AI-Driven Efficiency: How Target’s Data Science Models Optimize Demand and Inventory for 2,000 Stores

In today’s competitive retail landscape, precision in demand forecasting and inventory management is a cornerstone of success. At Target, data science drives this precision by leveraging AI and machine learning to optimize operations across its vast retail network. Through fully automated, integrated systems for forecasting, purchasing, and product positioning, Target has significantly reduced manual intervention, enhancing operational efficiency and ensuring seamless product availability for consumers, says Sharad Limaye, Senior Director, Data Sciences, Target. In this editorial interaction, Sharad shares details into how these systems have been implemented, the collaborative strategies that drive their success, and the emerging innovations in AI and data science that are poised to redefine inventory management and demand forecasting for the retail sector

Some edited excerpts from the interview:

How are AI and machine learning models improving the accuracy of demand forecasts?
Given the size and scale of our operations AI and ML models are ...