Client Study

A leading beverage company in the U.S. faced a critical challenge: identifying stock-outs across the thousands of retail stores they served. While their sales teams and distributors provided some feedback, most stock-out issues went unnoticed until it was too late. Despite having near real-time visibility into SKU-level deliveries from distributors, this information alone wasn’t sufficient to reveal gaps in on-shelf availability. The company needed a smarter, proactive way to detect and address stock-outs before they impacted sales and consumer satisfaction.

The company partnered with Stock-Warning to implement an AI-driven early warning system. Our solution combined two key data streams:

• Supply side: real-time delivery information by SKU at the store level.

• Demand side: dynamic demand modeling at a micro-regional level, down to individual SKUs.

By integrating these signals, the system continuously modeled the stock balance for each store, highlighting risks where demand was outpacing supply. This statistical, AI-powered approach enabled the client to spot emerging stock-outs much earlier—without the need for intrusive retailer integration or expensive in-store hardware.

The impact was immediate and measurable:

• 3x more stock-outs detected compared to traditional methods.

• Faster resolution times, ensuring product availability before sales were lost.

• Improved collaboration between the client’s logistics and sales teams, who could now act on accurate, store-level alerts.

Ultimately, the beverage company gained a competitive edge by reducing missed sales opportunities and strengthening customer loyalty through consistent product availability.