Simulation of an automotive supply chain using big data

Abstract

Supply Chains (SCs) are dynamic and complex networks that are exposed to disruption, which have consequences hard to quantify. Thus, simulation may be used, as it allows the uncertainty and dynamic nature of systems to be considered. Furthermore, the several systems used in SCs generate data with increasingly high volumes and velocities, paving the way for the development of simulation models in Big Data contexts. Hence, contrarily to traditional simulation approaches, which use statistical distributions to model specific SC problems, this paper proposed a Decision-Support System, supported by a Big Data Warehouse (BDW) and a simulation model. The first stores and integrates data from multiple sources and the second reproduces movements of materials and information from such data, while it also allows risk scenarios to be analyzed. The obtained results show the model being used to reproduce the historical data stored in the BDW and to assess the impact of events triggered during runtime to disrupt suppliers in a geographical range. This paper also analyzes the volume of data that was managed, hoping to serve as a milestone for future SC simulation studies in Big Data contexts. Further conclusions and future work are also discussed.