SpartanNash leverages AI for fresh food orders

SpartanNash leverages AI for fresh food orders

SpartanNash is piloting an artificial intelligence (AI)-powered predictive ordering and inventory management system to boost its fresh food forecasting and ordering process.

The program, at 10 company-owned Family Fare grocery stores in the Grand Rapids, Mich., area, is intended to help minimize food shrink by ensuring the appropriate inventory levels are maintained and always fresh based on insights from customers’ shopping habits, the company said. 

The Grand Rapids-based distributor, wholesaler and retailer is leveraging an operating platform from San Francisco-based Afresh Technologies Inc. that is designed to support perishables operations. 

“Our partnership with Afresh will help SpartanNash deliver fresh produce to our store guests while also minimizing food waste, which is a key focus area for our company’s ESG efforts,” Bennett Morgan, SpartanNash chief merchandising officer, said in a statement. 

SpartanNash said in its 2021 ESG report that applying technologies and partnerships to reduce waste is a company priority. 

“SpartanNash is committed to being a good steward of our environment and minimizing the amount of waste from our retail stores and distribution centers,” the report said. “By reducing our waste, we lessen what we’re adding to landfills and associated environmental pollution.”

“The more time our associates save in the forecasting and ordering process, the more time they can spend on the floor serving our store guests,” Tom Swanson, SpartanNash executive vice president and general manager, said in a statement. “SpartanNash will continue to implement innovative solutions that improve our shoppers’ in-store experience and help us deliver the ingredients for a better life.” 

The technologies also will enable SpartanNash to better manage the ever-changing nature of fresh, said Matt Schwartz, Afresh chief executive officer. 

By using a fresh-oriented system, individual stores can better stock what customers purchase by considering sales, seasonality, display sizes and unique circumstances, while combining store-level data and real-time intelligence from fresh department managers, the company said.

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