Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model. Issue 5 (3rd April 2020)

• Record Type: Journal Article
• Authors: Chehbi-Gamoura, Samia; Derrouiche, Ridha; Damand, David; Barth, Marc
• Is Part Of: Production planning & control. Volume 31:Issue 5(2020)
• Page Start: 355
• Page End: 382
• Publication Date: 2020-04-03
• Subjects: Big Data Analytics -- supply chain management -- SCOR matrix -- nomenclature grid; Production planning -- Periodicals; Production control -- Periodicals; 658.5
• DOI: 10.1080/09537287.2019.1639839
• Languages: English
• ISSNs: 0953-7287

Abstract

Abstract: 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.