Dynamic Inventory Rebalancing for Just-In-Time Production

Multiple plants and suppliers feed tightly scheduled production lines, but demand volatility, long lead times, and sporadic supplier failures cause frequent near-term shortages and excess safety stock. Planners waste time chasing parts, creating expedited freight costs and unplanned downtime that reduce throughput and increase working capital.

Combine probabilistic short-term demand forecasts with supplier risk scoring and constrained optimization to produce ranked, actionable replenishment and transfer plans that a human planner reviews before execution. Integrate with ERP/WMS for real-time inventory state and with TMS for expedited transport options; use simulation to validate plans under plausible disruption scenarios and instrument auditing for regulatory and supply-chain governance.

• Use time-series and causal models (including event-aware ML) on transactional ERP, supplier ASN, shop-floor consumption, and shipment telematics to produce per-SKU, per-site demand distributions.
• Calculate supplier risk scores from lead-time variability, quality incidents, and external signals (port congestion, weather) and convert into probabilistic fill-rate forecasts.
• Run a constrained optimizer (mixed-integer) to propose transfers, expediting, or planned purchase changes that minimize total cost subject to service-level targets and transport constraints.
• Human-in-the-loop planning UI shows ranked proposals, root-cause evidence, and counterfactual simulations; planners approve, tweak, or reject before automated ERP change orders are issued.
• Governance: model versioning, backtesting dashboards, access controls, and automated alerts for plans that deviate from approved risk/cost thresholds.

Teams typically see 30-60% fewer line stoppages from parts shortages and reduce safety stock for critical SKUs by 10-25%, lowering working capital. Freight-expediting spend can fall by 20-40% and planner decision time drops from hours to minutes, enabling faster, evidence-backed tradeoffs between cost and service - results will vary by network complexity and data quality.