The concept of Digital Twin has been around since the beginning of the 21st Century. Although it has been more than twenty years, it was not emphasised enough until recent years. With the advancement of Internet of Things (IoT) and internet accessibility, Digital Twin technology is becoming more cost-effective and it is now being widely adopted by various industries. Digital Twin was also named as one of Gartner’s Top 10 Strategic Technology Trends for year 2017.
So … What is Digital Twin?
The idea first originated at NASA: full-scale mock-ups of early space capsules, used on the ground to mirror and diagnose problems in orbit; eventually gave way to fully digital simulations. The term “Digital Twin” was defined for the first time by Dr. Michael Grives at the University of Michigan 2002/03 in a Virtually Perfect Driving Innovative and Lean Products through Product Lifecycle Management.
To make it simple, a Digital Twin is a virtual counterpart of a physical object or a system across its life-cycle. The computer programme takes real-world data points of the physical object or system as inputs, and derives outputs in the forms of predication or simulations on how the object or systems will be affected by those inputs. The physical object or system could be a car, tunnel, aeroplane, jet engine, building, factory, or even a city.
Digital twins can be your engine to drive innovation and performance improvement. It enables learning, reasoning, and dynamically recalibrating for improved decision making, allowing the asset’s owner to have advanced monitoring, analytical, and predictive capabilities. IDC predicted that companies who invest in digital twin technology will see a 30 percent improvement in cycle times of critical processes.
How is this being done?
Firstly, smart components that use sensors to gather data about real-time status, working condition, or position are integrated with a physical asset. The components are connected to a cloud-based system that receives and processes all the data through a sensor monitor. This input is analysed against business and other contextual data.
Once the data has been aggregated at the cloud, it will then go through an Analytical Model where it has instant access to critical data points of the asset. Combining with the latest technologies such as Augmented Reality (AR) will allow more flavour in the output format, enabling a 3D representation of the assets in order to aid human understanding in the findings.
This can be strengthened by applying Artificial Intelligence (AI) and Machine Learning (ML) where the Analytical Model can automate the analysis to generate insights and prediction on the operation status, performance level, fatigue, behaviours et cetera. These insights generated will then allow the asset’s owner to make better business decisions to address current operation break-down, or plan for prevention acts to address a possible upcoming break-down.
In short, Digital Twin works by connecting real and virtual worlds using real-time sensors which detect, store and analyse data points of an asset for better decision making. Below is a conceptual architecture of Digital Twin:
Real life use case
Digital twin can be broken down into three broad categories, which can be adopted under different circumstances:
- Digital Twin Prototype (DTP) – This is undertaken before a physical product is created
- Digital Twin Instance (DTI) – This is undertaken once a product is manufactured, in order to run sensitivity test on different use case
- Digital Twin Aggregate (DTA) – This gathers the information from DTI to determine the capabilities of a product, conduct prognostics, and run test of various operating parameters
These various types of Digital Twin can offer a wide range of application possibilities in business operation, covering logistics planning, production development, production redesign, quality control, and system planning. Below are some possible business application areas in different industry sectors:
- Manufacturing – adopting digital twin technologies to commission and run the machines and production plants virtually during the design stage to verify the practicality of the design, and to detect possible problems for early resolution.
- Automotive – digital twins are made possible because cars are already fitted with telemetry sensors, but refining the technology will become more important as more autonomous vehicles hit the road.
- Healthcare – create a patient’s digital model using band-aid sized sensors which capture specific biophysical parameters and send back the health information to a digital twin for real-time symptoms monitoring and predictive analysis on the patient’s well-being.
- Hotel – allows the hotel manager to manage a hotel digitally online by creating a hotel digital model which shows in real-time, the room occupancy, electricity consumptions, pending room cleaning work, maintenance work required et cetera. Microsoft Azure smart hotel 360 is offering solutions in this area.
- Aviation – digital simulation on aircraft components can be done to assess durability, longevity, and reliability to effectively predict how particular parts will perform, and forecast — and avoid — the potential risks of engine failure. Boeing reported to have achieved up to 40% improvement in first-time quality of the parts and systems using this.
- Cities – city planners and authorities can install smart sensors to monitor and track traffic volume, determine occupancy, environmental conditions, energy consumption, public safety and various other aspects of an urban ecosystem. Singapore is the first country where it invested USD 73 million in its Virtual Singapore project, a digital 3D city model as a test bed for agencies, businesses and researchers to build a more resilient city.
Economic Value of Digital Twin
The benefits and economic value of the Digital Twin technologies will vary widely, depending on the monetisation models that drive them. For complex, capital-intensive equipment, services or processes, improving utilisation by reducing asset downtime and lowering overall maintenance costs will be extremely valuable, making internal software competencies critical to driving value with Digital Twins.
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 optimisation – and a way to measure progress as the products/services are rolling out.
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 and also be at risk at the same time.
Conclusion
With the IoT and other 4IR technologies becoming more commonly used in industrial and business settings, Digital Twin technology will definitely see an increase in adoption and application in more and more industries.
Moving forwards, the government sectors could reap the benefits of Digital Twin technologies by adopting it in the physical planning for cities and infrastructures. This could effectively test out the usability and practicality of the design under different evolution scenarios. Imagine the city population growing by ten-folds, with increased vehicles and pedestrians, what could happen to the infrastructures and how to better design the city to cater for such circumstances.
The benefits of Digital Twin to the private sector are obvious but it seems like many traditional players are unaware of the potential benefits or they are deterred by the investment of such technologies. To better prepare for this technology and make the most from its feature, a business owner should first conduct a thorough Digital Maturity Assessment on its organisations and various business units to better understand its current stand-point from people, process and technology perspectives. This should be followed by a Business Process Evaluation which looks at the potential areas, assets or products that could adopt the Digital Twin technology. The last thing a business owner wants is investing in things that are unable to bring tangible economic benefits to the company. Hence, a quantitative analysis which estimates the potential cost savings or revenue uplifts will need to be done prior to the actual procurement of the technology.
At 27Group, we help companies to craft transformation roadmap, develop customised digital solutions, secure funding and incubate start-ups. Our Digital Specialists at 27Digital focuses on the creation of customised digital solutions according to industry and business needs of the clients, aiming to maximise the benefits afforded by digital technology towards achieving business performances and competitiveness.
Speak to us to know more about our insights on how to improve your business operations using the Digital Twin technology.
Credit to: Mr. Joshua Jeyaraj, BIM Specialist
