A Production Management Expert System Developed in Tirs

This paper outlines work done by the authors in developing a production management expert system in TIRS, The Integrated Reasoning Shell. The expert system addresses the complex nature of decision making in production planning and scheduling. The decision making process is structured in a hierarchical form. Much of the knowledge encoded relates to modern thinking with regard to production vis-àvis lean production, just in time, group technology and total quality management. INTRODUCTION Production planning and control is a complex process lying at the heart of manufacturing operations. The process encompasses decision making spanning three progressive sub-functions, programming, ordering and dispatching. Programming plans output of finished product. Ordering plans material input from suppliers and output of parts from processing departments. Dispatching plans parts processing on machines necessary to complete orders by due-date The successful operation of production planning and control systems (PPCS) is crucial to the survival in the competitive consumer driven markets which prevail in most manufacturing sectors. Gone are the days of high stocks and using inflexible production planning techniques based on stock control and long range forecasting. Companies have to be dynamic, offering ever-more product options with reducing life cycles and far greater product complexity. They have to be able to supply 'Just in Time' and introduce new product ranges very rapidly. All this has had a great effect on the way production facilities are designed and managed with changes in levels of automation and in the application of computer based technology at all levels. The use of advanced computer tools such as expert systems within production planning and control is becoming more common place. Expert systems are often used for real time decision support to negotiate the complexities of scheduling. Most traditional approaches to production planning invariably encounter problems. The complex nature of scheduling often results in many shop floor constraints being ignored. Scheduling is often carried out only at an aggregate level with many potential resource conflicts being overlooked. However there is a substantial body of empirical knowledge connecting situations to actions in the domain of production scheduling, a situation that points to the use of expert systems. Many scheduling decisions depend upon an ability to predict the effects of implementing certain decisions. For example scheduling one part on a given sequence of machines will effect the progress of other parts scheduled on the same machines. There is a requirement for the PPCS to use domain modelling techniques (simulation) to generate information about the potential future states of the production system. Integrating expert systems and simulation within PPCS allows more accurate modelling of the decision making process while taking account of how various decisions interact and conflict with each other. The authors are involved in on-going work using expert systems & simulation tools together to enhance PPCS /1/. Figure 1 shows the approach with expert systems and simulation incorporated at each of the three progressive phases of PPCS. This paper describes the work done in this area using TIRS as the expert shell. Figure 1 The Proposed Production Planning & Control System Architecture THE PRODUCTION MANAGEMENT EXPERT SYSTEM In this project MODSIM (from CACI), an object oriented simulation language, is being used as the simulation tool and TIRS, the integrated reasoning shell from IBM, is being used to encode the expert knowledge at the three PPCS subfunction levels shown in figure 1. TIRS is an OS/2 based tool so the development work is in OS/2 using IBM's C Set++ for overall system integration. The Expert System takes an order entry at the Programming level where it is assigned a factory order number. The Programming level calls rules from the Ordering level for rough cut capacity planning. These rules indicate if the order is realisable with available resources. The rules are associated with the Ordering level as logically this is where most of the loading and capacity checking is done. If the order is unrealisable by the required date this information is fed back to the user who can move the order back in the program or decide upon other means of realising the due-date e.g. over-time or sub-contracting work out. Orders are assigned a priority (0-100), with a default of 50. The user can adjust the priorities. At the Ordering level the requirements of products, assemblies and parts are tallied. and netted against stock. Purchase orders and shop orders are raised. At this level two approaches are possible. The expert system can be interfaced to a classical MRP type system. For simple manufacturing environments the MRP functionality can be encoded directly in the expert system. The integration of the expert system rules with an MRP architecture means that the rigid fixed lead times in the MRP data bases for all parts can be replaced by dynamic rule based lead time estimation. The use of fixed lead times is one of the major flaws with MRP type architectures. For most piece part manufacture processing times represent a small fraction of the throughput time for parts and assemblies. Most of the time parts spend queuing or waiting for transport to the next operation. By using fixed lead times MRP fixes one of the variables that should be controlled to optimise production. Most of the concentration of effort in JIT is in reducing lead times. In this project the MRP functionality is coded in the expert system directly. The system development is modelled on a company producing electronic pagers. The product complexity typical in electronic assembly, with limited numbers of nested subassemblies means the complexity of the bill of materials and hence the production coordinating problem is reduced. The rules used at this level of the PPCS are designed to ensure fast throughput and low inventory operation or JIT. When orders are nearing production start dates they are released to the dispatching sub-function of the PPCS. Sequencing and scheduling rules similar to the OPT philosophy /2/ and PBC scheduling with group technology /3/ are used. Bottlenecks on the flexible production lines are identified. Other machines and processes in the lines are forward and backwards scheduled from the bottleneck. The Dispatching rules aim to keep the bottlenecks working and not let them be starved. Parts with long throughput times are also identified. The rules also to keep these parts moving. They are prioritised. Other parts are sequenced to fill out the schedule. The knowledge base has four logical divisions. The first contains all globally accessible data and defines the external routines that supply information to the knowledge base. The other three divisions of the knowledge base relate, one each, to the three sub-functions of the PPCS. CONCLUSIONSThe authors are involved in on-going research into the enhancement of PPCSwith the integration of Expert Systems and Simulation. This paper outlines the workdone in using TIRS as the expert system tool in this work. System development to-datehas been modelled on the production environment of an electronics company but willbe expanded. Early testing has shown promising results. REFERENCES1. Toal D, Smith P. Integrating Expert Systems with Simulation for ProductionPlanning. The Second World Congress on Expert Systems, Lisbon, 19942. Goldratt E M & Cox J, The Goal, Excellence in manufacturing, North River PressIncorporated, US, 1984.3. Burbidge J L, Operations Scheduling with GT and PBC. Int. J. Prod. Res. 1988.Vol. 26. No. 0.