Morrisons Uses Artificial Intelligence to Improve Product Availability

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Morrisons Uses Artificial Intelligence to Improve Product Availability

By Jamie Grill-Goodman - 03/30/2017

Morrisons, one of the UK’s largest supermarket chains, is using artificial intelligence in its ordering system to automate over 13 million ordering decisions per day, which is already reducing shelf gaps by up to 30%.

morrisons

The grocer has partnered with Blue Yonder, provider of artificial intelligence and machine learning applications for retail, to optimize replenishment and automate ordering of 26,000 ambient and long-life product SKUs in all its 491 stores. The Morrisons team have worked with Blue Yonder to use its technology to improve product availability and the new, simpler ordering system is reducing shelf gaps by up to 30%. 

Employees no longer need to spend time manually ordering goods, which frees up their time for other tasks, such as attending to customers.  Also, with improved in-store availability, customer satisfaction improves.

The Blue Yonder Replenishment Optimisation technology automatically analyzes sales data and other data sources from Morrisons and combines this with external data such as weather forecasts and public holidays. Through the automated analysis of data, the system can predict the level of demand down to the individual product and store location.  Blue Yonder’s technology then fully automates ordering per store and per product.

Blue Yonder’s Replenishment Optimization uses cloud technology, making it capital-light and highly scalable. Using machine learning technology, the system learns as it goes and can use a vast and complex amount of data to make highly accurate ordering decisions.

The system was launched in Morrisons during 2016 and now covers all 491 stores, automating over 13 million ordering decisions per day.

“Our biggest new initiative has been our new automated ordering system," said Morrisons’ CEO, David Potts. "The system is capital light, utilizing cloud technology and store-specific historic sales data to forecast stock requirements. It is reducing costs and stock levels, while also saving time for colleagues, and providing a better offer for customers.”