Published at IMC21 Conference, Limerick, Ireland, September 2004, pages 156-163 MANUFACTURING SIMULATION: GOOD PRACTICE, PITFALLS, AND ADVANCED APPLICATIONS Leo J De Vin, Jan Oscarsson, Amos Ng, Mats Jägstam and Thomas Karlsson Centre for Intelligent Automation University of Skövde Box 408, 541 28 Skövde, Sweden tfn +46 500 448000, e-mail leo@ite.his.se ABSTRACT The paper describes manufacturing simulation with a focus on discrete event simulation and computer aided robotics. Some generic good practices, problems, and pitfalls in the use of simulation are described. Some advanced applications of manufacturing simulation are described and elucidated on the hand of a system for simulation-based service & maintenance. Simulation-based decision support and information fusion are closely related, and plans for novel synergistic research in these area are presented. KEYWORDS: Manufacturing simulation, Life cycle support, Information fusion. 1. INTRODUCTION Although there are various ways to carry out a simulation, with the word “simulation” one usually means “computer simulation”. In this case, it can be defined as: “The investigation of processes or conditions by the use of computers programmed to imitate them, for understanding or for training purposes”. In manufacturing system life cycle support, many intricate decisions have to be made that require specialist skills, knowledge and competence. Examples of decision types are manufacturing system design, operational planning, technical planning, service & maintenance, and new product introduction. Various tools for manufacturing simulation can be used to support the decision maker(s). Although each simulation project in principle requires its own problem-tailored and company-tailored approach, there are many commonalities that can be identified, regarding good practice as well as regarding potential pitfalls. Examples are problem definition, scope of the project, communication and reuse of results, data access and data quality. It should also be kept in mind that simulation is only a tool for decision support or analysis, and that it is no goal in itself. Many questions can be answered just as well without simulation. Presentation and documentation of the results are also very important and should be tailored to different stakeholder groups. The paper discusses these and other aspects with a focus on discrete event simulation (DES, “production flow simulation”, Figure 1, to the left) and continuous path simulation (“computer aided robotics”, CAR, Figure 1, to the right). Typical examples of traditional types of problems addressed with the use of these tools include off-line programming, collision checks, buffer-size optimisation and output capacity analysis. Emerging advanced applications include operational production scheduling, remote diagnostics and machine service support. In these applications,