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CUB Rolls Out Afresh’s AI-Powered Fresh Operating System After Seeing Transformational Results

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CUB Rolls Out Afresh’s AI-Powered Fresh Operating System After Seeing Transformational Results

August 30, 2022 by Asia Food Journal

AI-Powered Fresh Operating System

Courtesy of Afresh

SAN FRANCISCO, Aug. 25, 2022 /PRNewswire/ — Afresh Technologies, the leading provider of AI-powered technology built specifically for Fresh categories within grocery stores, announced today the rollout of its Fresh Operating System across CUB stores. After utilising Afresh’s predictive ordering and merchandising solution in select stores, CUB, Minnesota’s largest grocery store serving millions of customers each month, quickly recognised significant improvements to inventory turns and product freshness for consumers, material labour efficiencies for store personnel, and a profound reduction in food waste across their supply chain.

“At CUB, the local community is at the center of everything we do. Afresh has helped us keep our community front and center by delivering better, fresher products to our shoppers, while also helping us reduce waste and lessen our environmental footprint,” said CUB CEO, Mike Stigers.

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Afresh allows CUB teams to manage the ever-changing nature of fresh food more efficiently, reducing food waste and achieving superior freshness in their stores. The solution works across CUB’s existing forecasting, inventory, and store operations, providing department managers with a suite of easy-to-use ordering tools powered by real-time insights. As a result, grocers utilising Afresh typically increase sales by an average of 3 percent across their chain while reducing food waste by 25 percent and significantly improving inventory turns.

“Afresh worked to make the implementation of the Fresh Operating System as easy, flexible, and lightweight as possible, which significantly reduced the burden on our IT team,” said CUB CIO Luke Anderson. “We’re excited to roll Afresh out to more stores as a part of CUB’s broader strategy to increase efficiencies across our chain.”

“CUB is a beloved cornerstone of their local community in Minnesota, a testament to the great work they do to constantly improve the grocery experience for their shoppers.” said Matt Schwartz, CEO and co-founder of Afresh. “Afresh is proud to continue to partner with CUB to help them reduce food waste and make fresh, healthy food accessible to their community.”

About Afresh

Afresh is the world’s leading fresh technology company. Afresh’s AI-powered solutions optimise critical functions in fresh food, including ordering, inventory, merchandising, and operations. Afresh significantly reduces food waste, improves its partners’ profitability, and makes fresher, healthier food more accessible to all. Founded in 2017 with the mission to eliminate food waste and make fresh food accessible to all, Afresh has grown rapidly with grocery customers across the US. Today Afresh has announced partnerships with grocers in over 3,000 stores and 40 states, including Albertsons, WinCo Foods, Heinen’s, Save Mart, Bashas, Cub Foods, and more. Learn more at www.afresh.com.

About Cub

Cub, based in Stillwater, Minn., was established in 1968 as one of the nation’s first discount grocery stores. The organisation operates 80 grocery stores and pharmacies in Minnesota and Illinois that offer customers fresh produce, a wide selection and food expertise throughout the stores to meet their everyday grocery needs. Cub also offers a well-crafted selection of wines, champagne, rose, craft beer, cider, and mixers through its 31 Cub Wine & Spirits and Cub Liquor stores. As the hometown grocer for over 50 years, Cub has made it a priority to be a good corporate citizen by helping to create healthy and thriving communities that enhance the quality of life for its customers, employees and neighbours. For company news and information, follow us on Twitter® at @CUBFoods and Facebook® at www.facebook.com/CUB, or visit our website at www.cub.com.

Other Topics: Afresh, Automation, Cub, food automation systems, predictive ordering

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