Discrete Event Simulation for Aftermarket Supply Chain

Abstract: The planning of an Aftermarket Supply Chain is a very complex task. This is due to an unpredictable demand which is driven by the need for maintenance and repair. This drive translates to a high variety of lead times, a large number of stock-keeping units (SKUs) and the capacity to deliver spare parts during its full lifecycle. With all these complexities in place, optimizing and parametrizing the planning process is a difficult and time-consuming task. Moreover, the current optimization tool focuses only on one node (each warehouse individually) of the whole Supply Chain, without considering the information such as inventory levels of the other nodes. Hence, the Supply Chain is not completely connected, making it difficult to get a better understanding of the system performance to identify cost draining areas. This leads to capital being tied up in the upper stream of the Supply Chain and later adding unnecessary costs like high inventory costs, rush freight costs, return or scrapping cost. In this study, Discrete Event Simulation (DES) is explored as an additional optimization tool that could analyse and improve the performance of the whole Supply Chain. To do that, the functioning of a node is modelled by replicating the logics behind the flow of material, which includes analysing some manual workflows which are currently present. In Addition, all the information needed from the orders, order lines and parts are mapped. The later part of the study aims to connect all the nodes to form a whole overview of the Supply Chain and further perform optimizations globally.  As an outcome, Multi-Echelon Inventory Optimization has been performed on the whole Supply Chain after connecting all the nodes and thus getting an overview. Furthermore, the impact of different parameters has been studied on the whole model to understand the sensitivity of parameters such as variations in lead time and demand. Finally, different what-if scenarios such as COVID and problems with delay in suppliers were studied, which could help understand the impact of unforeseen situations.