Using a simulation model for knowledge elicitation and knowledge management

The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process. This paper reports on the experience so far with using a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself. © 2004 Elsevier B.V. All rights reserved.

Using a simulation model for capacity management

A simulation model has been constructed of a valve manufacturing plant with the aim of assessing capacity requirements in response to a forecast increase in demand. The plant provides a weekly cycle of valves of varying types, based on a yearly production plan. Production control is provided by a just-in-time type system to minimise inventory. The simulation model investigates the effect on production lead time of a range of valve sequences into the plant. The study required the collection of information from a variety of sources, and a model that reflected the true capabilities of the production system. The simulation results convinced management that substantial changes were needed in order to meet demand. The case highlights the use of simulation in enabling a manager to quantify operational scenarios and thus provide a rational basis on which to take decisions on meeting performance criteria.

The development of a computer simulation model of the interaction between the supply and usage of major components in car assembly you use in stock control

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Modelling and improving human decision making with simulation

Modelling human interaction and decision-making within a simulation presents a particular challenge. This paper describes a methodology that is being developed known as 'knowledge based improvement'. The purpose of this methodology is to elicit decision-making strategies via a simulation model and to represent them using artificial intelligence techniques. Further to this, having identified an individual's decision-making strategy, the methodology aims to look for improvements in decision-making. The methodology is being tested on unplanned maintenance operations at a Ford engine assembly plant

Linking the witness simulation software to an expert system to represent a decision-making process

Expert systems, and artificial intelligence more generally, can provide a useful means for representing decision-making processes. By linking expert systems software to simulation software an effective means of including these decision-making processes in a simulation model can be achieved. This paper demonstrates how a commercial-off-the-shelf simulation package (Witness) can be linked to an expert systems package (XpertRule) through a Visual Basic interface. The methodology adopted could be used for models, and possibly software, other than those presented here.

Using simulation for facility design: a case study

A discrete event simulation model was developed and used to estimate the storage area required for a proposed overseas textile manufacturing facility. It was found that the simulation was able to achieve this because of its ability to both store attribute values and to show queuing levels at an individual product level. It was also found that the process of undertaking the simulation project initiated useful discussions regarding the operation of the facility. Discrete event simulation is shown to be much more than an exercise in quantitative analysis of results and an important task of the simulation project manager is to initiate a debate among decision makers regarding the assumptions of how the system operates.

Using DEA and simulation in guiding operating units to improved performance

The potential for the use of DEA and simulation in a mutually supporting role in guiding operating units to improved performance is presented. An analysis following a three-stage process is suggested. Stage one involves obtaining the data for the DEA analysis. This can be sourced from historical data, simulated data or a combination of the two. Stage two involves the DEA analysis that identifies benchmark operating units. In the third stage simulation can now be used in order to offer practical guidance to operating units towards improved performance. This can be achieved by the use of sensitivity analysis of the benchmark unit using a simulation model to offer direct support as to the feasibility and efficiency of any variations in operating practices to be tested. Alternatively, the simulation can be used as a mechanism to transmit the practices of the benchmark unit to weaker performing units by building a simulation model of the weaker unit to the process design of the benchmark unit. The model can then compare performance of the current and benchmark process designs. Quantifying improvement in this way provides a useful driver to any process change initiative that is required to bring the performance of weaker units up to the best in class. © 2005 Operational Research Society Ltd. All rights reserved.

Simulation modelling for business

Simulation modelling has been used for many years in the manufacturing sector but has now become a mainstream tool in business situations. This is partly because of the popularity of business process re-engineering (BPR) and other process based improvement methods that use simulation to help analyse changes in process design. This textbook includes case studies in both manufacturing and service situations to demonstrate the usefulness of the approach. A further reason for the increasing popularity of the technique is the development of business orientated and user-friendly Windows-based software. This text provides a guide to the use of ARENA, SIMUL8 and WITNESS simulation software systems that are widely used in industry and available to students. Overall this text provides a practical guide to building and implementing the results from a simulation model. All the steps in a typical simulation study are covered including data collection, input data modelling and experimentation.

The case for the organisational use of simulation

This paper describes the use of simulation in a large manufacturing organisation and analyses how it could have been used more fully. Although the benefits from using simulation were clear, it was seen that the technique could have been deployed more effectively by considering the organisational context of the use. From the analysis of the case study, certain recommendations are made on how to maximise the benefits from simulation within the organisation, thereby extending the benefits from what is often a considerable investment in developing a simulation model. Recent developments in simulation software in terms of improvements in usability and integration with organisational data are seen to increase the feasibility of a wider role for simulation if the organisational barriers discussed can be overcome.

Using process mapping and business process simulation to support a process-based approach to change in a public sector organisation

A case study demonstrates the use of a process-based approach to change regarding the implementation of an information system for road traffic accident reporting in a UK police force. The supporting tools of process mapping and business process simulation are used in the change process and assist in communicating the current process design and people's roles in the overall performance of that design. The simulation model is also used to predict the performance of new designs incorporating the use of information technology. The approach is seen to have a number of advantages in the context of a public sector organisation. These include the ability for personnel to move from a traditional grouping of staff in occupational groups with relationships defined by reporting requirements to a view of their role in a process, which delivers a performance to a customer. By running the simulation through time it is also possible to gauge how changes at an operational level can lead to the meeting of strategic targets over time. Also the ability of simulation to proof new designs was seen as particularly important in a government agency were past failures of information technology investments had contributed to a more risk averse approach to their implementation. © 2004 Elsevier Ltd. All rights reserved.

A simulation study of a localised computer network

An investigation is carried out into the design of a small local computer network for eventual implementation on the University of Aston campus. Microprocessors are investigated as a possible choice for use as a node controller for reasons of cost and reliability. Since the network will be local, high speed lines of megabit order are proposed. After an introduction to several well known networks, various aspects of networks are discussed including packet switching, functions of a node and host-node protocol. Chapter three develops the network philosophy with an introduction to microprocessors. Various organisations of microprocessors into multicomputer and multiprocessor systems are discussed, together with methods of achieving reliabls computing. Chapter four presents the simulation model and its implentation as a computer program. The major modelling effort is to study the behaviour of messages queueing for access to the network and the message delay experienced on the network. Use is made of spectral analysis to determine the sampling frequency while Sxponentially Weighted Noving Averages are used for data smoothing.

Knowledge elicitation supported by simulation

The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process. This paper reports on the experience so far with using a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself.