How predictive analytics cut retail stockouts by 50% and overstock 30%

A large retail chain struggled with manual, historic-average–based inventory forecasting, resulting in frequent stockouts, overstocking, and high costs. Meeting demand meant predicting it, not averaging the past.

The Challenge

Forecasting on historic averages couldn't keep up with real demand across a large store network. Four issues compounded:

1. 01

   Manual, historic-average forecasting. Simple averages spanned many stores and warehouses with no real prediction.

2. 02

   Overstock and stockouts. Frequent overstock in some locations, stockouts in others, at the same time.

3. 03

   Unpredictable demand swings. Seasonality, promotions, and local events created swings averages couldn't capture.

4. 04

   No automation. Manual processes caused delayed actions, high holding costs, and lost sales.

The chain wasn't short on data, it was short on prediction. Averages of the past couldn't anticipate a promotion, a season, or a local event. The engagement brief

The Solution

Periscope built a predictive analytics platform: machine-learning models that read every demand signal, surface decisions to planners, and trigger replenishment automatically, all on a monitored, cloud-native architecture.

ML demand forecasting. Models ingest past sales, the promotions calendar, local-event data, and supply-chain lead times to predict demand per location.

Dashboards & alerts. Purchasing and replenishment decisions are surfaced to planners with clear, timely alerts.

Auto-order triggers. Orders fire automatically when predicted inventory falls below thresholds.

Decoupled microservices. Components scale independently as stores and SKUs grow.

Cloud monitoring. Hosting with monitoring and logging tracks model drift and accuracy, keeping forecasts sharp.

The Results

Predicting demand instead of averaging it turned inventory from a cost center into a competitive advantage.

Higher availability. Stockouts fell 50%, protecting sales and customer satisfaction.

Lower holding cost. Overstock dropped 30%, freeing up working capital.

Data-driven decisions. Planners now rely on predictive insight rather than intuition.

Scalable & self-improving. The system absorbs new stores and SKUs without degradation, and models improve while support costs fall over time.

ML forecasting on every demand signal, with auto-order triggers and drift monitoring, cut stockouts 50% and overstock 30%, and keeps getting better. Outcome summary