Business Problem
A large retail organizationstruggled with inefficient merchandise inventory planning due to limitedvisibility at the store–SKU level. Inaccurate demand forecasts led tooverstocking in some locations, stockouts in others, and costly merchandiseredeployment across stores.
Actions Taken
· Implemented machine learning–based demandforecasting models at store–SKU granularity.
· Applied ARIMA and ARIMAX models at both SKUand aggregated product hierarchy levels.
· Generated store-specific quantity projectionsto support replenishment decisions.
· Integrated forecasting outputs into inventoryplanning workflows.
Outcomes Achieved
· Achieved 90+% accuracy at store leveland 85+% accuracy at product level.
· Improved accuracy of inventory positioningacross stores.
· Reduced inefficiencies caused by misalignedinventory allocation.
· Enabled more informed inventory planningdecisions at scale.
Question for the contact us form: Doyou lack visibility into what’s selling where?
