Local inventory management policies for optimal global performances in a decentralized supply chain

The optimal calibration of inventory management policies in a multi-echelon linear supply chain (SC) constitutes a complex problem. The difficulty to provide a clear and unique answer leads to frustration in using serious games for educational purposes. In this work, a simulation-based optimization approach, using a dynamic tabu search metaheuristic, is developed and applied to a specific case of a four-echelon linear SC. A stochastic demand with constant mean is used and different service levels to the market are considered. The results indicate significant differences of the required on-hand inventory, cost contributions and cost element importance between the different echelons. The non-linear influence of the service level on the various parameters and costs is also observed. The obtained results are consistent and provide a clear answer to the stated problem. The methodology developed can be easily generalized to other cases