the distribution of references cited by IL, the distribution of all the references in the other eight surveys, and the individual distributions of the other eight surveys. The IL survey cites far fewer mainstream North American accounting journals, but cites more non-mainstream accounting and practitioner journals than the other eight survey papers.4 Only 23% of the references in IL are to mainstream North American accounting journals, whereas in the other eight studies 51% of the citations are in these journals. This is due in part to IL’s objective of casting their net widely. It also results from few published papers to review in the North American journals.

 Equally as dramatic as the preceding citation rates is IL’s low reference frequency to economics, finance, and statistics literatures compared to the other eight surveys (9% versus 24%). These citation rates are consistent with a literature that draws less on economics and finance than other areas of

 3 The references are counted in the version of each paper presented at the conference in April 2000.

    4IL’s citation distribution is statistically significantly different from the combined distribution and the eight individual distributions at the 0.05 level based on chi-square tests.

J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 415

Table 1

Eight categories used to classify references cited in the nine surveys in this volume

Category

Mainstream North American Accounting Review

accounting journals Contemporary Accounting Research Journal of Accounting and Economics Journal of Accounting Research Review of Accounting Studies

Other accounting journalsa Accounting Horizons

Academy of Management Accounting Accounting, Organizations, and Society Journal of Accounting, Auditing and Finance Journal of Accounting and Public Policy Journal of Accounting Literature

Journal of Business, Finance, and Accounting Journal of Management Accounting Research Management Accounting Research

Working papers -

Books and monographs -

Economics, finance, and statistics journalsa American Economic Review

Bell/Rand Journal of Economics

Econometrica

Journal of the American Statistical Association

Journal of Economic Literature

Journal of Finance

Journal of Financial and Economics

Journal of Political Economy

Journal of Public Economics

Quarterly Journal of Economics

Practitioner-oriented journalsa Accounting and consulting firm publications

AICPA and FASB publications

Financial Analysts Journal

Harvard Business Review

Institute of Management Accountants

Journal of Applied Corporate Finance

Management and strategy journalsa Academy of Management Journal

Journal of Business Strategy Strategic Management Journal

Tax journalsa Journal of the American Taxation Association

National Tax Journal

aRepresentative journals, but not an exhaustive set.

accounting research. Fewer citations to economics and finance is consistent with a literature oriented to describing practice, not testing theories (which often are based on economics or finance). Only 3% of IL’s citations are to

Table 2

Distribution of references of papers published in this volume

Ittner and Other Bushman Fields, Lys Healy and Holthausen Kothari Lambert Shackelford Verrecchia

Larcker 8 papers and Smith and Vincent Palepu and Watts and Shevlin

Mainstream North American accounting journals

23%

51%

33%

87%

67%

58%

45%

48%

44%

79%

Other accounting journals

30%

4%

3%

4%

7%

13%

4%

4%

1%

8%

Working papers

8%

11%

16%

2%

10%

17%

11%

9%

15%

0%

Books and monographs

13%

5%

5%

1%

1%

4%

7%

9%

4%

0%

Economics, finance and statistics journals

9%

24%

40%

3%

13%

4%

29%

31%

14%

13%

Practitioner-oriented journals

13%

2%

2%

2%

2%

4%

3%

0%

1%

0%

Management and strategy journals

3%

0%

1%

0%

0%

0%

0%

0%

0%

0%

Tax journals

0%

3%

0%

0%

0%

0%

0%

0%

20%

0%

Total references

194

1364

221

141

84

144

551

111

194

62

J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 417

management and strategy journals, which suggests that the papers they review are not testing behavioral science theories either.

 Similar inferences regarding the empirical managerial literature are obtained when IL’s citation rates are compared to each of the individual eight surveys’ citation rates. Looking at the eight individual surveys, we observe that each cites more mainstream accounting journals and finance, economics, and statistics journals and fewer professional/practitioner journals than the IL review.5

 Upon closer examination of IL’s references, 44 cited papers are in the mainstream North American accounting journals. After excluding 13 compensation studies and seven theory papers, 24 empirical managerial papers are published in these journals. Certainly, the shortage of empirical managerial papers published in mainstream North American accounting journals reflects the scarcity of data. I conjecture below that other reasons are contributing factors as well.

 To summarize this section, IL cite more papers in practitioner-oriented journals and more papers outside the mainstream North American accounting journals, and rely less on economics, finance, and statistics than the other eight survey papers. These citation frequencies are consistent with the empirical managerial literature being long on describing practice (data description) and short on developing and testing hypotheses derived from economics and finance.

4. Role of theory in empirical studies

 I assert that one reason that the empirical managerial literature has failed to produce a coherent body of knowledge is because the literature’s objective is not to test theories. Moreover, in the few studies that do test theories, their hypotheses are often ad hoc or derived from a variety of different disciplines (contingency theory or expectancy theory). Unlike the incentive compensation studies (that began by explaining practice but now test agency-theoretic hypotheses) and capital market studies (that began by explaining practice but now test financial economics hypotheses), no unifying, economics-based theory has developed to guide empirical managerial accounting research.

 Succinctly stated, a theory explains what has been observed, tests empirically the hypotheses derived from the theory, and then predicts what is yet to be

 5Only 3% of the Fields et al. (2001) citations and 4% of the Holthausen and Watts (2001) citations are to economics, finance, and statistics. But 87% and 58% of the Fields et al. and Hothausen and Watts citations, respectively, are to mainstream North American accounting journals.

418 J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427

observed. As Hempel (1966) explains, knowledge accumulates through the systematic testing of hypotheses suggested by theories. Theory allows the systematic ordering of facts. Most survey papers in this volume offer an underlying theoretic framework to organize their literatures. Testing hypotheses derived from theory allows knowledge to accumulate in the sense that refuted hypotheses force revisions in the underlying theory. Theories seek to explain systematic empirical regularities and, generally, to afford a deeper and more accurate understanding of the phenomena in question. Theory broadens our knowledge and understanding by predicting and explaining phenomena that were not known when the theory was formulated. Theories suggest hypotheses that help guide scientific investigations regarding data to collect. The essential point is “without y hypotheses, data analysis and classification is blind”(Hempel, 1966, p. 13). Early descriptive studies often start with preliminary (“strawmen”) hypotheses.6

  Theory construction and empirical research interact. As described earlier, just as theories stimulate empirical work, rich empirical settings stimulate theory. Empirical facts and regularities cause theorists to construct explanations for what is observed. But in addition, theories make predictions about facts that have not yet been collected. Eventually, empirical anomalies cause theory revision (Kuhn, 1969).

  Some might argue that without data, generating hypotheses is a useless activity. However, clever empiricists will discover interesting data sets to test important hypotheses. This is especially true today given the wide variety of machine-readable data sets available and access to internet-based information.

  It is easy to overlook, but important to emphasize, how economic principles generate testable hypotheses and allow the accumulation of knowledge about accounting. Consider the LIFO/FIFO method choice. This literature is summarized in Kothari (2001), Fields et al. (2001), and Shackelford and Shevlin (2001). Given the descriptive studies of stock prices (their random walk behavior) and using principles from economics, financial economists deduced the efficient markets hypothesis. One implication of this hypothesis (and the maintained hypothesis about capital markets valuing cash flows) predicted that firms shifting to LIFO for both tax and financial reporting (because of the tax conformity rule) should have positive abnormal returns to the extent the market did not anticipate the LIFO adoption. The alternative, mechanistic hypothesis predicted negative abnormal returnsFthe market is functionally fixated on accounting earnings, which are now lower. The early tests were consistent with market efficiency (Sunder, 1975). Later studies refined the earlier tests using more sophisticated hypotheses as inconsistencies in earlier

  6For example, the early executive compensation studies (e.g., Coughlan and Schmidt, 1985; Murphy 1985) sought to reject the hypothesis that pay and performance were unrelated, as was often claimed in the popular press at the time.

J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 419

tests were discovered. As the research proceeded, knowledge about the efficiency of the capital markets with respect to accounting information accumulated and our theories of market efficiency evolved.

  Accounting empiricists often underestimate the importance of rigorous theory in designing their studies. Weak theory development is probably the most recurring reason for the accounting journals to reject empirical papers. A paper’s motivation and contribution critically depend on theory. Theory structures the study and suggests alternative hypotheses.

  Theories need not be stated in terms of mathematics. The essential element is the logic of the analysis. Mathematics makes the logic more rigorous and transparent. For example, consider agency theory that examines the trade-off between incentives and risk. Mathematics has proven very useful in developing a series of rigorous principal-agent models (Lambert, 2001). However, important theories exist that were not stated in mathematical terms. Consider Fama’s (1965, 1970) statement of the efficient markets hypothesis, William-son’s (1975, 1985) transaction cost economics, and the Jensen and Meckling (1976) agency theory. These are important non-mathematical theories. But more importantly, they spawned a wide variety of empirical work seeking to test the theories’ hypotheses. Other examples of non-mathematical theories abound: Coase’s (1937) theory of firms versus markets, Stigler’s (1971) theory of regulation, the Watts and Zimmerman (1978, 1986) positive theory of accounting choice, the Scholes and Wolfson (1992) tax framework, and the Smith and Watts (1992) predictions regarding incentives and firms’ investment opportunity sets. Bushman and Smith (2001) describe a non-mathematical theory of accounting and corporate governance in this volume.

5. Conjectures regarding the empirical managerial accounting literature

  In this section I offer six conjectures regarding why the empirical managerial literature has failed to accumulate a systematic set of findings. They include: the lack of reliable, consistent data; the literature’s atheoretical approach; changing incentives of researchers; the literature’s failure to embrace economics as its underlying discipline; few empirically testable theories; and the literature’s almost exclusive focus on decision making, not control.

5.1. Lack of data

  The paucity of “good”data is a longstanding and popular refrain for the empirical managerial accounting literature’s lack of progress. Compared to financial accounting research with its Compustat, EDGAR, CRSP, IBES, and NAARS files, empirical managerial research is definitely wanting. Probably the single biggest factor hampering empirical managerial research is the lack of

420 J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427

consistent data about what firms do internally. No cross-sectional data set exists about firms’ budgeting systems, transfer pricing methods, standard cost systems, cost allocation schemes, and so forth. This has a number of implications:

Doctoral students gravitate away from this research area towards data-rich environments, such as capital markets, executive compensation, and tax.

Data collected from surveys suffer from well-known problems such as response and surveyor biases. These limitations require researchers to be more careful in drawing inferences from studies employing survey methods.

Data collected from companies to which researchers happen to have access are likely to be a non-random sample of firms. For example, firms having problems may be more willing to allow researchers access than successful firms concerned about potential competitors gaining access to their proprietary data.

To the extent researchers gain access to proprietary data sets their studies are not replicable. However, useful insights can be gleaned from such data sets.

  Clearly, “better”data is always preferred to “poorer”data. But it is hard to lay all the blame for the empirical managerial accounting literature’s lack of progress on this one reason. Economics has tackled very interesting, nontraditional questions and made considerable progress on them lacking machine-readable, standardized data. Lazear (2000) describes numerous examples where economists have successfully attacked non-traditional problems (discrimination, the family, theory of the firm, and education). Many of these studies rely on the creative use of ad hoc data sets. For example, Wolfson’s (1985) oil and gas paper illustrates the insightful combination of interesting theory and unique data. Masten and Crocker (1985) and Allen and Lueck (1992) test incentive contracting hypotheses using natural gas contracts collected by a US government agency, and landowner–farmer contracts from a 1986 Nebraska and South Dakota leasing survey, respectively. Given the accomplishments of economists, I find it difficult to attribute our slow progress in empirical managerial accounting to the scarcity of machine-readable data sets.

  Unfortunately, the “poor data”mantra has led to various dysfunctional outcomes. “Poor data”is often used to justify weak (or no) theory and/or badly designed and implemented research methods. Some researchers mistakenly believe that all journal editors and referees impose the same empirical standards on managerial studies as they do on large-scale financial accounting studies. This belief is used to avoid these journals and the high academic standards they impose. However, 24 empirical managerial

J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 421

accounting studies have been published in mainstream North American journals, thus refuting the claim that these journals reject all such papers.

5.2. Atheoretical approach

  In 1986, the Harvard Business School held a colloquium on field studies in managerial accounting to encourage “authors to understand and document the management accounting practices of actual organizations. ...A second, and even more important, objective of the colloquium was to begin the process by which field research methods in management accounting could be established as a legitimate method of inquiry”(Bruns and Kaplan, 1987, p. 2–3). Hopwood (1983), Kaplan (1983, 1984, 1986), and others encouraged researchers to conduct more field-based studies documenting contemporary practices. Kaplan (1986) describes a research process that first focuses on case studies and field studies, and then eventually develops models and theories. While Kaplan (1986) points out that theory is useful in guiding empirical research, his prescription for managerial accounting research called for observation and description. Accounting researchers should be “in the field attempting to understand how accounting information is developed or used in actual organizations”(p. 429).

Researchers will need to leave their offices and study the practices of innovating organizations. ... The challenge for academic researchers is to discover the Pierre du Ponts, Donaldson Browns, Alfred Sloans, and Frederick Taylors of the 1980s; to describe and document the innovative practices that seem to work for successful companies. The research will be more inductive than deductive, but likely productive both for the individual researcher and for the management accounting discipline (Kaplan, 1984, p. 415).7

Notice that Kaplan is not calling for researchers to go into the field and test hypotheses from theories. He asserts that, unlike other social sciences, managerial accounting has not “accumulated a reliable and systematic body of factual knowledge”(p. 432) and therefore, it is premature to develop theories or test propositions.

  7Peters and Waterman (1982) adopted the same approach in their popular book, In Search of Excellence. They studied the management practices of 62 large, successful US firms. Many of these firms have failed to continue their previous performance trends (e.g., Atari, Eastman Kodak, Wang Labs, Proctor & Gamble, Levi-Strauss, and Xerox). Few of the eight basic principles of Peters and Waterman (bias for action, staying close to the customer, autonomy and entrepreneurship, productivity through people, hands-on, value driven executives, stick to the knitting, simple form, lean staff, dedication to central values) have provided the hoped for panacea predicted by the authors. The problem is a lack of theory. The eight prescriptions of Peters and Waterman suggest a “one-size-fits-all”approach. All eight must be used.

422 J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427

  Over 15 years have elapsed since the first call for more descriptive field-based research. Such a body of studies now exists. But it has not led to the theory building and testing that was envisioned. Perhaps it is too early, not enough field studies have accumulated, or the ones conducted are of low quality. These are certainly plausible justifications. However, other accounting research areas did not require 15 years between the initial descriptive research and eventual theory building and testing. Alternatively, perhaps the appeal for primarily inductive, descriptive research has not proven as productive a path as originally claimed.

  Not every empirical paper must test hypotheses. Purely descriptive studies that inform us about heretofore-unknown facts are useful. However, it appears not to have been fruitful for researchers to wander the hallways of corporations and manufacturing plants searching for facts unguided by tentative hypotheses. As Hempel (1966, p. 13) states,

(T)he maxim that data should be gathered without guidance by antecedent hypotheses about the connections among the facts under study is self-defeating, and it is certainly not followed in scientific inquiry. On the contrary, tentative hypotheses are needed to give direction to a scientific investigation.

5.3. Changing research incentives

  Perhaps the empirical managerial accounting literature has failed to evolve from describing practice to developing and testing theories because researchers no longer have these incentives. Maybe researchers face stronger incentives to describe practice than to develop and test theories. If business schools are

encouraging faculty to conduct more “practical”and less “theoretical”research, then faculty incentives have changed. Descriptive research usually

generates more citations in the popular press and thereby improves the school’s reputation in the business community than more theoretical research. The 2000 Business Week business school rankings now include a measure of each school’s “intellectual capital”. Faculty citations in The Wall Street Journal and Business Week, along with citations in scholarly journals are used to assess intellectual capital. Faculty consulting also enhances the school’s presence in the business community. All too often business students tend to value faculty consulting activities over research, especially “theoretical”research to the extent that a school’s ranking in the popular press depends on student and the business community’s perceptions, schools have incentives to reward faculty for descriptive research.

  The audience of our research papers is no longer just others in the academy as it was 30 years ago. Now, we seem to be conducting our research because it informs practitioners (Demski and Zimmerman, 2000). For example, Maher (2000, p. 341) states, “The motivation for some empirical research in

J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 423

management accounting has been to test the claims of consultants who propose ‘new’ management methods”. If this conjecture is true, then other accounting research areas should also be witnessing a similar movement from developing and testing theories to practitioner-oriented studies.8

5.4. Non-economics-based frameworks

  Lazear (2000) argues that “economics is the premier social science”, citing its expanding scope of inquiry beyond consumers, firms, and markets into explaining other social interactions and its adoption by other disciplines

 Other social sciences, such as cognitive psychology, could provide the necessary basic framework to develop accounting theories. However, the empirical evidence from the last 40 years indicates that with few exceptions, most accounting research innovations have their conceptual roots in economics.9 Either economics is more powerful or alternatively, the critical mass of accountants well trained in other social sciences is too small to produce a cumulative literature in accounting. (Creating knowledge requires large economies of scale involving skilled researchers who employ an underlying framework that uses a common language.)

 To the extent, empirical managerial studies test hypotheses, they often employ non-economics-based theories (expectancy theory and contingency theory). If economics-based hypotheses are more productive in furthering knowledge than other social sciences (as suggested by Lazear), then another factor retarding empirical managerial accounting’s lack of progress is its reliance on non-economics-based theories.

 8 Casual observations of the value relevance and valuation literatures are consistent with this conjecture. See Holthausen and Watts (2001) and Kothari (2001).

  9 Consider the following partial list: the role of accounting disclosures in capital markets relies on the efficient markets hypothesis and capital asset pricing model; many of the topics taught in managerial accounting such as fixed versus variable costs, transfer pricing, and cost allocations are based on microeconomics; information economics spawned theories of accounting disclosures, the audit risk model, and revealed flaws in the controllability principle; the early normative debates regarding the theory of income measurement often relied on economic income; agency theory has generated models of contracting and stimulated compensation research; experimental markets research has generated similar studies in accounting; economic theories of the firm and corporate governance have stimulated accounting choice and earnings management studies; and much of the accounting-based tax research follows from the economics paradigm of Scholes and Wolfson (1992).

424 J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 5.5. Few empirically testable theories

 While managerial accounting empiricists have been lax in developing and testing hypotheses derived from rigorous theories, managerial accounting theorists share some of the blame. Rigorous managerial accounting theory papers too infrequently take the next step and tease out the theory’s empirical implications (testable hypotheses). Rarely do we observe sub-sections in theory papers titled, “Empirical Predictions”. To some, mathematical elegance (and certainly tractability) seems preferred over relevance. An implicit assumption apparently exists that theorists should develop models and empiricists should take these models and deduce the empirical implications from the theory. More high quality empirical managerial research would be forthcoming if theorists made a greater effort to generate models with testable predictions and to discuss their models’ empirical implications.

5.6. Emphasis on decision making, not control

 It is well understood that accounting systems serve both decision making and control roles (Zimmerman, 2000). However, much of the empirical managerial research and the practice literature on which it is based emphasize the decision making/planning function to the near exclusion of control. For example, total quality management, re-engineering, activity-based costing, the theory of constraints, value chain management, just-in-time, and the balanced scorecard all assume that agents will enthusiastically adopt the new approach because it promises to maximize firm value. The maintained assumption of ABC is that if you provide managers with supposedly more accurate product costs they will embrace them. This “Field of Dreams”(if you build it, they will come) approach ignores employee self-interest. In particular, adopting ABC creates windfall gains and losses among internal managers because product costs are part of most firms’ internal control systems.

 Except for the recent interest in economic value-added metrics, most management fads have shunned new techniques that better align shareholder and employee interests. It has become popular among practicing management accountants to assert that their role includes both planning (improve decision making) and control (reduce agency conflicts). They wish to become an equal member of the decision-making team (Siegel and Sorensen, 1999, p. 5).

 Note the difficulty in empirically assessing the relative importance of decision making or control for a given firm’s accounting system. In equilibrium, firms’ control systems should not be binding, and hence it would appear that accounting systems are not being used for control.

 I am unsure as to why some academic and practicing accountants seem to favor accounting’s decision-making role in favor of its control role. Maher (2000) and Siegel and Sorensen (1999) argue that the term “accountant”

J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427 425

appears to have an increasingly negative connotation among students and practitioners. In 1999, the Institute of Management Accountants changed the name of its monthly magazine from Management Accounting to Strategic Finance. Accountants are viewed as passive bystanders or scorekeepers while others “play the game”.10 Perhaps decision-making-type research is more popular to practitioners, and hence accounting researchers have more incentives now to conduct such studies. As much as practitioners and academics would like to believe, firms’ internal accounting systems are used primarily for decision making, wishing it so does not make it happen. If researchers enter field sites thinking the accounting system is being used for decision making when in fact it is being used for control, then an incorrect implicit theory is guiding their data collection and analysis. Little wonder that the empirical findings from a misguided theory produces scant results. Holthausen and Watts (2001) draw a similar conclusion about value relevance research in financial accounting.

6. Conclusions

Certainly the lack of progress is partially attributable to the difficulties in securing “good”data. However, other fields (notably economics) have overcome data limitations. A Compustat-like data set for management accounting is unlikely to be produced. Nonetheless, individual researchers can become more innovative in discovering interesting data sets.

  Progress requires better collaboration between managerial accounting empiricists and theorists. Theorists should seek to develop models that yield

refutable implications. And empiricists must stop using the “bad data”apology to excuse papers that either do not test hypotheses or test poorly

formulated hypotheses. Managerial accounting researchers likely are best served by relying on economics-based hypotheses. Finally, accounting researchers should not ignore why accounting is what it is. Management accountants used to be called “controllers”. While some may find the control

10 Recognizing that accounting’s primary role is control, might be unpopular to some. “Control”raises the specter of agency problems, self-interest, and hence “greed”. Some people find it

unseemly to view society as avaricious. Viewing management accounting as part of a firm’s control system makes accountants into cops and places them outside of the decision-making team. A similar tendency to ignore accounting’s control function exists in capital markets research that focuses on descriptive research with little theory building (value relevance and valuation). See Holthausen and Watts (2001) and Kothari (2001) for surveys of these literatures.

426 J.L. Zimmerman / Journal of Accounting and Economics 32 (2001) 411–427

function of accounting pejorative, ignoring this likely important function leads to incorrect theories being applied and ultimately to studies that do not enhance our stock of knowledge.

   To the extent that incentives within business schools have shifted towards more consulting-like, practice-oriented research, then less theory development and testing papers will be written. In the long run, our stock of knowledge, not only in empirical managerial accounting research, but also in all areas of accounting inquiry, will suffer.

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