Global Retail Giant (unnamed, Fortune 100)

In large-scale retail, inventory miscalculation compounds quickly across thousands of SKUs and geographies — excess stock ties up capital while stockouts push customers to competitors. This Fortune 100 retailer, operating 2,000+ stores across 40 countries with $90B+ in annual revenue, faced a structural forecasting failure: legacy ERP systems generated static, backward-looking demand signals incapable of differentiating between store-level demand in divergent markets. The result was a 22% inventory mismatch rate across the network, $230M locked in dead stock, and cascading downstream costs in excess warehousing, markdown cycles, and last-mile delivery — all symptoms of infrastructure built for a simpler, slower retail era.

IBU Consulting led the engagement, beginning with a data consolidation effort that cleaned and unified 6 TB+ of historical sales, point-of-sale, and logistics records spanning the retailer's global network. On that foundation, ML-based predictive demand models were trained across 17,000+ SKUs, incorporating geo-specific demand signals alongside external data streams — weather patterns, regional holidays, social media sentiment, and macroeconomic indicators — to capture hyper-local demand variability that static ERPs could not represent. A unified real-time analytics control tower was deployed to provide live inventory visibility across distribution hubs and surface AI-driven auto-replenishment recommendations to planners. Critically, digital twins of 12 key distribution hubs enabled scenario modeling — allowing teams to stress-test replenishment strategies before committing — reducing both lead time exposure and dead stock accumulation at scale.

The headline outcome was a 60% reduction in dead stock, from $230M to $92M within six months of deployment, freeing substantial working capital previously locked in unsellable inventory. Forecast accuracy climbed from 68% to 91%, giving merchandising and procurement teams reliable SKU-level forward visibility for the first time. Inventory accuracy improved from 74% to 95%, reducing manual reconciliation overhead and enabling tighter replenishment cycles across the distribution network. Delivery SLA compliance rose from 82% to 97%, reflecting improved stock positioning across stores.

• Dead stock volume: $230M → $92M (−60%), within 6 months
• Forecast accuracy: 68% → 91%
• Inventory accuracy: 74% → 95%
• Delivery SLA compliance: 82% → 97%

• Digital twin simulation of distribution hubs enables planners to model and stress-test replenishment scenarios before execution — reducing the cost of inventory positioning errors at enterprise scale.
• Fusing external signals (weather, regional holidays, social sentiment, economic indicators) with internal POS data produces materially better hyper-local forecasts than sales history alone.
• Clean, unified data is a prerequisite, not a parallel workstream — the 6 TB+ consolidation effort was foundational to model accuracy.
• The transition from static ERP forecasting to adaptive SKU-level ML models can unlock nine-figure working capital improvements for retailers with large, geographically distributed store networks.