Theme: Digitalisation
Industry Adoption: ⚫ ⚫ ⚪ ⚪ Early Adopters
Impact: ⚫ ⚫ ⚪ Significant

Digital twin technology creates a virtual replica of a physical object, system, or process, enabling real-time monitoring, simulation, and optimisation. By integrating Internet of Things (IoT) and AI-driven simulations, it enables predictive maintenance, supply chain planning, and warehouse layout optimisation. However, its implementation requires significant investments in new technologies and processes, as well as comprehensive training programmes to fully leverage capabilities like risk prediction and scenario simulation.

The trend drives major operational changes across multiple functional areas, with technology demanding targeted strategies and resource allocation to enable real-time optimisation. Despite its potential, the trend is mostly being adopted by innovative companies. The global market is projected to grow by 30–40% annually, reaching 125–150 billion USD by 2032. Early adopters in logistics are experimenting with supply chain optimisation, though applications remain limited compared to manufacturing. While limited data on C-Suite mentions or startups suggests the trend remains in its early adoption phase with cautious business engagement, research and academia show stronger involvement. Decent patent activity (350+, including 60 groundbreaking) and approximately 500 scientific publications indicate a growing, yet focused, interest.

What are the opportunities?

Digital twins can enable significant cost savings, operational efficiency, and climate resilience. They improve dispatch planning, asset utilisation, and customer confidence in pricing and service reliability through advanced simulations.

What are the challenges?

The scaling of digital twins technology remains challenging due to external factors like geopolitical disruptions or new regulatory standards. Moreover, transitioning from manual to automated systems, including integrating diverse data sources, requires significant investments in technology and training.

Sources:

• McKinsey (2024).
• Survey among 500+ global logistics decision makers across various industries, conducted by Statista for Maersk (Q4 2024).
• In-depth interviews with global industry experts, academia, and futurists.
• Unstructured web sources with more than 10,000 search term permutations using AI.
• Curated data from startup databases, patent databases, and analysis tools, as well as Semantic Scholar.