Expert Systems: Implications for Operations Management

Yunus Kathawala

12 INDUSTRIAL MANAGEMENT & DATA SYSTEMS 90,6

S ome applications of expert systems in manufacturing are examined in order to enable firms to become more efficient.

Introduction Expert systems are on their way to the factory. We can read about their application and future in popular magazines such as Business Week, and in academic journals such as Production and Inventory Management. Expert systems seem to be applicable in every area of business. For example, Bankers Magazine published an article in 1986 about "AI in Banking" which concludes that artificial intelligence will provide banks with a competitive advantage when used and implemented properly[1]. However, the main application of expert systems and artificial intelligence in the future will be in manufacturing[2]. The application of expert systems varies from process planning and scheduling, to design, analysis, diagramming and interpretations. In this article, only some applications of expert systems in manufacturing will be examined. Production and operations management will be influenced by expert systems because they will help to allocate scarce

resources in a more efficient way. Decision making, and the utilisation of equipment and resources, can be improved. The task of management science is to solve complex problems by applying algorithms, and using heuristics and other methods to find an optimum solution. Expert systems will help in this task which, today, is accomplished by conventional computers. Such systems are computers of the fifth generation which will enable knowledge to be captured and conclusions drawn from it.

The ability to recommend action which should be taken is only one difference between conventional computer and expert systems' output. The structure and operating method of an expert system is quite different, too. Someone who wants to apply an expert system, however, has to recognise the limitations and shortcomings generated by technology and the human being.

Architecture of an Expert System An expert system can be described by explaining the three components which perform several tasks within it:

(1) The knowledge base (2) The inference engine (3) The input-output facility.

The Knowledge Base This is the source of facts and knowledge associated with an area of application. It contains general information as well as heuristic and judgemental knowledge [3]. It must be built by a knowledge engineer using a question method to acquire the knowledge needed to perform the expert system's task. Knowledge acquisition and representation is the most time- and money-consuming task in building an expert system. Representation can be accomplished by using various techniques. The most common of these is to utilise rules, each of which can be isolated and, thus, changed later[14]. A rule represents one or more conditions of a particular item of knowledge; if the rule requirements are met it will lead to action. The most common rules used in knowledge representation are If-then relationships. However, other forms of knowledge representation exist, for example, semantic and association networks which point out factual relationships.

A third representation form is frames whose utilisation is increasing[14]. These result from a combination of the above stated representation focus, and have the advantage of representing hierarchies and relationships at the same time.

The Infereme Engine The inference engine is the heart of an expert system. It is responsible for the execution of the system's task. It consists of a reasoning method and is the mechanism for using the knowledge stored in the knowledge base. This latter can be manipulated by this mechanism. Two processing strategies are applied in inference engines[3]:

The author wishes to acknowledge the valuable assistance of Mr Peter Walter, research assistant, in conducting library research and helping to write this article.

EXPERT SYSTEMS: IMPLICATIONS FOR OPERATIONS MANAGEMENT 13

(1) Forward chaining (bottom-up processing): The system begins with the facts and looks for the best conclusion.

(2) Backward chaining (top-down processing): The system begins with a hypothesis and works backward, checking to see if the facts support the hypothesis.

The Input-output Futility This is the interface component of the system. It provides the user with the results and explanations of the reasoning. A good input-output facility will provide the user with answers to questions such as "How will the system arrive at a result?" and "How will the information be used?"

Expert systems software can use both algorithms and

heuristics Expert systems are quite different from conventional computers. The big difference is in the software. Because of their ability to manipulate knowledge, they are superior to a conventional computer which manipulates only data. Conventional software can use only algorithms, whereas expert systems software can use both algorithms and heuristics[5]. A further difference is that an expert system's software uses inferential processes while a conventional computer uses repetition processes.

The software of conventional computers is primarily numeric programmed. The programming of expert systems software is primarily symbolic processing.

Task Domain of Expert Systems Various tasks can be accomplished by knowledge-based systems. The task itself will exhibit the same characteristics which will be examined now in some detail.

Complexity Expert systems are applied in complex tasks such as planning, scheduling and diagnosing. These tasks often have to be broken down into subtasks or subproblems in order to be solved. An expert system can use this approach of performing a task, and it can be programmed into the inference engine. Simple problems should not come into the domain of an expert system since they are more appropriate for a conventional computer program.

Specification Since expert systems' tasks are complex, they should not be broad and widespread. This is because the search

space of such a system will increase with the number of potential solutions to a problem. The search space (problem space) is a "conceptual or formal area defined by all of the possible states that could occur as a result of interactions between the elements and operators that are considered when a particular problem is being studied" [6]. Narrowly defined tasks such as process planning or scheduling will still require a large amount of knowledge, but it will be limited to one task. Another consideration which leads to this characteristic is the problems associated with knowledge acquisition and representation. Since both activities performed by a knowledgeable engineer are very expensive and time consuming, every company has to take into account the costs and benefits of a broader task-performing expert system.

Expertise Expert systems perform tasks which require a particular amount of expertise. Expertise can be taken to mean a large amount of knowledge in a particular area and the ability to draw conclusions from this knowledge. Tasks such as operations planning and process design show this kind of characteristic. A large amount of data, information and knowledge about relations and linkages have to be considered when performing a planning task. Thus, an expert system will be an appropriate tool to support management in deriving a decision involving expertise [7].

Judgement Another characteristic of an expert system's task is the requirement for judgemental decision making. The system arrives at conclusions and action recommendations which include judgements on the part of the decision maker. This feature distinguishes expert systems from conventional computers, since a computer's output is usually a quantitative conclusion; whereas the expert system's output is an evaluated recommendation. Thus, an expert system will lead to qualitative decisions rather than to quantitative facts supporting a decision.

All the above characteristics regarding tasks for an expert system illustrate the differences between these systems and conventional computer programs. Therefore, an expert system can handle uncertainty which is a common feature of today's decision-making process. Since uncertainty can be diminished, the accuracy of the decision arrived at by using an expert system will increase, and, therefore, the quality of the decision will improve too.

Expert Systems and Manufacturing Manufacturing is an area where expert systems should find application. Computer integrated manufacturing (CIM) and flexible manufacturing systems (FMS) can be heavily influenced by the introduction of expert systems. Several applications of such systems in production management

14 INDUSTRIAL MANAGEMENT & DATA SYSTEMS 90,6

and manufacturing have already been established and will be explained in this article. Their contribution to increased efficiency and profit is easily discernible.

Process Planning One example is an expert system in process planning applied at Hughes Aircraft[5]. The company uses an automated process planning and assembling instructive system on its printed circuit-board assembly lines. The heart of the operation is a software package called Hughes Integrated Classification System or "Hiclass".

This software package, the development time of which was 15 man-years, uses three potential phases: interpretation, recovering and presentation. In the first phase, engineering, designing and drawings are transformed into tokens (an abstract form of data) for further processing. The reasoning phase includes an analysis of the designing and manufacturing constraints and requirements. The presentation of the translated tokens into usable computer outputs for display[5] is the last phase of the operation.

The system uses the database of the computer-aided design system and the manufacturing database. The goal of implementing such a system is to detect changes in production, since lot-size quantity is often small. Further, manufacturing these boards is labour intensive, and changes can save the company a considerable amount of money.

An expert system is able to work with incomplete data

and uncertainty In "Hiclass", if the assembler has access to a display, the system will give instructions on how to perform successive operations. Based on data that define products' design, "Hiclass" generates the appropriate manufacturing process. It applies manufacturing knowledge and rules to produce process plans which are then graphically displayed to assembly workers[8].

Operations Planning Another manufacturing example of expert systems is in operations planning. "Xcut" is a prototype expert system under development at Bendix, which is part of a larger effort to automate process engineering through computer-aided process planning techniques [8].

Information to "Xcut" can be given through a feature access-graph. The input can be supplied either

automatically by an expert system for feature recognition or by a process engineer[8]. "Xcut" has the knowledge in its knowledge base and the input information in order to generate an optimum plan of machine sequence.

Inventory Control Inventory control is another area where expert systems could be applied. The task of an expert system would be to minimise overall material costs. Thus, the system has to compare inventory costs with the costs of stockouts. Information necessary to accomplish this task includes:

(1) Frequency of use (2) Comparison of costs (3) Amount used per period (4) Delivery time (5) Availability of the item (6) Past data.

To illustrate the description of this knowledge in a knowledge base some inventory control and purchasing rules from a recent article by McGartland and Hendrickson are presented [3]:

If: material is concrete Then: average delivery time is three days If: material is reinforcing bar Then: average delivery time is three weeks If: material is framework Then: average delivery time is two weeks If: average delivery time is deferred (i.e. three

weeks) Then: time period to be considered is when

delivery time coincides only with those activities occurring within this time period

If: inventory levels are not sufficient to last the defined (amount in inventory < amount required)

Then: check purchase orders If: (amount ordered through purchase order

and inventory level) < estimated quantity required during time period for all activities

Then: request user to issue change order If: date of scheduled delivery > date material

is required Then: request earlier delivery of material or check

for possible alternative suppliers.

Process Control Process control is also mentioned by some authors as an area of potential expert systems' application[9]. When an operator has to handle a process problem, the advice of an expert recommending a particular action is necessary. In this case, an expert system could be the operator's guide and it will provide the operator with an evaluation of the problems. The evaluation process includes assessing the given information and the search for more data. The expert

EXPERT SYSTEMS: IMPLICATIONS FOR OPERATIONS MANAGEMENT 15

system asks questions if the given information is not sufficient to find a solution.

An advantage of the expert system in this process is its ability to work with incomplete data and uncertainty. After evaluating the problem, the system will lead the operator to a solution. Such a system is used at Campbell Soup, Inc. The company uses it to diagnose malfunctions of the company's cooking system (hydrostatic steriliser). The company sends a disk instead of an expert to the particular plant where the malfunction occurs.

The application of expert systems in process control will gain in importance as more manufacturing plants become automated.

Scheduling Expert systems can also be used for scheduling problems, especially important with flexible manufacturing systems. Problems arise because operation times for a large number of parts are variable and machine utilisation is a consideration. Rescheduling is often necessary due to component failures. An AI-based schedule will perform the following functions [10]:

• Determine optimal schedules based on production goals and machine and tool availability.

• Reschedule the FMS as needed, including replanning necessitated by breakdowns.

• Provide instructions and data to other systems for instructing staging personnel and recording data on system performance.

A well-known expert system for scheduling is Intelligent Scheduling and Information System (ISIS), a knowledge-based system for factory scheduling[ll]. This system takes into account a variety of constraints such as set-up times, operation alternatives, operation preferences and resource reservations. It is built by using Schema Representation Language (SRL) which enables the system to model a plant at all levels of detail (from physical machine descriptions to process descriptions) [11]. Thus, ISIS allows the user to build schedules at various levels of abstraction and it checks the schedule constraints relevant to the scheduling problem. Then the system will indicate to the user whether a feasible solution is reached.

The literature mentions more expert systems in the area of rescheduling, such as MARS (Management Analysis Resource Scheduler) [9]. Scheduling is one of the most promising application areas of expert systems since manufacturing costs are high in this area. The importance of scheduling in systems using FMS or CIM is that expert systems could have a favourable impact on overall performance.

Process Design The design of the production process is another area where expert systems can be applied. The expert system's

knowledge base has to contain, for example, the different kinds of tool arrangements and requirements for a particular production process. The expert system will decide on the sequence of assembly by operations considering set-up times, tool costs and other constraints [12].

Expert Systems and Computer Integrated Manufacturing (CIM) Computer integrated manufacturing is highly dependent on the information and data of the computer integrated manufacturing systems (CIMS). CIMS decisions are mostly very complex and ill-structured. Thus, a highly efficient CIMS is based on the use of good information and its accurate reasoning. The co-ordination of plans and particular tasks will contribute to a satisfying overall manufacturing process.

An expert system applied in CIM can perform various tasks which were formerly performed by human experts or a conventional computer. The system will increase the quality of the decision and the speed of the decision­making process. The results will be better products, less idle capacity and a potentially competitive advantage since utilisation of this new technology will have a long-term impact on manufacturing.

Expert Systems and Flexible Manufacturing Systems (FMS) The application of flexible manufacturing systems (FMS) is crucial to the success of manufacturing companies in an increasingly competitive market. A FMS shows various advantages due to the overall flexibility of the system. To exploit the strength of FMS, management has to make decisions on a daily basis in a complex environment. The manager can be assisted in this by an expert system. The potential benefits of the systems are monetary savings, improved quality, reduced lead time and reduced production and inventory costs[13].

All the examples show possible applications of expert systems in manufacturing. However, management has to consider several issues when implementing and applying such systems to achieve the intended results.

Implications for Management Although expert systems have a potential future, they should only be applied in tasks which show the above characteristics.

The development of the system depends on the function of the system for management. It will be influenced by the width of the task and the knowledge required to perform it. Since knowledge acquisition and representations are an expensive step in developing an expert system, management should consider the trade­off of these systems.

16 INDUSTRIAL MANAGEMENT & DATA SYSTEMS 90 ,6

After deciding whether to develop and implement an expert system responsibility for its development has to be assigned. Some authors[14] suggest starting with a small project to gain experience. Each stage (development and implementation) can be a source of failure and mistakes which can lead to crucial shortcomings of the whole system. For example, knowledge acquisition can be the cause of various problems[15]. It can be too costly or the knowledge cannot be represented, since the human expert may be unable to communicate his experience. Another important consideration is the choice of the knowledge representation language [15]. The language can be too rich to represent the knowledge or it may be incompatible with the expert's knowledge. The implementation stage can also cause problems when the system is not accepted or it cannot be tested due to the inability to verify its output.

The maintenance of an expert system presents no problems and, if new knowledge is acquired, the knowledge base can easily be enlarged and the new knowledge added to the existing base.

Expert systems in manufacturing will be increasingly applied. Today's early utilisation will pay off in the future since it will provide the user with a competitive edge and with valuable benefits. Improved quality, cost reduction, shorter lead time and less idle capacity are only some of the benefits. Expert systems will aid the speed and accuracy of information processing and decision making [16] and they will revolutionise the manufacturing environment.

Conclusion The examples quoted in this article show how expert systems are applicable in operations management. Whether they are applied in inventory control or process control, the task of expert systems will be to solve complex problems, to allocate resources, and to enable the firm to become more efficient. Production and operations management search for optimal solutions with an environment of constraints, knowledge and variety. Expert systems can help to perform this task. It will be necessary to use them in production and operations management to achieve satisfactory results.

References 1. Weigh, K.H., Rockweel, G.B. and Marbone, T.J.,

"Artificial Intelligence in Banking", The Bankers Magazine, Vol. 169, 1986.

2. "Manufacturing Will Be Main Application Area for Expert Systems'', Industrial Engineering, October 1986, p.4.

3. McGartland, M. and Hendrichson, C.T., "Expert Systems for Construction Project Monitoring", Journal of Construction Engineering and Construction Management, Vol. 111, 1985, pp. 293-307.

4. Harvey, J.J., "Expert Systems: Present and Future", Computers and People, January-February 1987, pp. 85-90.

5. Mordoff, K.F., "Hughes Aircraft Utilizes Artificial Intelligence in Assembly Procedures", Aviation Week and Space Technology, 2 September 1985, pp. 66-7.

6. Harmon, P. and King, D., Expert Systems Artificial Intelligence in Business, John Wiley & Sons, Inc., New York, 1985.

7. Fisher, E.L., "Expert System Can Lay Groundwork for Intelligent CIM Decision Making", Industrial Engineering, March 1985, pp. 78-83.

8. Goff, K.W., "Artificial Intelligence in Process Control", Mechanical Engineering, October 1985, pp. 58-61.

9. King, M.S. and Brooks, S.L., "Knowledge-based Systems", Mechanical Engineering, October 1985, pp. 58-61.

10. Schaffer, G.H., "Artificial Intelligence: A Tool for Smart Manufacturing", American Machinist and Automated Manufacturing, August 1986, pp. 84-94.

11. Fox, M.S. and Smith, S.F., "ISIS - A Knowledge-based System for Factory Scheduling'', Expert Systems, Vol. 17 No. 1, 1984, pp. 25-49.

12. Mertens, P and Kanet, J.J., "Expert Systems in Production Management: An Assessment", Journal of Operations Management, Vol. 6 No. 4, August 1986, pp. 393-404.

13. Turban, E. and Sepehri, M., "Applications of Decision Support and Expert Systems in Flexible Manufacturing Systems", Journal of Operations Management, Vol. 6 No. 4, August 1986.

14. Keefe, R.M., Belton, V. and Ball, T., "Experiences with Using Expert Systems in OR", Journal of the Operational Research Society, Vol. 37 No. 7, 1986, pp. 657-68.

15. Bell, M.Z., "Why Expert Systems Fail", Journal of the Operational Research Society, Vol. 36 No. 7, 1985, pp. 613-19.

16. Denton, K., "Decision-making Technology", P & M Review with APICS News, January 1988, pp. 35-7.

Further Reading Herlmere, G., "The Future of Artificial Intelligence", Radio

Electronics, May 1987, pp. 85-90. Kathawala, Y. and Timpner, C.J., "Artificial Intelligence: A Key

to the Future", Midwest Production/Operations Management Division, Midwest Business Administration Association and International Journal of Computer Applications (forthcoming).

Rao, H.R., Lingaraj, B.P. and Kathawala, Y., "Artificial Intelligence: Its Impact on P/OM", Proceedings, P/OM, MBAA, March 1987.

Yunus Kathawala is Professor of Management at Eastern Illinois University, Charleston, USA.