What kind of Digital Twin are you looking for
The concept of a “digital twin” is discussed across many industry forums. It is a hot topic right now. The problem is you hear different stories about what they do and why one approach is better than another. The only consistent message out there seems to be a digital twin is supposed to enhance operations and maintenance. If you are creating a scope for a digital twin for your business, you will need to be very clear on the features that will be right for your business?
You will need to develop your scope with the key stakeholders across your business that will interact with and benefit from the solution, such as those responsible for your asset management policies right down to your asset information requirements. If you don’t investigate this first you could be facing two potentially costly risks. The first risk is that your request for a digital twin is too detailed and includes features that will not benefit your stakeholders, which can create over-scoping and high costs from the supplier/s. The other risk is that if your scope is too broad and open-ended then suppliers will deliver to you what they think you and your stakeholders want, which also results in a higher costs and possible rework as you try to integrate the solution within your existing asset management system.
Defining what digital twin means to your business will be different for everyone. Colin Parris at GE Global defines it as: ‘A Digital Twin is a living model that drives business outcome.’ When you expand that statement out a good interpretation would be, a digital twin is information or data that is hosted using digital technology. It is based on some form of model, whether that be 3D, process models, etc., that interacts with and uses the technology to help us make better decisions. This no doubt sounds great, but we need to break this down further to make it tangible.
Every business will have a unique focus on how the digital twin works for them. The following definitions are established by Dr Grieves (University of Michigan) for manufacturing, which we have morphed to suit the Built Environment and something that works with the ethos of the ISO 19650 standards. This places the digital twin into four core categories and is useful to help to decide how the digital twin needs to perform for your business. It is important to note these categories are deliberately designed as a stepped process and can be built one upon another.
The four categories are as follows.
- Digital Twin Prototype – Simulation Model
Function: testing and assessing
Commonly used in the planning, design, and construction phase for model/data simulation.
- Digital Twin Instances – Database Model
Function: connecting
Commonly used in the operation phase to connect to the asset management eco-system.
- Digital Twin Aggregates – AI, IoT Model
Function: collecting
Commonly used in the operation phase to connect and collect information from digital twin instances and devices to deliver performance analytics.
- Digital Twin Environment – AI, IoT, Controls and Robotics Model
Function: interacting
Commonly used in the operation phase to operate physical asset/s from the virtual asset/s.
The connection to living, model, and business outcomes is present in each of these categories. All are digitally hosted information models, helping to make those smarter decisions. Now to select which is most suitable for your business.
Digital Twin Prototype
Digital twin prototype is the living model built before the physical item is constructed. This is not necessarily one model, it can be an aggregate of models (which is called a federated model). This is built so the planners, designers, and construction teams can test and assess the performance of the asset and their construction methodology prior to and through the process of construction.
The three core reasons to pick a digital twin prototype are; 1. to provide you with a snapshot in time between the design and construction phase; 2. to understand the designed performance metrics for future adjustments; and 3. if you have internal designers and engineers, to iteratively refine the asset over time.
Digital Twin Instances
Digital twin instances are the as-built version of the prototype being connected to the asset management eco-system. This is built to assist with transparency across the operation and maintenance of a site, facility, or asset. It achieves this at a basic level by connecting the model database to the asset hierarchy and into the additional operating systems such as the SCADA, asset performance management, enterprise asset management, etc.
The two core reasons for picking digital twin instances are: 1. to connect the information from the capital build into your asset management eco-system; and 2. to visually connect your model to the eco-system.
Digital Twin Aggregates
Digital twin aggregates are digital twin instances that are dynamically connected to the asset management eco-system. This is built to further enhance the transparency and reduce the risk across the operation and maintenance of a site, facility, or asset. It achieves this by further aggregating the model with operating devices (e.g. internet of things - IoT), automating levels of artificial intelligence, and embedding levels of machine learning. In return producing performance analytics to enable higher levels of decision making.
The four core reasons for picking digital twin aggregates are: 1. to dynamically link the information from the capital build into the asset management eco-system; 2. to reduce risk and enhance the user experience by visually connecting the performance analytics; 3. to enable automation of tasks between the eco-system operating systems, and 4. to enable machine learning to assist with decision making processes.
Digital Twin Environments
Digital twin environments are digital twin aggregates that connect to the physical environment. This is built to maximise the overall performance across the operation and maintenance of a site, facility, or asset. It achieves this by allowing the digital/virtual asset to control the physical asset and vice versa. This live connection is collecting the data to form the optimum performance needs, using the simulation to test the performance, and constantly enhancing the performance based on environmental or situational circumstances.
The core reason for picking digital twin environments is to maximise the performance of your site, facility, or asset in every aspect.
Hopefully, this has provided you with an awareness of the core differences, and functions, of digital twins. This will assist you when you are speaking to providers about the solution they offer. Your next step will be to define how and where the digital twin needs to sit within your asset management eco-system. This will guide the performance of your digital twin in respect to your data acquisition, through to storage, virtualisation and how it is consumed by your staff and other stakeholders.
Need help? Magnae are here to be your guiding partner.
