Predictive Analytics for Demand Forecasting

Using AI and predictive analytics, retailers are improving demand forecasting and inventory management. Demand forecasting is a process that uses data from various sources to ensure the right products are in the right store at the right time. By boosting accuracy, retailers can optimize the supply chain and make a significant impact on their bottom line.​

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.

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.

Same-Day Forecasting for Quick-Service Restaurants (QSRs)

A leading restaurant chain with more than 2,000 restaurants was experiencing issues with their forecasting modeling approach to ensure product readiness for same-day orders. The legacy forecasting engine was inaccurate, lagged sales trends, couldn’t account for external influences or seasonalities, and didn’t adapt to tailored models. 

Quantiphi delivered a forecasting engine that leverages deep learning on NVIDIA GPUs. It has improved accuracy by over 20 percent and enables visualization, analysis, alerts, and the establishment of control variables.

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