ABSTRACT
Current practices in production planning and scheduling involve deterministic approaches plus an assumed correction factor to account for unplanned random events based on historical data. This approach allows for inaccuracies in the rough-cut capacity planning (RCCP) which is used for the master schedule. The inaccuracies in the supply plan may result in lost sales, tardiness, low inventory turnover ratio, etc. and ultimately economic loss. In this study, the capabilities of manufacturing systems simulation (MSS), as a method to capture manufacturing complexities and risks through simulation, are analyzed and tested. Hierarchical decision model (HDM) methodology was used to assess the importance of perspectives and criteria around the selection of software based on the experts’ opinions. Accordingly, discrete element simulation (DES) software, Simio, was selected for simulation of manufacturing processes. A manufacturing model, based on make-to-order (MTO) production, was simulated to assess the ability of Simio in capacity planning and detailed scheduling optimization. The results showed that the feasibility of the production plan based on the monthly demand and randomly assigned due dates can be checked and the detailed scheduling decisions can be optimized to increase the production efficiency as well as reduce the tardiness of the production.
