Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Supply chain data integration: a literature review

Supply chains (SCs) are dynamic networks subject to uncertainties and risks that may occur anywhere, anytime, and whose consequences affect the entities that comprise such SC, possibly affecting others. In fact, there are several examples wherein the occurrence of certain events resulted in considerable costs. Thus, it is important to ensure that SCs can apply preemptive measures, rather than just react to disruptions that may occur. Simulation tools may play an important role in achieving this, as these tools may be used to test alternative scenarios, as well as to quantify the impact of risks. To fully exploit this possibility, simulation tools should be used as data integration tools, so that the aforementioned analysis can be conducted using data from several relevant sources, thereby improving the quality of such analysis. In this regard, this paper proposes a Systematic Literature Review (SLR) of simulation methods that deal with risks in SCs, with particular emphasis on the type of data integration employed by such works. The obtained results show that researchers tend to simplify the problem at hand, without modeling their entire complexity, and failing to properly integrate data from the involved processes. The analyzed works’ compliance with Industry 4.0 (I4.0) revealed similar conclusions, as it was found that studies tend to disregard some of the main features of simulation in I4.0. In light of the obtained findings, literature gaps are identified, and future research directions are proposed.ESF - College of Environmental Science and Forestry, State University of New York(PDE/BDE/114566/2016