Case Study

Case Study

Mapping and Matching Critical Retailer Business Data

Mapping and Matching Critical Retailer Business Data

Adeptia’s AI enabled a major U.S. food company to automate 70% of its data exchange

A major U.S. food company found itself unable to maintain a line of sight on its inventory or sales data, with no way to automatically match the data it was receiving from retailers. The company was forced to rely on a 10 percent sample size to estimate its inventory in stores. Because it had only 10 percent accuracy of this inventory data, the company was forced to extrapolate the total inventory, but because it dealt in enormous volumes, with major clients like Walmart, Target, and Amazon, this posed great risks. A single percentage point of inaccuracy had a major impact on the company’s costs, revenues, supply chain management, and delivery.

To make matters more challenging, the data this company received from retailers was in a variety of unstructured formats, often with spelling errors, non-standard abbreviations, and other syntax issues. All these factors served to further complicate the data integration process.

Read this case study to learn how Adeptia’s AI software enabled the company to transform its data exchange process, increasing the accuracy rate to 90 percent and standardizing the retailer data the company received.