Supply chain data integration: a literature review

Abstract

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.