Estimation of key performance measures in a manufacturing system via simulation

Every manufacturing facility is unique in a way that it facilitates manufacturing of different types of products and serves a variety of customers depending on their requirements. Although the characteristics and nature of each facility are different, the basic performance measures that are used to evaluate the facilities are common and these measures are often called as Key Performance Indicators (KPI). Examples of KPIs include average inventory, average backorder level, percentage fill rate and average cycle time. The KPIs give an indication of how well the system is performing. In this thesis, simulation is used as a tool to determine these KPIs to a specific scenario in a manufacturing industry. The entire operation within the industry is simulated using Arena. The KPIs such as average inventory, average backorder level, percentage fill rate and average cycle time are calculated from the simulation output.;The main objective of this thesis is to compare two different production strategies using simulation and recommend the one that optimizes the system for a given scenario. The first strategy is called Manufacturing Resource Planning (MRP), which is a method used by many industries. The second strategy is Dynamic Risk based Scheduling system (DRS). Two different simulation models are created to represent these two systems and the results are compared to establish which one performs better based on the KPIs. KPIs are used as a measure to identify the level of performance of the system. We also compare our simulation results with LPST (Lean Physics Support Tools) software developed by Factory Physics Inc. Our simulation results are consistent with the LPST results. Based on the results obtained from the two simulation models and the LPST software, we believe that the DRS model helps to optimize the system than the conventional MRP based system.