Improve inventory management with machine learning

Client Snapshot

Blueprint created sales forecasting and inventory prediction models powered by machine learning that give a national retailer with thousands of locations results that match the company’s historical manual counts. The models will allow the company to conduct virtual inventory counts at any time for any given store to better manage inventory and shrinkage, while also reducing unnecessary manual labor and spend.

Work Summary

The problem

A nationwide discount retailer was struggling to maintain inventory accuracy and manage shrinkage across its thousands of retail locations. Infrequent physical inventory checks, a lack of shipment and SKU tracking from distribution to store and shrinkage due to product damage or theft had left the company unable to effectively manage inventory at the individual store or company-wide level. In retail, not having an item a customer expects can have devastating ripple effects.

“When customers walk into a physical store, they expect a better level of customer service, adjacency of products and immediacy compared to shopping online. If an item is out of stock, the customer leaves,” said Blueprint Vice President of Technology. “The retailer loses its whole value prop, the immediate revenue and, potentially, a great deal of future revenue.”

The Blueprint Way

Blueprint leveraged Azure Databricks and Power BI to deploy advanced analytics and machine learning with the client’s sales and operations data. In less than 90 days, Blueprint created machine learning models that outperformed historical inventory models in addition to a very accurate sales-forecasting model. The dashboards and reports built from these forecasts allow the company to see a virtual inventory count at any time for any given store, reduce unnecessary spend, and better manage inventory and shrinkage. 


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