Inventory Optimization Engine

Our client, a regional retail chain operating forty-three stores across the Southeast, was caught in a familiar trap: too much of the wrong inventory and not enough of the right. Their buying decisions were driven by a combination of spreadsheet-based historical analysis and gut instinct from regional managers, a process that generated persistent overstock in slow-moving categories while leaving high-demand items frequently out of stock.

The financial impact was significant. The chain was carrying an average of two point four million dollars in excess inventory at any given time, tying up working capital that could have been deployed elsewhere. Simultaneously, stockouts were costing an estimated eight hundred thousand dollars per quarter in lost sales and customer goodwill. Store managers spent roughly fifteen hours per week manually reviewing stock levels and placing ad-hoc reorders, time that could have been spent on customer experience and team leadership.

Previous attempts to address the problem with basic reorder-point formulas and min-max thresholds had failed to account for the complex demand patterns the chain experienced, including seasonal shifts, local event-driven spikes, and cannibalization effects between nearby stores. The leadership team needed a solution that could model these dynamics and automate replenishment decisions at scale.

A key design principle was seamless integration with the chain's existing ERP and point-of-sale systems. Rather than requiring a platform migration, we built data connectors that pull transaction data in real time from their existing infrastructure and push reorder decisions back through their established procurement workflows. This approach minimized disruption during rollout and meant store teams could continue using familiar tools.

The demand forecasting models use an ensemble approach combining gradient-boosted trees for capturing complex feature interactions with time-series decomposition methods for seasonal pattern detection. Each model is automatically retrained weekly on the latest transaction data, ensuring predictions remain accurate as consumer behavior evolves. Model performance is monitored through automated drift detection alerts that flag when forecast accuracy drops below acceptable thresholds.

The operations dashboard was built on a no-code platform specifically chosen so the chain's internal IT team can modify views, add new KPIs, and create custom reports without relying on STC for ongoing changes. We conducted a two-day training workshop with the operations team to ensure they were fully comfortable administering and extending the dashboard independently.