Harnessing digital twins for workforce training and upskilling
16 Jun 20259 min read
Summary
- Digital twin technology — virtual replicas of physical systems powered by real-time IoT and analytics — is transforming how companies train, simulate and optimise operations. The global digital twin market is projected to grow significantly, underlining its increasing strategic importance.
- In Asia, digital twins are especially valuable for workforce development in supply chain operations, enabling safe, simulated training on complex scenarios such as inventory risk, demand shocks and logistical disruptions.
- While offering benefits like cost-efficient training and faster upskilling, digital twin adoption faces challenges in data integration, cyber-security and needed expertise — requiring sustained tech investment and change management.
Digital twin technology is revolutionizing how industries train and upskill their workforce. A digital twin is a virtual model of a physical system, asset, or process, enabling real-time simulations and analytics. By integrating data from IoT devices and machine learning, digital twins replicate real-world scenarios to improve training outcomes. According to Gartner, the global digital twin market could reach $379 billion by 2034, highlighting its growing significance.
In Asia alone, Digital Twin technology is transforming supply chain management, offering virtual replicas of physical assets and processes that enhance efficiency, resilience, and workforce development. The Asia-Pacific digital twin market is projected to grow from $2.5 billion in 2023 to $256.5 billion by 2033, reflecting a compound annual growth rate of 58.64%.
How can businesses leverage this innovative tool to foster a future-ready workforce while overcoming its challenges?
What are Digital Twins?
Digital twins are dynamic, virtual replicas of physical entities that simulate their real-world counterparts in real-time. By aggregating data from sensors, IoT devices, and analytics platforms, digital twins enable organizations to model and test processes, assets, or systems in a risk-free digital environment.
Here are some industry examples:
Measuring ROI
Addressing these challenges will require strategic planning, cross-functional collaboration, and long-term commitment to digital innovation.
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