Retailers need to be able to understand what products they need to stock in which stores to a really low level of detail to make sure that they’re serving their customers, that the products are on the shelf, so customers can get them when they want them. Tesco’s talented supply chain team helped to implement new machine learning-based forecasting algorithms, providing the ability to manage over 3,000 stores and over 30 million products over a 21-day horizon. — Rob Armstrong, Director of Data Science, Tesco
Demand Forecasting Walmart has trained their machine learning algorithms 20X faster with RAPIDS™ open-source data processing and machine learning libraries. Built on CUDA-X AI™ and leveraging NVIDIA GPUs, RAPIDS has enabled Walmart to get the right products to the right stores more efficiently, react in real time to shopper trends, and realize inventory cost savings at scale. WATCH VIDEO: HOW WALMART IS IMPROVING FORECASTING (40:27)
Forecasting Customer Reorders Consumer shopping behaviors are changing rapidly and more retailers want to run daily forecasts on millions of item-to-store combinations and improve the accuracy of their forecasting. It’s important for retailers to increase the agility of their supply chains with faster, more reliable forecasting and optimize inventory management. One way to increase agility is to predict grocery reorders given a customer’s purchase history. READ BLOG: BEST PRACTICES FOR USING AI TO DEVELOP THE MOST ACCURATE RETAIL FORECASTING SOLUTION (March, 2021)