MONTE CARLO SIMULATION BASED PERFORMANCE ANALYSIS OF SUPPLY CHAINS

Since supply chain management is one of the most important management practices that impacts the financial results of services and companies, it is important to optimize and analyze the performance of supply chains. Simulation provides a way to get closer to real life complex situations and uses less simplifications and assumptions than needed with analytical solutions. This paper proposes the application of Monte Carlo simulation based optimization and sensitivity analysis of supply chains to handle modeling uncertainties and stochastic nature of the processes and to extract and visualize relationship among the decision variables and the Key Performance Indicators. In this article the authors utilize their own interactive simulator, SIMWARE, capable to simulate complex multi-echelon supply chains based on simple configurable connection of building blocks. They introduce a sensitivity analysis technique to extract and visualize the relationships among the decision variables and key performance indicators. . The proposed robust sensitivity analysis is based on an improved method used to extract gradients from Monte Carlo simulation. The extracted gradients (sensitivities) are visualized by a technique developed by the authors. The results illustrate that the sensitivity analysis tool is flexible enough to handle complex situations and straightforward and simple enough to be used for decision support.