Retail Inventory Automation

Fresh and short-life inventory created avoidable loss, but the available point-of-sale data did not give management a consistent view of which products, categories, and locations were driving the pattern. The retail operation also supported a broader institutional sustainability mission, making waste reduction an operating and environmental priority.

A repeatable process was needed to accept new data, retain history, distinguish recurring waste signals from one-time incidents, and evaluate whether new products generated demand without producing disproportionate waste.

The system ingested NCR CounterPoint point-of-sale data and normalized inconsistent product, store, category, and date fields into a shared tracking structure. Validation checks prevented missing or malformed files from silently distorting the history.

Location-aware views made it possible to compare damage-out and expiration behavior without rebuilding the analysis for each store.

Each file passed structure and required-field checks before loading. The process normalized stores, products, categories, dates, and transaction states, retained repeated observations so trends remained visible, and produced location and category views for recurring management review. The resulting history also supported before-and-after comparisons when new products or assortment changes were tested.

Point-of-sale data reflects how staff use the system, so the workflow surfaced missing exports, inconsistent entry, and differences between recorded damage-outs and actual handling practices.

Historical comparison reduced the chance that an unusual week would drive a permanent product decision. Managers could compare sales with expiration and damage-out behavior before changing ordering, placement, or assortment, and could evaluate whether a new item added demand without adding avoidable waste.

The chain gained a practical feedback loop for fresh-product decisions. Management could compare locations, identify products that required attention, and test additions or assortment changes without manually reconstructing the data each time. The same measures supported the operation’s wider effort to reduce food and packaging waste through better purchasing decisions.