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.