Predictive Replenishment to Cut Stockouts

A mid-market retailer carries thousands of SKUs across stores and e-commerce, but seasonal spikes, promotions, short supplier lead times, and manual reorder rules cause frequent stockouts and overstocks. That results in lost sales, emergency air shipments, frequent markdowns, and bloated safety stock buffers.

Build a production pipeline that converts sales, inventory, fulfillment, and external signals into probabilistic demand forecasts and then optimizes orders against service-level and cost objectives, with planners in the loop for exceptions and governance on model behavior.

• Data: POS/OMS/warehouse telemetry, shipment lead times, promotions, web traffic and search trends, local weather, and supplier constraints.
• Models & tools: probabilistic forecasting (quantile regression / gradient-boosted trees or time-series deep learning) plus causal uplift adjustments for promotions; use MLOps for versioning and automated backtests.
• Optimization: stochastic replenishment optimizer that minimizes expected stockout + holding costs under service-level constraints and supplier lead time uncertainty.
• Human-in-the-loop: exception dashboard where planners review and override suggested orders, provide corrective labels, and approve safety-stock changes.
• Governance: performance SLAs, drift and calibration monitoring, explainability for key SKU decisions, and data contracts for upstream feeds.

Illustrative pilots typically reduce stockouts by 10-30% and trim excess inventory by 5-20%, while improving service levels by 2-10 percentage points. Financially, teams can expect lower emergency logistics and markdown costs and a measurable reduction in inventory carrying costs - often improving working capital usage within months of deployment depending on SKU mix and supply variability.