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.

Simulation pulled by the need to reduce wastes and human effort in an intralogistics project

“…Simulation represents an excellent form of communication, as it uses visual models or graphical animations, making it possible to make representations of the dynamic systems more intelligible to the various interlocutors interested in analysing the system, as can be seen in many examples [39]- [41]. It is possible to simulate the physical incorporation of new technologies, such as AGVs within organizations, through 3D-simulation, which allows assisting and understanding the feasibility and risk associated with the incorporation of these new technologies in the organization [42], [43].…”

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

“…Although the integration of Big Data in risk assessment can notably provide a vital advantage for real-time data quality monitoring in I 4.0, the real-time processing of quietly variable amount of collected data demands not only new mechanisms and tools, but also the broadening of mathematical and statistical information, and AI innovations, primarily oriented to both quantitative and qualitative analysis of data. As a consequence, data interpretation and transformation into a high added-value become notably more efficient which currently requires elaboration and design of new skills and knowledge for the transformation of data into advanced industrial strategic resources and simulations of industrial supply chain [137].…”

Advanced Methodologies for Biomass Supply Chain Planning

“…A simulation model was used to perform the analysis based on different scenarios. Vieira et al [24] presented an integrated framework consisting of a simulation model and big data to analyze the impact of risk scenarios in an automotive supply chain. Data from multiple sources were collected and stored in a big data warehouse, then analyzed and used by a simulation model.…”