100 % Fresh and Optimal Product Availability in Grocery Retail

Customer expectations in today’s omnichannel retail food business are tremendous, especially in fresh produce. Machine-Learning solutions make it possible for retailers to keep customers satisfied without threatening margins.

The fight for customers is not only more intense because of increased competition, but also because of customers’ growing demands. Today’s consumer is not only demanding the best price but also the greatest selection, the highest degree of availability and above all, utmost freshness. Prof. Dr. Michael Feindt, our founder and Chief Scientific Advisor, sums it up:

“The added complexity of the omnicommerce environment has made satisfying a myriad of factors, such as freshness or price, that affect the customer’s experience – and purchasing decisions – nearly impossible while still turning a profit. It has become an either-or decision for those who aren’t choosing to innovate.” (Study Customer Experience in Grocery Retail: the Fresh Opportunity).

Sucessfully managing replenishment of fresh goods with Machine Learning

Food retailers have to consider a significant amount of factors and make many decisions in order to guarantee the freshest products the consumers demand. Many solutions for demand planning quickly reach their limits for fresh produce with a shorter shelf life as planning for fresh produce is more complicated than for non-perishable goods. By using machine learning solutions, retailers receive millions of automated decisions that are necessary for grocery retail.

Machine Learning in use: Kaufland automated central planning for self-service fresh meat

Kaufland automated central planning with replenishment optimization

To automate the replenishment process in its fresh meats division to the fullest extent possible, Kaufland was looking for a new solution. With their previous systems, the existing supply chain process had reached its limits. Kaufland chose Blue Yonder Replenishment Optimization, a machine learning solution, that enabled them to highly automate daily orders. In addition, the solution makes it possible to integrate products into the process chain and therefore more closely link them to demand planning. By centralizing and highly automating planning, the amount of work in each Kaufland store was significantly reduced. At the same time, product availability improved, which led to optimized stock and therefore more freshness and fewer write-offs.

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Machine Learning in action: Natsu optimizes sales planning and reduces remaining stocks by 20%

Natsu optimizes sales planning with replenishment optimization

Natsu Foods supplies nearly 3,000 supermarkets with premium fresh products like sushi, wraps and salads. A big challenge is the short shelf life for these products, which is a maximum of four days. If the goods are not sold within this time frame, they go back and must be written off. On the other hand, stores are also trying to avoid losses in sales due to out-of-stock situations. To reduce expensive overstocking and/or shortages, Natsu relies on Blue Yonder Replenishment Optimization. Using this solution has had the desired effect: the machine learning solution even considers external factors such as holidays, vacations and the weather and can thus produce precise sales forecasts. This enables Natsu to run product planning more efficiently and reduce surplus stock by 20%. In addition, Replenishment Optimization improves calculation of raw material procurement and logistics processes, and allows for an efficient response to the high demands of especially fresh products.

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Want to learn more about Replenishment Optimization? Read the brochure Replenishment Optimization!

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World-class retailers can reduce their out-of-stock rates by as much as 80% and see their revenue and profits improve by more than 5%.