Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda

Abstract

Big data and predictive analytics (BDPA) tools and methodologies are leveraged by businesses in many ways to improve operational and strategic capabilities, and ultimately, to positively impact corporate financial performance. BDPA has become crucial for managing supply chain functions, where data intensive processes can be vastly improved through its effective use. BDPA has also become a competitive necessity for the management of supply chains, with practitioners and scholars focused almost entirely on how BDPA is used to increase economic measures of performance. There is limited understanding, however, as to how BDPA can impact other aspects of the triple bottom-line, namely environmental and social sustainability outcomes. Indeed, this area is in immediate need of attention from scholars in many fields including industrial engineering, supply chain management, information systems, business analytics, as well as other business and engineering disciplines. The purpose of this article is to motivate such research by proposing an agenda based in well-established theory. This article reviews eight theories that can be used by researchers to examine and clarify the nature of BDPA’s impact on supply chain sustainability, and presents research questions based upon this review. Scholars can leverage this article as the basis for future research activity, and practitioners can use this article as a means to understand how company-wide BDPA initiatives might impact measures of supply chain sustainability.