The Digital Twin Consortium released a definition in December of 2020 in a blog post focused on the foundational elements that constitute a digital twin. This post expands on the following statement in the definition: Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action.
Many of the requirements of the digital systems that manage real-world entities and processes originated in the constraints of the paper-based systems they replaced. Some of those features linger long after technology advances should have made them redundant. As a result, these systems inherit a limited perspective on the subject matter they manage, which can even narrow over time as industries ossify around these requirements. This leads to decision-making based on incomplete (at best) or incorrect (at worst) data throughout the lifecycle of these entities and processes, which compounds negatively throughout the value chain.
Consider a typical visit to your doctor. Your doctor examines you and your medical history and then identifies patterns to make a diagnosis, considers treatment scenarios, and prescribes action. But technology exists (most likely already in your pocket or on your wrist) to capture richer and more accurate information about your health than what can be gleaned from the form you completed by hand in the waiting room, the medical diagnoses of your past transcribed by an intermediary in another office, or a five-minute examination. Additionally, the technology exists to analyze your health data against an accumulation of medical knowledge at an unimaginable scale, with pattern recognition and simulation capabilities that are orders of magnitude beyond the powers of a single doctor.
A digital twin of you would provide you and your doctor with a more holistic understanding of your health. It would initially integrate data from digital systems already used by your doctor, and eventually might replace them. It would enable optimal decision-making for diagnosis and prescription. And it would ensure effective action by controlling, measuring, and learning from the results. Your digital twin would accelerate and improve every step of the process.
It doesn’t mean you won’t need a doctor anymore; it just means you’ll have a better doctor.
This is a straightforward example of how a digital twin could transform your health and the business of healthcare. Similar transformations like this are underway across industries, including - most likely – your own.
What is a Digital Twin System?
Once you buy-in to the vision of a transformative digital twin, how do you get one?
A digital twin system is what enables the ue of a digital twin. Your existing digital systems may both enhance and constrain a digital twin system, and you will most likely need to buy and/or build some missing subsystems. They will also need to be integrated – which may be a significant part of the effort.
Future blog posts will describe the digital twin technology stack in more detail, but some typical subsystems of a digital twin system include:
- Digital models of various kinds (e.g., legacy business systems, engineering models, purpose-built knowledge graphs, other databases, IoT data feeds and data historians, satellite imagery, point clouds, video, etc.).
- One or more observational and interventional synchronization mechanisms (that keep the digital models in sync with the real-world).
- A digital thread implementation (which weaves digital models together along different dimensions to achieve a holistic view. See The Case for Digital Thread.
- Subsystems for visualization, analysis, automation, data integration, security, and other functionality.
Let’s consider a use case for the real estate and construction industry. How can a digital twin system transform the business of optimizing the health of a building?
Building owners typically use multiple systems in the operational life of their facilities to manage space, maintenance, and building controls. If a building owner wanted to evaluate retrofit options to make their building more sustainable, none of these systems on their own would provide them with a holistic understanding of their building’s current state or with the capability to efficiently evaluate retrofit scenarios and prescribe an optimal scope of work. For example, a building automation system would rely on historical energy consumption and occupancy patterns but would have a blind spot related to resource planning and work order history. This could result in a diagnosis that prescribes an HVAC equipment upgrade (with more sensors and actuators) without considering that the organization also needs a reconfiguration of space to accommodate less staff in the office, or that the existing equipment is less expensive to maintain than the newer models.
Each system brings its own blind spots, but a digital twin system can integrate them to cover each others’ blind spots and yield a comprehensive view. The integrated digital twin system enables a more holistic understanding (e.g. the current HVAC system works fine, space allocation needs to be adjusted for a distributed workforce), resulting in optimal decision making (e.g. simulations validate program and design choices) and effective action (e.g. procure, manage and QA retrofit scope of work).
If this sounds like a lot of work, it can be. Digital twin platforms can give your digital twin system a boost by providing and integrating some of the necessary subsystems. They can supply the high-level user interface, visualization, and API that make your digital twin system usable by humans and machines. They can provide relevant digital models and synchronization mechanisms to keep them in sync with reality.
In summary, digital twin systems make digital twins functional, accessible, secure – and transformational. As for defining the requirements and implementing a digital twin system for your organization’s use cases – that’s for another blog post.