Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

TLDR

Big data analytics is increasingly used for forecasting business trends, and supply‑chain risk management—especially the prediction of social‑issue risks—is critical for sustainable enterprises. The study explores how big data analytics can mitigate supply‑chain social risk to support environmental, economic, and social sustainability. An expert panel and survey identified and validated supply‑chain social issues, and a case study illustrated how big data analytics can detect and mitigate those risks. Results show that big data analytics can predict a range of social problems—such as workforce safety, fuel consumption, health, security, vehicle condition, unethical behavior, theft, speeding, and traffic violations—demonstrating that information‑management actions can reduce social risks and advance sustainability.

Abstract

The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.