Digital Twins

Trend Overview

Impact:

Limited

Adoption:

< 5 Years

Focus Areas:

Visibility & Resilience

Trend Clusters:

Digital Backbone

Sectors:

The trend of Digital Twins encompasses virtual models that accurately mirror the real-time conditions and behaviors of physical objects or processes they represent. A digital twin provides value through visualization, diagnosis, analysis, prediction, simulation, and optimization without the need for someone to interact with its physical twin.

The rapid advancement of sensor technology, cloud computing, and artificial intelligence (AI) over the past few years has significantly enhanced the accuracy and capabilities of digital twins. These virtual replicas now include a wide range of objects and systems, from individual machines to entire cities, providing unprecedented insights and control. This technological evolution has caught the attention of numerous industries, including logistics, where the potential to revolutionize supply chain operations is immense. The digital twin market, valued at US$ 12.8 billion in 2024, is expected to continue to grow, driven by innovations and increasing adoption rates, with projections showing a compound annual growth rate (CAGR) exceeding 40% from 2023 to 2035 ($240.3 billion).

Adoption of digital twins in logistics has accelerated due to recent technological developments, and yet full integration into supply chains remains quite a few years away. Digital twins are moving from single-use applications to utilization in more comprehensive systems, integrating all aspects of logistics operations. Larger logistics companies like DHL are leveraging digital twins to enhance efficiency, resilience, and sustainability across their operations. As the technology matures, its impact is expected to grow, eventually but not yet transitioning from moderate to significant as organizations build more interconnected digital ecosystems.

Five types of Digital Twins

Source: Vidya Technology (2022): The 3 Levels of the Digital Twin Technology - 2023

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. Recent events such as the Covid-19 pandemic and geopolitical tensions have underscored the need for resilient supply chains. Stress testing an entire supply chain with a digital twin can help build resilience.

Logistics and supply chain organizations can leverage digital twins to simulate disruptions across global networks, including natural disasters, cyberattacks, and sudden market changes. These simulations provide a comprehensive view of potential impacts on service levels, the cost to serve, and supply chain integrity without affecting real-world operations. By applying these scenarios, logistics planners can identify vulnerabilities and optimize their response strategies in real time. Additionally, digital twin technology enables the modification and rerunning of scenarios to test various strategic adjustments, helping to enhance overall supply chain resilience.

With this proactive approach, more informed decisions can be taken, strengthening logistics operations in the event of unexpected disruptions. This new level of robustness ensures continuity, reliability, and sustainability in service delivery.

Next use case

Optimizing Logistics Processes

Practically all processes within supply chains can benefit from the use 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, has used 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 stock-keeping unit (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, this helps 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.

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Predictive Maintenance

Unplanned downtime is disruptive and expensive, costing industrial manufacturers lost time of over 15 hours a week and over $50 billion a year. 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. 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, forklifts, trucks, and other assets, tracking the condition of each one 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 any given year, boosting operational throughput and reducing costs.

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Challenges

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 risk, giving cybercriminals a possible new point of entry to disrupt an organization’s operations.

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.

With direct connection to physical objects, digital twins potentially pose a security risk, giving cybercriminals a possible new point of entry to disrupt an organization’s operations.

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Outlook

While digital twin technology has existed since the start of the 21^st century, it is now approaching a tipping point where widespread adoption is likely within the next 5-10 years. Initially, the primary application areas focused on singular assets and contained systems. However, the future will see digital twins encompassing entire supply chains, integrating thousands of assets across various players. The continuous development and integration of AI, machine learning, and Internet of Things (IoT) will further expand the capabilities of digital twins, making them indispensable tools for modern supply chain management.

This trend should be monitored TO SOME EXTENT,with use cases in some applications that can already be addressed today.