EV Manufacturing: Autonomous Supply Chain with Auditable Boundaries
Deployed intelligent supply chain agents for EV production across 8 global plants with real-time risk escalation
The Challenge
Global EV manufacturer faced three critical gaps: (1) Unpredictable supplier lead times caused production line delays costing $2M/day. (2) Zero visibility into which decisions caused line stoppages—manufacturing teams didn't trust any system recommendations. (3) Regulatory traceability for critical component sourcing was manual, incomplete, and incompatible with audits. (4) Production planners made sequential decisions without constraint visibility, causing cascading failures 5 weeks into the cycle.
Our Approach
Our approach: Skill Assetization + Governance-by-Design. We encoded manufacturing expertise into Decision Skills: 'Supplier Performance Monitoring' (flagging at-risk suppliers 3 weeks early), 'Component Allocation' (distributing scarce components optimally across plants), 'Production Sequence Optimization' (respecting supply constraints). Each Skill logs reasoning, inputs, and decisions. Risk rules are explicit: if a supplier's lead time exceeds 30 days, auto-escalate; if inventory drops below 10 days of supply, trigger contingency supplier activation. Agents recommend; manufacturing directors approve (with full decision audit trail). No 'black box' models—every recommendation explains its logic and boundary conditions.
The Outcome
Supplier-induced line delays reduced by 72%. Production planners now spend 60% less time on reactive firefighting (freed 45 FTEs for innovation). Auditors can now trace every critical sourcing decision to its rationale. Production cycle variability dropped 35%. Critically: trust increased—when the system recommends a decision, teams understand why, and can validate it against their judgment. Total implementation: 20 weeks across 8 plants.