A semiconductor distributor was managing inventory across 12 warehouses with spreadsheets and instinct. Demand forecasts came from sales team estimates, adjusted by a planner's judgment, refreshed monthly. The result was predictable: excess stock in some locations, stockouts in others, and a lot of expedited shipments to compensate. Working capital was tied up in inventory that wouldn't move, while in-demand SKUs were back-ordered.
A Power BI plus Azure ML demand forecasting solution that ingests three years of historical sales, customer order patterns, supplier lead times, and external signals (semiconductor industry indices, customer earnings cycles). The model produces SKU-by-location demand forecasts updated daily, with confidence intervals so the planning team knows where to trust the forecast and where to apply judgment. Built directly into the planners' existing Power BI workflow — no new tool to learn, just better numbers.
40% reduction in excess inventory across the warehouse network within six months, with no increase in stockouts. Working capital previously tied up in slow-moving inventory was redeployed to expand high-margin product lines. Expedited shipping costs dropped sharply as the forecast caught demand shifts weeks ahead of when humans would have noticed. Planners now spend their time on exception handling and supplier negotiation, not on building forecasts from scratch every month.
Distribution businesses live and die on inventory accuracy, and most still run forecasts on a monthly cadence with manual adjustment. Daily ML-driven forecasts with confidence scores transform inventory from a defensive line item into a competitive advantage. The pattern works for any business with a lot of SKUs, multiple locations, and historical demand signal — including parts distribution, medical supply, and specialty chemicals.