Big Data Analytics Empowering Supply Chain Risk Management: A Case Study of JD Logistics

Abstract

Global supply chains are constantly hit by unpredictable disruptions, including extreme weather and transport congestion, rendering conventional experience-based risk management slow and passive. Lean TPS effectively reduces operational variations yet struggles with fragmented data and limited end-to-end visibility across logistics networks. Relevant empirical research on combining big data analytics and lean risk control within large domestic logistics providers still remains insufficient. This study examines how Big Data Analytics Capability (BDAC) can strengthen Lean/TPS-based risk governance in JD Logistics by improving end-to-end visibility, speeding detection, and enabling proactive, data-driven responses. Using JD Logistics as a focal case, this paper explores how technologies like JD Super Brain and digital twins translate Lean concepts—jidoka, standard work, and Kaizen—into scalable, real-time capabilities across regions and partners. The findings reveal that while BDAC enhances sensing and cadence, persistent gaps remain in emergency escalation, cross-regional handoffs, and learning diffusion. Durable resilience emerges when predictive analytics are tightly integrated with scenario-based standard work, unified data governance, and a network-wide Kaizen pipeline.