Today, digital twin technology is revolutionising industries including architecture, healthcare, automotive and transportation, as well the energy sector. In fact, the digital twin market is projected to grow from $3.8 billion in 2019, to $35.8 billion per year by 2025. What accounts for this kind of growth? And why now? After all, digital twin capabilities are not new.

This article first appeared in ESI Africa Issue 3-2020.
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Since the early 2000s, pioneering companies have explored ways to use digital models to improve their products and processes. Appreciating the advancements, today in the automotive and aircraft sectors digital twins are becoming essential tools for optimising entire manufacturing value chains and innovating new products.

And in the energy sector, oil field service operators are capturing and analysing massive amounts of in-hole data used to build digital models that guide drilling efforts in real-time. In health care, cardiovascular researchers are creating highly accurate digital twins of the human heart for clinical diagnoses, education, and training. And the tip of the iceberg is the smart-city management in Singapore where the country uses a detailed virtual model of itself in urban planning, maintenance, and disaster readiness projects.

In the early days of the technology, many companies found that the connectivity, computing, data storage, and bandwidth required to process massive volumes of data involved in creating digital twins was cost-prohibitive. Which led to the slow adoption of digital twins. However, the world now builds larger projects in larger numbers than at any time in history. Our planet must accommodate the equivalent of 10,000 new cities by 2050 just to keep pace with the projected population explosion.

That’s the challenge confronting architects, engineers, and construction professionals today. Yet many are forced to work with 20, 30, 50, and sometimes hundreds of different systems that don’t speak to each other, resulting in multiple disconnected data siloes. The result? Productivity declines, decisions are poorly informed, and projects get delayed or yield sub-optimal outcomes. That’s where the digital twin technology takes centre stage.

Technology in vogue

The digital twin technology trend is gaining momentum thanks to rapidly evolving simulation and modelling capabilities, better interoperability and IoT sensors, and more availability of tools and computing infrastructure. As a result, digital twin capabilities are more accessible to organisations large and small, across industries.

Digital twins can help optimise supply chains, distribution and fulfillment operations, and even the individual performance of the workers involved in each. As an example of this in action, global consumer products manufacturer Unilever has launched a digital twin project that aims to create virtual models of dozens of its factories.

At each location, IoT sensors embedded in factory machines feed performance data into AI and machine learning applications for analysis. The analysed operational information is to be fed into the digital twin simulations, which can identify opportunities for workers to perform predictive maintenance, optimize output, and limit waste from substandard products.

Smart city initiatives are using digital twins for applications addressing traffic congestion remediation, urban planning, and much more. Singapore’s ambitious Virtual Singapore initiative enables everything from planning for cell towers and solar panels, to simulating traffic patterns and foot traffic. One potential use may be to enable emergency evacuation planning and routing during the city’s annual street closures for Formula 1 racing.

What’s new in the digital twin market?

Over the course of the last decade, deployment of digital twin capabilities has accelerated due to a number of factors:

Simulation. The tools for building digital twins are growing in power and sophistication. It is now possible to design complex what-if simulations, backtrack from detected real-world conditions, and perform millions of simulation processes without overwhelming systems. Further, with the number of vendors increasing, the range of options continues to grow and expand. Finally, machine learning functionality is enhancing the depth and usefulness of insights.

New sources of data. Data from real-time asset monitoring technologies such as LiDAR (light detection and ranging) and FLIR (forward-looking infrared) can now be incorporated into digital twin simulations. Likewise, IoT sensors embedded in machinery or throughout supply chains can feed operational data directly into simulations, enabling continuous real-time monitoring.

Interoperability. Over the past decade, the ability to integrate digital technology with the real world has improved dramatically. Much of this improvement can be attributed to enhanced industry standards for communications between IoT sensors, operational technology hardware, and vendor efforts to integrate with diverse platforms.

Visualisation. The sheer volume of data required to create digital twin simulations can complicate analysis and make efforts to gain meaningful insights challenging. Advanced data visualisation can help meet this challenge by filtering and distilling information in real time. The latest data visualisation tools go far beyond basic dashboards and standard visualisation capabilities to include interactive 3D, VR and AR-based visualisations, AI-enabled visualisations, and real-time streaming.

Instrumentation. IoT sensors, both embedded and external, are becoming smaller, more accurate, cheaper, and more powerful. With improvements in networking technology and security, traditional control systems can be leveraged to have more granular, timely, and accurate information on real-world conditions to integrate with the virtual models.

Platform. Increased availability of and access to powerful and inexpensive computing power, network, and storage are key enablers of digital twins. Some software companies are making significant investments in cloud-based platforms, IoT, and analytics capabilities that will enable them to capitalise on the digital twin trend. Some of these investments are part of an ongoing effort to streamline the development of industry-specific digital twin use cases.

The future of digital twins across industries

Industry experts anticipate digital twins will be deployed broadly across industries for multiple use cases. For logistics, manufacturing, and supply chains, digital twins combined with machine learning and advanced network connectivity such as 5G will increasingly track, monitor, route, and optimise the flow of goods throughout factories and around the world.

Real-time visibility into locations and conditions (temperature, humidity, etc.) will be taken for granted. And without human intervention, the ‘control towers’ will be able to take corrective actions by directing inventory transfers, adjusting process steps on an assembly line, or rerouting containers.

Organisations making the transition from selling products to selling bundled products and services, or selling as-a-service, are pioneering new digital twin use cases. Connecting a digital twin to embedded sensors and using it for financial analysis and projections enables better refinement and optimisation of projections, pricing, and upsell opportunities. For example, companies could monitor for higher wear-and-tear usage and offer additional warranty or maintenance options.

Better yet, companies could sell output or throughput as-a-service in industries as varied as farming, transportation, and smart buildings. As capabilities and sophistication grow, estimations are that more companies seek new monetisation strategies for products and services, modelled on digital twins.


This is article is based on an adaption of a report titled Tech Trends 2020, produced by Deloitte. For more information, visit: