Through collaborative creation with Hitachi, Ltd., Seiyu GK will introduce the Hitachi Digital Solution for Retail/AI Demand Forecast Auto Replenishment Service to stores all over Japan from October 2019 as a system for automatically replenishing based on demand forecasted by AI. The subjects of this service include approximately 250 items produced at Seiyu's central kitchens among the products sold at the deli section. Conventionally, replenishment is implemented by experienced Seiyu associates (employees) in charge for each product based on past sales results and others. In contrast, in this system, AI conducts a high level of demand forecast for each store and product. The replenishment quantity is determined based on the results of the forecast, and thus the system enables automation of replenishing operations.
With machine-learning technology, retailers can address the common--and costly--problem of having too much or too little fresh food in stock. 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.1 1.Raphael Buck and Arnaud Minvielle, "A fresh take on food retailing," Perspectives on retail and consumer goods, Winter 2013/14. Fresh food is perishable, demand is highly variable, and lead times are often uncertain.
Many retailers today employ inventory management systems based on Re-Order Point Policies, most of which rely on the assumption that all decreases in product inventory levels result from product sales. Unfortunately, it usually happens that small but random quantities of the product get lost, stolen or broken without record as time passes, e.g., as a consequence of shoplifting. This is usual for retailers handling large varieties of inexpensive products, e.g., grocery stores. In turn, over time these discrepancies lead to stock freezing problems, i.e., situations where the system believes the stock is above the re-order point but the actual stock is at zero, and so no replenishments or sales occur. Motivated by these issues, we model the interaction between sales, losses, replenishments and inventory levels as a Dynamic Bayesian Network (DBN), where the inventory levels are unobserved (i.e., hidden) variables we wish to estimate. We present an Expectation-Maximization (EM) algorithm to estimate the parameters of the sale and loss distributions, which relies on solving a one-dimensional dynamic program for the E-step and on solving two separate one-dimensional nonlinear programs for the M-step.
Almost half of UK grocery retail directors say replenishment is still driven by gut feel, according to research by Blue Yonder, which supplies predictive applications of retail. It said that interviews with 750 grocery managers and directors in the USA, UK, Germany and France, showed that despite a rise in accurate machine learning algorithms for automated replenishment and demand planning, 46 per cent of surveyed directors in the UK say replenishment is still an entirely manual process. Some 85 per cent of respondents identified automation as a key tool for making the fast decisions needed to meet customer demand. The research also identified that 31 per cent of directors in the UK feel there are now too many decisions to be made manually, with the same number stating that gut feel is slowing them down. The research also found that 62 per cent of UK directors say they have invested in replenishment optimisation in the last two years; 31 per cent say they will be investing further in replenishment optimisation in the next two years.