Data Analytics Applications in Supply Chain Resilience and Sustainability Management: The State of the Art and a Way Forward

Digital technologies have become a cause célèbre among academics and practitioners as great tools capable of building resilient and sustainable supply chains. This chapter provides an overview of the recent developments in data analytics applications in supply chain resilience (SCR) and supply chain sustainability (SCS). There has been an exponential growth of literature on data analytics applications for SCR and SCS, with a particularly notable increase observed since 2015. In this systematic literature review, we find that both SCR and SCS research are concentrated around five main themes: (1) data analytics capabilities, (2) role of Industry 4.0, (3) blockchain adoption, (4) big data analytics, and (5) machine learning. Closed-loop supply chain design and circular economy are of unique focus in SCS, while digital twin only emerged as a research theme in SCR research. The underlying themes in SCR contexts are more dispersed than in SCS, mostly due to the comparative maturity of SCS research. In light of the promising developments in data analytics applications for SCR and SCS, promising avenues for future inquiry are the design of effective data-sharing incentive mechanisms, and the utilization of big data from social media platforms, yielding valuable insights for both research and practitioners.