Inventory Forecasting and Replenishment Automation
Manual inventory forecasting led to $400K in excess inventory and frequent stockouts on high-turnover items
Context
Wholesale distributor with 25,000 SKUs, 8 supplier relationships, average 90-day inventory turnover. Inventory manager spent 3 days monthly creating Excel-based demand forecasts using historical sales data. Forecasts were static—same forecast used for entire month, no adjustment for trends or seasonality. Result: Overstocking slow-moving items (tied up $400K in capital), frequent stockouts on fast-moving items (lost sales). Lead times from suppliers varied (14-45 days), but forecasts didn't account for supplier-specific timelines. Seasonal patterns existed but weren't systematically incorporated into forecasts.
The Real Problem
Manual forecasting process was time-consuming and error-prone—Excel formulas were complex, small errors cascaded into large forecast inaccuracies. Forecasts based on simple historical averages didn't account for trends (items selling faster or slower over time). No consideration of seasonality—same forecast used year-round despite clear seasonal patterns in some categories. Supplier lead times weren't factored into reorder timing—orders placed based on current stock level, not anticipated demand. Safety stock calculations were arbitrary—same percentage applied to all SKUs regardless of demand variability. Off-the-shelf inventory management platforms had basic forecasting but couldn't handle business-specific rules (supplier minimum orders, category-specific policies). No integration with sales pipeline—forecasts didn't account for known future orders or sales team projections. Budget constraint: enterprise forecasting software was expensive ($50K+ annually) and required extensive configuration.