Bypassing Data Issues of a Supply Chain Simulation Model in a Big Data Context

Supply Chains (SCs) are complex and dynamic networks, where certain events may cause severe problems. To avoid them, simulation can be used, allowing the uncertainty of these systems to be considered. Furthermore, the data that is generated at increasingly high volumes, velocities and varieties by relevant data sources allow, on one hand, the simulation model to capture all the relevant elements. While developing such solution, due to the inherent use of simulation, several data issues were identified and bypassed, so that the incorporated elements comprise a coherent SC simulation model. Thus, the purpose of this paper is to present the main issues that were faced, and discuss how these were bypassed, while working on a SC simulation model in a Big Data context and using real industrial data from an automotive electronics SC. This paper highlights the role of simulation in this task, since it worked as a semantic validator of the data. Moreover, this paper also presents the results that can be obtained from the developed model. (C) 2020 The Authors. Published by Elsevier B.V.