Demand forecasting is the retail problem of predicting how much of each product will sell, where, and when, so a company can stock enough to meet demand without tying up cash or warehouse space in goods that will not move. Getting it wrong means either empty shelves and lost sales or markdowns and waste, which is why large retailers have invested heavily in machine learning to do it better than simple historical averages.
Walmart describes how its AI-powered inventory system combines historical signals
- past sales, online searches, and page views - with forward-looking data such as weather patterns, economic trends, and local demographics to anticipate demand across stores and online. In a 2023 description of the system, Walmart noted a patent-pending ability to detect and “forget” one-time anomalies, such as a weather-driven sales spike, so a single unusual event does not distort future ordering. The aim is a unified view of inventory across stores, fulfilment centres, and the supply chain.
For a general reader, demand forecasting is one of the highest-value everyday uses of AI in commerce: small percentage gains in forecast accuracy translate into large savings on inventory and waste across a business that sells billions of items.