Manufacturing, engineering and production management decisions involve the consideration of multiple parameters. These often complex, interdependent factors and variables are too many for the human mind to cope with at one time. Agile production needs a management and evaluation tool for production changes, manufacturing system development, configuration and operations planning. A decision support system based on manufacturing simulation is one suitable solution. 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 development has enhanced DES models for use in the day-to-day operational production planning of manufacturing facilities. These "as built" 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. After a simulation model has been built, experiments are performed by changing the input parameters and predicting the response. Experimentation is normally carried out by asking "what-if" questions and using the model to predict the likely outcome. A simulation-based Decision Support System (DSS) can be used to augment the tasks of planners and schedulers to run production more efficiently. This chapter sheds light on development challenges and current development efforts to solve these challenges for this data and model-driven DSS. The major challenges are: 1) data integration, 2) automated simulation model creation and updates, and 3) visualisation of results for interactive and effective decision making.
|Title of host publication||Decision Support Systems|
|Subtitle of host publication||Advances in|
|Place of Publication||Kroatia|
|Publication status||Published - 2010|
|MoE publication type||A3 Part of a book or another research book|
Heilala, J., Montonen, J., Järvinen, P., & Kivikunnas, S. (2010). Decision Support Using Simulation for Customer-Driven Manufacturing System Design and Operations Planning. In G. Devlin (Ed.), Decision Support Systems: Advances in (pp. 235-260). InTech. https://doi.org/10.5772/39400