In a highly competitive battle for customer loyalty, grocery retailers have their bets on fresh food. Fresh food drives sales, basket size, and traffic to the store. High-quality fresh food can even be equal to price as the most important criteria for choosing a grocery store. This being said, retailers do want to make sure that fresh products are always in stock.
However, fresh food is perishable, and with a shelf life of 1 to 7 days, it requires daily forecasting and daily replenishment posing a challenge to every retailer's planning department. And results are often far from satisfactory: demand forecasting errors cost retailers billions of dollars annually, with fresh food comprising 70% of food wasted. As it turns out, while popular forecasting systems perform well for basic stock items, most of them are just not suited for fresh food.
This poorly optimized area of demand forecasting gives retailers a great opportunity to get tangible results with every improvement made. ML technologies, and Bayesian methods of machine learning, in particular, can give retailers a fresh perspective on tackling this challenge. Instead of point estimations, Bayesian methods of machine-learning utilize interval estimations that allow computing quantile grids and approximating probability distributions for different levels of future demand. With fresh and ultra-fresh products losing their market value every day, interval estimations can help drive optimal inventory allocation decisions.
This approach requires fewer amount – months rather than years – of data and allows getting business gains within the first month after starting the project. The key to such rapid outcome is resistance of Bayesian methods to overfitting and high turnover typical for fresh food. Retailers can track results of implementing fresh food forecasting solutions by using business metrics such as the number of write-offs and out-of-stocks. According to DSLab experience of implementing ML-based fresh food forecasting, retailers can attain write-offs reduction of 10% while out-of-stocks tend to decrease by 5%. For example, food delivery service Samokat with over 120 warehouses in Moscow and Saint Petersburg obtained a 14% decrease in write-offs and a 7.5% reduction in stockouts.
To learn more about fresh food forecasting, read the article Demand forecasting: challenge of fresh and ultra-fresh assortments