Five Real-World Digital Twins in the Warehouse User Cases

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.

1. Manage Volatility and Complexity with a Static Workforce

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.

2. Help Machines Run Smarter

As DCs become more capital-intensive with the 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 - says, robotic arms loading a conveyor belt - but also monitors systems for predictive maintenance to minimize downtime. Twins also manage process flow and the movement of people, machines, and products 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, a 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.

3. Link and Optimize the Distribution Network

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.

4. Enable End-to-end Resilience Through End-to-end Visibility

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 of 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.”

5. Reduce Procurement Risk

Procurement has been a concern since COVID because of the 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 of finished products. Finally, geopolitical uncertainties - the war in Ukraine, U.S.-China trade tensions, and 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 a cost-effective one. Having clear visibility of your true inventory values, knowing exactly what you have everywhere, and taking tight control over the distribution of an in-demand resource, solves artificially generated demand spikes.

Digital Twins in the Warehouse An Essential Overview

A digital twin is a continuously updated, virtual representation of the physical warehouse that tracks assets and inventory, orchestrates staffing and workflow, and tests future scenarios in real-time to improve performance and resilience.

Digital Twins in the Warehouse: An Essential Overview Sponsored by Tecsys A digital twin is a continuously updated, virtual representation of the physical warehouse that tracks assets and inventory, orchestrates staffing and workflow, and tests future scenarios in real-time to improve performance and resilience.

The Basics Growing supply chain and order fulfillment complexity, combined with worker shortages and high turnover, have left third-party logistics providers and distributors scrambling to meet stricter customer expectations in the post-pandemic, increasingly omnichannel B2C environment.

Pressure to handle greater volumes at faster speeds with finite resources has overwhelmed many manual warehouse operations, driving demand for digital solutions. Digital twins offer a real-time, 3D simulation of the physical warehouse, showing workflow and staff allocation, how busy or full a facility is, locations of highest pick or replenishment activity, and more.

Executing against defined business rules and objectives, analytics can spot patterns and recommend process adjustments to enhance efficiency, profitability, sustainability, or other goals. A digital twin can model “what-if” scenarios such as space reconfiguration, slotting changes, or introducing robotics without disrupting day-to-day operations. Once limited to complex industrial operations, digital twin use cases now extend to more companies and sectors as advances in cloud computing, artificial intelligence (AI) and visualization tools have brought down costs.

The Future Digital twins are part of a larger digital transformation that warehouses and supply chains will need to undertake to remain competitive going forward. Neither the underlying technology nor the use cases are likely to change much over time. But accelerating acceptance and use of twins and of the AI, machine learning, and advanced modeling behind them will force evolutionary change in end-to-end supply chains.

AI-enabled twins are mostly viewed today as an equalizer helping 3PLs, distributors, retailers, and other warehouse operators keep pace with the Amazon and Walmarts of the world. In the process, however, digital tools will inevitably raise the overall bar for operating efficiency. As parts of the end-to-end supply chain begin processing huge volumes of granular data scale for faster decision-making, the rest of the chain will be pressured to integrate. A chain, after all, is only as strong as its weakest link. And just as the complexities of e-commerce fulfillment and supply disruption have strained traditional manual warehouse operations, the speed, and agility made possible by AI, machine learning, and, eventually, quantum computing -with real-time digital twin visualization - will pressure supply chains to automate more of their processes.

Supply chains are still in the very early days of digitization, moving toward the end goal of nearly complete autonomy, merging three key AI-enabled capabilities: end-to-end collaboration; a single, secure, trusted source of granular, real-time data; and a continuously updated control tower view into operations and inventory, from sourcing to payment.

Over time, AI and machine learning will first master and automate repetitive back-office tasks, then initiate alerts and recommend network adjustments, optimized from thousands of possible options in seconds, to achieve key business objectives. To the extent that successful outcomes inspire confidence, systems will begin to execute more recommendations independently, for faster, frictionless performance.

There will be challenges - data standardization and secure onboarding, for example -as companies use incentives and leverage to integrate suppliers and lock in data partners for their ecosystems. But the visibility, collaboration, rapid response, and resilience benefits will be worth the effort, especially for early movers.

The Ultimate Guide to Warehouse Automation Success: Preparation, Evaluation, and Implementation

If automation is something you are considering for your warehouse, you will need to do some careful research and planning to make sure you’re making the right decision.

All this hard work will help make the transition to automation as smooth as possible for your business.

To get you started on your journey, Tecsys is reviewing the three stages of warehouse automation success: preparation, evaluation, and implementation.

Download Now!

About the Author

Bill Denbigh is Vice President of Product Marketing at Tecsys Inc. In the supply chain world, his special focus is on transportation and logistics management systems. He is particularly an advocate for excellent customer experience and supply chain solutions that stress accessibility and ease of use.