Fresh food, already a fiercely competitive arena in grocery retail, is becoming an even more crowded battleground. Discounters, convenience-store chains, and online players are recognizing the power of fresh-food categories to drive store visits, basket size, and customer loyalty. With fresh products accounting for up to 40 percent of grocers’ revenue and one-third of cost of goods sold, getting fresh-food retailing right is more important than ever....
...Most traditional supply-chain planning systems take a fixed, rule-based approach to forecasting and replenishment. Such an approach works well enough for stable and predictable product categories, but fresh food is much more complicated. Because local demand and conditions vary from day to day, planners have to manually enter different types of data—price changes or promotions, for instance—into their replenishment systems. These daily manual processes are time consuming, error prone, and heavily reliant on individual planners’ experience and gut instincts.
There’s a better way. A number of leading retailers have found a solution that revolutionizes their supply-chain planning: machine learning. Based on algorithms that allow computers to “learn” from data even without rules-based programming, machine learning allows retailers to automate formerly manual processes and dramatically improve the accuracy of forecasts and orders. Retailers that use machine-learning technology for replenishment have seen its impact in many ways—for instance, reductions of up to 80 percent in out-of-stock rates, declines of more than 10 percent in write-offs and days of inventory on hand, and gross-margin increases of up to 9 percent.