| Summary: | The dynamic and complex nature of Supply Chains exposes these networks to disruptive events, which have consequences that are hard to quantify. To avoid such problems, simulation may be used, as it allows the uncertainty and dynamic nature of systems to be considered. Furthermore, such systems comprise several processes, with the respective sources generating data with increasingly high volumes and velocities. This data can add the required level of detail to the simulation models, hence paving the way for the development of simulation models in Big Data contexts. This thesis analyzes the real case of an automotive electronics Supply Chain and proposes a Decision-Support System, supported by a Big Data Warehouse and a simulation model, with this integration being aligned with Industry 4.0. The first stores and integrates data from multiple sources and the second reproduces movements of goods and information exchanges from such data, incorporates risks and allows their impact to be analyzed. The results of this thesis revealed that, albeit the organization, where this thesis was inserted, has technological conditions (e.g., information system) and reference business processes, several data problems were observed. Simulation is used to detect and bypass some of these issues, since those incoherencies were only identified by the needs inherent to the use of simulation. The identification of such issues in this excellence environment suggests the novelty of this research and that similar projects may experience equivalent problems. Hence, a classification of the identified data issues is proposed, serving as a milestone for future similar projects. Having bypassed such data issues, simulation is used to support the decision-making, allowing to: (1) run simulations using the stored data; (2) incorporate risks either by allowing users to fire disruptions in runtime, or by applying statistical distributions to model variability in supply or demand; (3) adapt the simulation model in real-time to eventual data changes; (4) load the real system’s state from the data to analyze specific time slots without having to wait for the simulation to reach the time to start the analysis; (5) and allow the simulation to run beyond the time considered in the stored data, adding, thus, predictive features to the simulation models.
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