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This article was originally published on SupplyChainBrain.
The digital twin of a warehouse — a real-time, 3D virtual representation of an actual facility down to the space, people, equipment and inventory — lets operators monitor activity, respond instantly to disruption and model future scenarios to improve performance. Here are five top use cases for a digital twin.
Chronic shortages in warehouse labor continue, leaving operators to navigate more complex order flow and heightened customer expectations for service reliability with the same or fewer workers, all as wages and operating costs rise.
The latest U.S. Department of Labor numbers for October 2022 tell the story: 482,000 job openings in transportation and warehousing, with 322,000 hires minus 294,000 quits, layoffs and discharges. Industrywide churn hovers around 50% annually. Worker-retention efforts focus on flexible hours, more varied work, less travel time and less repetitive physical stress, as much as on pay.
Automation is key to filling workforce gaps — not just robotics but also process automation to optimize workflow as companies begin many days uncertain about who will show up to do which jobs. Prioritizing, allocating and dynamically adjusting work to match demand requires control tower visibility across the entire facility.
Warehouse VPs, directors and managers want to know what’s going on in their warehouse, asking questions like “How busy am I? How full am I? Where are the most common pick locations? Where am I running out of space?”
A digital twin provides that baseline operating visualization. Underlying artificial intelligence and machine learning monitor operations in real time, map high-activity areas, flag disruptions and orchestrate movement of people and assets against defined objectives like order priority, throughput, travel time or cost per move. As a virtual copy, it can model “what-if” scenarios without disrupting workflow.
As DCs become more capital-intensive with adoption of mechanization, automation and robotics, they become more like factories, with a growing share of ROI reliant on maintaining, orchestrating and fully utilizing capital assets.
A digital twin can manage both equipment and processes. It optimizes equipment performance and interaction — say, robotic arms loading a conveyor belt — but also monitors systems for predictive maintenance to minimize downtime. Twins also manage process flow, the movement of people, machines and product in the warehouse space. Near real-time simulation is a key differentiator for the technology. “Say you’re running a digital simulation using AGVs and realize you need to adjust dynamically to the reality of what’s happening on the floor to make them more efficient,” says Joe Vernon, principal business consultant with enterprise software and consulting firm EPAM Systems. “You can now do that over and over again, in very fast cycles.”
The result is shorter simulation times for “smarter” equipment that can be pivoted quickly to adapt to changing conditions, in both simulations and actual operations. Systems can also generate real-time feedback from the floor on performance, constraints from narrow aisles or tight corners, or speed adjustments needed to align with human activity.
End-to-end visibility within the warehouse covers a relatively short distance from truck-in to truck-out, but warehouses are integrated within larger, complex supply networks.
Omnichannel e-commerce growth, for example, has prompted dramatic changes in how warehouse management space is configured and utilized. Operators are creating dedicated spaces within conventional facilities to accommodate cross-docking and wave-picking operations; regional distribution centers now feed smaller local warehouses and micro-fulfillment centers closer to urban customers, in response to next-day and same-day demand. Distinct space and operating constraints require both demand-sensing and tight inventory controls.
“When you’re designing product flow throughout your network of distribution centers, management of inventory location becomes incredibly important. You need to know how much inventory you should be staging at each, and how your sales forecasts affect that staging.” Beyond meeting regular demand, network visibility and scenario modeling are essential to running promotions or liquidation of seasonal inventory through multiple channels.
Within the warehouse, it’s critical to closely track available inventory, know when a truck delivery is delayed or when a conveyor or forklift is due for scheduled maintenance, and how those events will affect time and resource allocation.
The capability with AI and machine learning to instantly assess causality — how each process and action affects all the others — has been a key technology differentiator. Potential benefits extend well beyond the four walls of the warehouse and even the wider distribution network, across global supply chains involving multiple handoffs over thousands of miles.
Vernon says demand for data processing and visualization capability has surged since COVID-19, as companies came to realize the visibility gaps they face daily. “This isn’t theoretical,” he emphasizes. “As I’m watching procurement, movements through the port and all the other steps, I can know the risks. I can simulate things like price changes, bad weather and China cutting factory production to 50% because of COVID, so I’ll know three months from now that normal lead times will be late by a month or two.”
Procurement has been a concern since COVID because of potential downstream impacts of materials shortages or transportation delays.
Add to that growing environmental, social and governance (ESG) concerns as companies, customers and investors in a wide range of sectors are closely monitoring and making purchasing decisions about the origin, content and end-to-end carbon footprint for finished product. Finally, geopolitical uncertainties — the war in Ukraine, U.S.-China trade tensions, Russia and Iran sanctions — raise compliance challenges.
All of these increase the risk of materials shortages, delayed shipments and demand-driven or compliance-related shortages. A worst-case scenario, you may recall, was hospital personal protective gear (PPE) early in the pandemic. Often there was no actual PPE shortage, but rather a visibility problem: Once it became apparent the clinic was running low, everyone stashed some in a desk or closet. Supply was everywhere, but unavailable.
Today planning around a resilient supply chain is as important as for a cost-effective one. Having clear visibility of your true inventory values, knowing exactly what you have everywhere and taking tight control over distribution of an in-demand resource, solves artificially generated demand spikes.