Production Planning & Control · 2019 · 138 citations · 180 references
ManagementData ScienceSupply Chain ManagementBig Data AnalyticsData AnalyticsSupply Chain AnalyticsSupply Chain Decision Support SystemsSupply Chain DigitizationNomenclatureStructured Literature Review
SCM and BDA together offer many research opportunities, yet the application of analytics to supply chain processes remains unclear for academics and industry. The study presents a structured literature review of BDA methods in SCM using the SCOR model to align process needs with analytics capabilities. The authors review literature from 2001 onward to develop a taxonomy, nomenclature grids, and a SCOR‑BDA matrix. The review reveals a clear disparity and misalignment between data scientists and supply chain managers, underscoring the urgent need for collaborative efforts and highlighting future research tracks and gaps.
When supply chain management (SCM) intersects with Big Data Analytics (BDA), uncountable opportunities for research emerge. Unfortunately, how analytics can be applied to supply chain processes is still unclear for both academics and industries. To better connect SC processes needs and what BDA offer, we present a structured review of academic literature that addresses BDA methods in SCM using the supply chain operations reference (SCOR) model. The literature since 2001 is reviewed to provide a taxonomy framework resulting in a nomenclature grids and a SCOR-BDA matrix. The most important result of this paper indicates a clear disparity and points to an urgent need to bring the efforts closer in a collaborative way for more intelligent use of BDA in SCM. Furthermore, this paper highlights a misalignment between data scientists and SC managers in BDA applicability. It also highpoints upcoming research tracks and the main gaps that need to be stunned.
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