Digital twins: How big is the opportunity for industrial organisations?

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By Deborah Sherry

Looking at how advanced our world has become, a bridge to the virtual world is no longer an imagination as the concept of digital twins is already being developed in the IoT space, ushering in an era of more efficient operational performance and business processes.

 

Industrialisation has had a profound impact on human history and has created major shifts in society thanks to technology innovations that can be traced back to four key stages of industrialisation. While the first three stages were marked by the rise of mechanisation, mass production and automation, the last one, often dubbed the Fourth Industrial revolution, is defined by ubiquitous connectivity. This new stage of industrial development is driven by the wider adoption of the Industrial Internet of Things (IIoT) and other emerging technologies such as AI, advanced robotics and 3D printing, which have the potential to revolutionise production and drive productivity growth.

The Industrial Internet, which connects machines, product diagnostics, software, analytics and people, so that businesses can operate more efficiently, presents a massive opportunity to transform European industries. Conservative estimates suggest that the Industrial Internet market is about £173 Billion globally, compared to the consumer Internet, which is about £131 Billion. Apart from being a driver of growth, the Industrial Internet will drive the adoption of new technology innovations that have the potential to revolutionise how goods and machines are manufactured and repaired. One of these innovations is digital twins.

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Beyond theory: Using Digital Twins to simulate new operating conditions

Gartner1 predicts that Digital Twins will be one of the key strategic technology trends transforming industries in the next couple of years. Digital Twins are digital replicas of physical assets built with artificial intelligence (AI) algorithms that allow companies to understand, predict and optimise the performance of individual assets. These could be individual pieces of equipment or an entire factory.

Digital twins can be used to analyse and simulate real word conditions, test new changes to existing products and monitor how they respond to these changes. This can help companies uncover deep patterns of behaviour and get the most out of each asset by integrating analytics from Digital Twins across an entire class of assets. Digital twins, combined with AI, can improve operations by helping organisations predict potential issues and how to prevent them.

Digital Twins are digital replicas of physical assets built with artificial intelligence (AI) algorithms that allow companies to understand, predict and optimise the performance of individual assets.

For instance, when an asset wears down, its twin can provide recommendations on how to fix the problem by running simulations based on its past history, context and environment and by building feedback loops for continuous improvement. Until now, organisations don’t have visibility into the impact of external factors such as weather conditions when planning the maintenance of their machines. A jet engine, for example, does not have the same maintenance needs if it flies over Europe as when it flies across the desert. A digital twin can integrate the historical data of previous flights, including data from the engine and data from the operating conditions, to help engineers understand the impact of the weather (or other external factors) on the engine and when it needs to be repaired.

That makes a huge difference for industrial organisations as it allows them to tailor repair and maintenance to the needs of the physical asset and avoid unnecessary service disruptions. The ability to know when a jet aircraft engine needs maintenance is critical – but the insight to know it can be repaired after normal operations instead of delaying the next flight is an important consideration.

 

Improved efficiency and service innovation

These improvements offer tremendous potential for driving service innovation. For instance, we are working with a famous global food & beverage brand to help them optimise the efficiency of their filling and labelling machines which produce millions of bottles every day. By analysing data from digital twins about the operating conditions of the machines and external factors that impact their performance, the company is able to predict system failures and improve efficiency. So far, this approach has helped our customer improve throughput by 8%, which resulted in $1,000,000 of savings per production line per year. 

Similarly, our customer Exelon is using Digital Twins to optimise the efficiency of its power plant and deliver faster, more affordable and more sustainable energy. These are just a few of the examples of how Digital Twins can deliver significant benefits to businesses.

Digital twins, combined with AI, can improve operations by helping organisations predict potential issues and how to prevent them.

Such a data-driven approach to repair and maintenance can improve the lifecycle of critical processes in advance manufacturing and allow organisations to save millions by avoiding potential unplanned downtime of critical assets. Moreover, the combination of digital replicas of machines and AI can significantly improve enterprise decision-making by removing the uncertainty from introducing new changes across the whole organisation. This can help organisations augment human skills by using digital twins and data analytics to improve work outcomes.

 

Achieving scalability and creating new business models

Apart from improving assets performance management, digital twins have strategic importance for organisations because they can enable the introduction of new business models. Optimising the maintenance costs and knowing precisely how an asset will evolve in specific operating conditions, can enable organisations to sell assets-as-a-service and other add-on repair services rather than just the physical product.

Another important consideration for business leaders is the scalability of the technology – not just across factories but also across the entire value chains. In the future, every machine will have a Digital Twin with the ability to connect a system, or systems of Digital Twins easily, creating Digital Threads. As more digital twins are created and connected to a digital platform, the industrial learning system will be able to provide data to the individual digital twins, improving fidelity. This will drive greater and greater productivity gains, allowing businesses and industrial processes to become more efficient and to adapt faster to the rapidly changing market requirements.

To be able to take advantage of these opportunities, businesses should be looking to build digital platforms that allow them to centralise structured and unstructured data from a variety of external and internal sources. This will help them create enterprise level dashboards that allow executives to look at their global operations in real time, and drill down to an individual asset on a manufacturing line or an oil rig to investigate how it is working.

More importantly, the use of AI and machine learning means that engineers and executives alike will be given recommendations for the best decision to make in a certain context. This approach will help organisations make informed decisions that improve the way they sell, manufacture, design, service and operate. As a result, companies adopting these technologies will be able to gain significant competitive edge in their industries.

Increasingly, we will move from a world where people tell machines what to do, to a world where machines tell people what to do for specific tasks, making them more efficient than today.

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About the Author

Deborah Sherry is the Senior Vice President and Chief Commercial Officer of GE Digital in Europe. Her division delivers cloud-based solutions that connect industry, transforming industrial businesses into digital industrial businesses. Prior to joining GE, Deborah has had experiences with companies such as Google, France Telecom Group (now Orange), Samsung, and Citibank in London. She holds an MBA from the London Business School, an MA (Hons) Law from Oxford University and a BA from Columbia University. She is a strong supporter of diversity, promoting equality for women and the LGBT community.

References

1 Gartner, Top 10 Strategic Technology Trends for 2018, 3rd October 2017 https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/

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