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 Amazons 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 on more recommendations independently, for faster, frictionless performance. 

There will be challenges  in 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.

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