Discrete-event simulation (DES) has mainly been used as a production system analysis tool to evaluate new production system concepts, layout and control logic. Recent developments have made DES models feasible for use in the day-to-day operational production and planning of manufacturing facilities. Operative simulation models provide manufacturers with the ability to evaluate the capacity of the system for new orders, unforeseen events such as equipment downtime, and changes in operations. A simulationbased Decision Support System (DSS) can be used to help planners and schedulers organize production more efficiently in the turbulent global manufacturing. This paper presents the challenges for development and the efforts to overcome these challenges for the simulation-based DSS. The major challenges are: 1) data integration 2) automated simulation model creation and updates and 3) the visualization of results for interactive and effective decision making. A recent case study is also presented. Agile, fast and flexible production networks are a must for companies facing today's global competition. The connections between manufacturing systems and processes are becoming more complex and the amount of data required for decision-making is growing. In order to make decisions related to manufacturing, engineering and production management, multiple parameters must be considered. Agile production requires a management and evaluation tool for changes to production, manufacturing system development, configuration and operations planning. As shown in this study, a Decision Support System (DSS) based on manufacturing simulation is one suitable solution. Delivering on the stated order date is a key element in customer satisfaction. One of the important decisions in production planning is the scheduling and synchronization of activities, resources and material flow. Old static production planning methods are not adequate; production planners need accurate and dynamic models of production, i.e. a simulation model that uses a production network and real shop floor data in near real-time. Given the current global competition and in lean and agile manufacturing, material stocks are kept as small as possible, while expensive resource utilization should be kept as high as possible. Production personnel must seek a balance between customer orders and limited resources. Every new order upsets the current balance (Figure 1.) .
Heilala et al. REF developed a simulation-based decision support system as an operative simulation model that enables to handle unforeseen events such as downtime and changes in operations.
Developing simulation-based Decision Support Systems for customer-driven manufacturing operation planning
Proceedings of the 2010 Winter Simulation Conference
Proceedings of the 2010 Winter Simulation Conference
Computer Science