Imagine your car-parts supply chain as a highway system. A pandemic is like sudden roadblocks and accidents everywhere. This research looks at how AI can act like a smart traffic control center—constantly watching conditions, rerouting shipments, predicting future blockages, and suggesting backup routes and suppliers so parts still arrive on time.
Pandemics and similar disruptions break just‑in‑time automotive supply chains—causing stockouts, plant shutdowns, and excess costs because companies cannot see risks early, cannot re-plan fast enough, and lack robust contingency options. The paper surveys how AI methods can predict disruptions, optimize inventory and sourcing, and support resilient supply chain design and operations in such crises.
In practice, defensibility will come from proprietary operational data (demand, logistics, supplier performance), deeply integrated planning workflows, and organization-specific models and scenarios tuned to the company’s network and risk profile rather than from generic algorithms alone.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Data quality and integration across tiers of the supply chain; maintaining robust models under regime shifts (pandemics) and avoiding degradation when patterns change abruptly.
Early Majority
Focuses on AI specifically for pandemic and extreme-disruption contexts in supply chains—emphasizing resilience, scenario planning, and risk-aware optimization rather than only classical cost or efficiency optimization.
80 use cases in this application