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In logistics, the ultimate digital twin would be a real-time replica of an entire supply chain network.

Source: DHL

Relevance to the Future of Logistics

Stress-Testing Resilience

Global supply chains are performing under extreme pressure, navigating disruptions to meet increasing demand and handle ongoing volatility. Taking the Covid-19 pandemic and other recent events as lessons, logistics organizations have renewed efforts to achieve more resilient supply chains.

Stress testing the digital twin of an entire supply chain can help build resilience. In such simulations, the user’s complete network of inbound and outbound flows between manufacturing sites, distribution centers, ports, markets, and other entities are first reflected as accurately as possible in a digital twin. Then, applying scenarios like natural disasters and cyberattacks, the user can see – without impacting real operations – how these disruptions would affect service levels and the supply chain. The user can additionally modify the digital twin of the supply chain and rerun scenarios to see if certain rearrangements lead to more resilient results. These stress-test simulations are already being offered by logistics consultants and digital twin providers with whole digital supply chains being built in the span of just a few weeks, depending on the level of detail.

As it would be impractical to simulate crises on actual, physical supply chains, digital twins provide an opportunity for logistics planners and crisis managers to pinpoint and take more resilient strategic decisions.

Optimizing Logistics Processes

Practically all processes within supply chains can benefit from the trend of Digital Twins. From appropriately allocating workloads to efficiently managing inbound and outbound flows, a digital twin can facilitate logistics optimization through visibility.

German drugstore retailer dm-drogerie markt, for instance, recently utilized digital twins to optimize inventory operations, including replenishing products on shelves. The retailer created digital twins of each of its 2,000+ stores, including the shelf layout and all product SKU locations in every branch. By having real-time visibility of product availability across all its locations, dm-drogerie markt has been able to optimally combine goods on incoming mixed pallets from distribution centers to ensure shelves are properly stocked with the fewest pallets needed. Furthermore, as the retailer knows where exactly in each branch every product belongs, it can help to minimize employee in-store walking distances. On a smartphone display, personnel can see the optimal restocking path for each item.

In implementing digital twins for specific supply chain processes, logistics organizations can potentially reduce the cost, time, resources, and waste previously incurred when completing tasks.

Predictive Maintenance

Unplanned downtime is disruptive and expensive, costing industrial manufacturers lost time of over 15 hours a week and over 50 billion USD a year, even before the pandemic. With equipment failure cited as the cause of almost half of all downtime, predictive maintenance (anticipating and repairing assets before they break or fail) is seen as a worthy strategy to effectively cut costs and increase productivity for manufacturers and logistics providers alike.

Able to provide real-time visibility of the condition of physical objects, digital twins are often seen as the ideal solution for predictive maintenance. In 2022, Kraft Heinz partnered with Microsoft to create digital twins of all its 34 manufacturing sites in North America, with one of the goals being to reduce mechanical downtime at each facility.

Digital twins can additionally be applied to smaller, individual assets, not just whole warehouses. The most advanced logistics players and equipment service providers are creating digital twins of assets like individual robots, trucks, and tools, tracking their conditions and detecting any wear and tear that should be addressed to avoid breakdowns.

By using digital twins to facilitate predictive maintenance, logistics providers can save about 40% of reactive maintenance in a given year, boosting operational throughput and reducing costs.


Real-time, high-quality data is the basis for any digital twin solution but demanding or prohibitive field conditions can limit data access and degrade the accuracy of a digital twin.
Digital twins require considerable investment in sensor technology, platforms, model development, and high-touch maintenance.
Some qualities of a complex asset like its chemical, electrical, and thermal state can be extremely costly and challenging to accurately replicate, often forcing users to make generalized, less accurate assumptions and simplifications in digital twin models.
With direct connection to physical objects, digital twins potentially pose a security risks, giving cybercriminals a possible new point of entry to disrupt an organization’s operations.

This trend should be MODERATELY monitored, with some use cases applicable today.


While digital twin technology has existed since the start of the 21st century, the trend is now approaching a tipping point where widespread adoption is likely in the next 5-10 years. For now, the main application areas will primarily be singular assets and contained systems. However, for supply chain management, the next level of adoption will be digital twins of entire supply chains, involving thousands of assets spanning various players.

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  1. Capgemini Research Institute (2022): Reflecting reality; Digital twins: adding intelligence to the real world
  2. Forbes (2022): Unplanned downtime costs more than you think
  3. Wall Street Journal Custom Studios (2022): Unlocking performance
  4. General Electric (2022): Digital twin