The concept of digital twins is not new. Digital twins refer to the digital representation of physical objects, and for more than 30 years, product and process engineering teams have used 3D renderings of computer-aided design (CAD) models, asset models and process simulations to ensure and validate manufacturability. NASA, for example, has run complex simulations of spacecraft for decades.
Several factors have now converged to bring the concept of the digital twin to the forefront as a disruptive trend that will have increasingly broad and deep impact over the next five years and beyond. In fact, Gartner predicts that by 2021, half of large industrial companies will use digital twins, resulting in those organizations gaining a 10% improvement in effectiveness.
“Digital twins drive the business impact of the Internet of Things (IoT) by offering a powerful way to monitor and control assets and processes,” says Alfonso Velosa, research vice president at Gartner. “However, to truly drive value from digital twins, CIOs will need work with business leaders to develop economic and business models that consider the benefits in light of the development costs, as well as ongoing digital twin maintenance requirements.”
The economic value of digital twins
The economic value of digital twins will vary widely, depending on the monetization models that drive them. For complex, expensive industrial or business equipment, services or processes, improving utilization by reducing asset downtime and lowering overall maintenance costs will be extremely valuable, making internal software competencies critical to driving value with digital twins.
The complexity of digital twins will vary based on the use case, the vertical industry and the business objective
Developing and supporting digital twins in such environments will require continuous updating of data collection capabilities and curating, as well as adaptive analytics and algorithms. Consider the software updating that a car manufacturer provides to its automobiles. Going forward this will require even more component monitoring and software updating, to the point that OEMs, such as vehicle makers, must also become software vendors, and use these digital twin and software skills as part of their differentiation. Asset operators will have to add software skills to their operations teams as they add smarter assets, and address more complex digital twins in their operations. They must also add software and data terms to their contracts.
As such, the costs of developing and maintaining digital twins must be driven by both business and economic models. Digital twins are not developed in a vacuum. Both the business concept and model must be tested against an economic architecture – revenue, profits, return on investment (ROI), cost optimization – and a way to measure progress as the products/services are rolling out.
Of course, different levels of digital twins complexity – from simple devices like water sensors to complex assets like vehicles and power plants – will have differing development and ongoing maintenance costs, complicating CIOs’ project business cases and ROI analysis.
To obtain the highest value from digital twins, the enterprise must address the digital ethics issues raised by different parties interacting with the data from not just the enterprise, but also its partners and customers
“The complexity of digital twins will vary based on the use case, the vertical industry and the business objective. In some cases, we will have simple, functional digital twins that are based on clearly defined functional or technical parameters,” Velosa says. “In other cases, they may require physics-based high-fidelity digital twins. In still other cases, there are compound systems composed of other digital twins that need to be integrated.”
Complex assets will often be composed of multiple digital twins, typically organized into large, composite digital twins. These will drive further development, integration and deployment cost analysis because at each layer of a composite digital twin there will be different business requirements to meet the needs of different constituencies (for example, manufacturer, customer, and the maintenance provider) that will add to, and drive up, the complexity, behavior and cost of the digital twin.
To obtain the highest value from digital twins, the enterprise must address the digital ethics issues raised by different parties interacting with the data from not just the enterprise, but also its partners and customers. This will require the enterprise to think about the value of the data and its contributions to the business and partners, and also to identify potential areas where its customers or its own data could drive value but also could be at risk.