Warehouse management systems have long produced a wealth of data relating to picking, packing, slotting and inventory management, but the visualization capabilities provided by digital twins go a long way toward making that information actionable.

Most warehousing operations have the required data in their warehouse management systems (WMS) to put artificial intelligence (AI) and digital twins to use in warehouse decision-making and optimization processes. Adding the visualization and diagnostic capabilities of digital twins together with AI software allows warehouse managers to easily understand the implications of moving rack and slotting configurations. Digital twins can depict warehouse hotspots — areas of heightened picking activity — which makes decisions about configuring facilities and deploying labor quicker and easier, without engaging warehouse consultants and engineers as was required in the past. 

Earlier systems had access only to historical data available for comparison when it came to grappling with optimizing warehouse picking and packing. Digital twins, powered by AI, allow warehouse managers to make decisions based on real-time data and to extrapolate data to create predictive models to inform warehouse managers of the need for equipment maintenance and repairs — thereby preventing equipment failures and operational disruptions. 

Digital twins and AI also enable warehouse managers to drill down into how employee behavior is impacting facility efficiency. Building a three-dimensional model of the warehouse and taking an analytical view of the data helps to optimize product locations and reduce missed picks based on the facility cube and product velocity. Analytics capabilities provide answers on how inventory can be more productive with different slotting schemes. Implementing AI and digital twins for warehouse decision-making and optimization can be easier than many think. How so? Read on to find out.

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