A third-party logistics warehouse handling fulfillment for 30+ e-commerce brands was struggling with efficiency. Pickers walked miles of unnecessary distance daily, popular items were stored far from packing stations, order volume spikes caught them understaffed, and error rates were costing them client contracts. We built an AI operations system that optimizes every aspect of warehouse workflow.
Warehouse operations involve interconnected optimization problems — the best pick path depends on current inventory locations, which should be dynamically slotted based on velocity, which depends on predicted order mix, which varies by day and season. The system needed to solve these simultaneously while working within physical constraints (aisle widths, equipment availability, labor skill levels).
We built three interconnected AI modules. The demand forecasting module predicts order volumes by SKU group for optimal staffing. The dynamic slotting module repositions inventory based on velocity patterns, co-pick frequency, and physical characteristics. The pick optimization module generates batched, routed pick lists that minimize travel distance. All three modules update continuously and feed into a warehouse management dashboard.
Want to see how this solution could work for your business? Book a personalized demo with our team.
Request a DemoOur pickers are doing 45% more orders per shift and making 80% fewer mistakes. We won three new client contracts based on our improved SLAs.
If it exists, AI can improve it. Let's build something great together.
Book a Free Strategy CallPlayers improved serve accuracy 28%. Academy retention rate increased 50%.
View DetailsRead times decreased 40%. Critical findings flagged within minutes instead of hours.
View DetailsSimulation iteration cycles reduced from weeks to hours. Two new papers published in first quarter.
View Details