Order picking is the most resource-intensive process and it highly depends on storage location policy. For instance, regarding storage layouts, many warehouses underutilize vertical space, leading to wasted capacity and increased storage costs. This can be due to outdated storage systems, lack of proper racking infrastructure, or inefficient stacking practices. Additionally, static layouts, blind to shifting inventory needs and demand, force pickers to travel needlessly – a staggering 55% of their time. This drags down efficiency and creates bottlenecks that delay orders.

AI-driven solutions optimize warehouse space through dynamic product clustering, calculating ideal product placements to streamline picking routes. This reduces unnecessary travel within warehouses, improving space utilization, operational efficiency, and cost savings. With AI, warehouses can dynamically adapt to inventory changes and demand patterns, ensuring streamlined workflows and scalable efficiency gains.

Optimized storage layouts lower operational expenses by reducing unnecessary labor costs and streamlining picking processes, enabling warehouses to handle more inventory with fewer resources​​.

Better organization reduces order-picking time, minimizing errors and enhancing customer satisfaction​​.

Inefficient use of storage space limits warehouse capacity and increases costs, which ML can address dynamically​​.