Supply Chain Simulation in a Big Data Context: Risks and Uncertainty Analysis

Abstract

Due to their complex and dynamic nature, Supply Chains are prone to risks that may occur at any time and place. To tackle this problem, simulation can be used. However, such models should use Big Data technologies, in order to provide the level of data and detail contained in the data sources associated to the business processes. In this regard, this paper considered a real case of an automotive electronics Supply chain. Hence, the purpose of this paper is to propose a simulation tool, which uses real industrial data, provided by a Big Data Warehouse, and use such decision-support artifact to test different types of risks. More concretely, risks in the supply and demand end of the network are analyzed. The presented results also demonstrate the possible benefits that can be achieved by using simulation in the analysis of risks in a Supply Chain.