The growth of e-commerce has led to the proliferation of SKUs in a warehouse and increased the need to service customers by offering super-fast fulfillment. The increased demand during COVID-19 and high customer expectations have put pressure on warehouses to employ technology, automation, and data science to improve warehouse operations and throughput. Travel time between locations during order picking contributes to 60-to-70% of an operator’s working time. This project aims to reduce the pick time of a customer order by placing the products intelligently in the warehouse, referred to as slotting, such that the picker travels less distance in total to pick a complete order. A reusable mathematical programming model was developed in two stages. The first stage conducts product affinity analysis using the Apriori algorithm. The second stage uses affinity between products, product order frequency (product popularity), and warehouse layout (bin location distances) as coefficient inputs to a nonlinear programming assignment problem to derive optimal product slotting design.