Digital twins are increasingly being used to model systems of interconnected things. Here Matthew Margetts defines what a digital twin is, and suggests where the technology is headed.
What is a digital twin?
Digital twins are virtual replicas of a physical product, process, or system that bridge the physical and digital worlds. Interestingly, the concept of digital twin technology has been around for longer than you may think. When the Apollo mission was developed, scientists at NASA created a digital twin to conduct experiments on the clone before the mission started.
Today’s digital twins use sensors to collect real-time data about a physical item, which is used to create a virtual duplicate of the item. The digital duplicate can be optimized, manipulated, and analyzed to test different scenarios in a risk-free environment. Digital twins also integrate artificial intelligence (AI) and machine learning (ML) to bring together data, algorithms, and context. This enables organizations to:
- Get new answers to new questions
- Test new ideas
- Uncover problems before they happen
- Monitor items remotely.
The advantages of digital twins include:
- Virtual representation of a virtual ecosystem map of assets across operations and business processes using accessible, real-time data flowing across connected systems helps automate workflows, mitigate risks, and drive greater sustainability.
- Data intelligence allows for constant monitoring of any entity, system, or device, shared to interactive dashboards in real-time. This thread of corresponding data allows organizations to overcome data and organizational silos to truly understand how well their operations are performing.
- Secure components of digital twins can be shared with various stakeholders in the ecosystem, enabling better collaboration and communication, regardless of location.
How are digital twins being used today?
In the past, digital twins were used to improve the performance of single assets, such as wind turbines or jet engines. These days, they connect not just one asset, but systems of assets and devices or even entire organizations. As they combine more and more assets with information about processes and people, their ability to help solve complex problems is also increasing.
Digital twin examples related to risk and resilience
Supply chain optimization and resilience
Supply chains have undergone massive disruption and instability in the last few years in the wake of the COVID-19 pandemic and the war on Ukraine, with shortages of raw materials, and finished products impacting daily life. Supply chain visibility has become more important than ever before. Deploying digital twin technology enables companies to digitize their end-to-end supply chains, using intelligence to automate and optimize operations, reduce risk, and increase on-time delivery.
Resilience and risk management
Coca-Cola has managed to stay ahead of supply chain challenges by using digital twin technology for improved resilience and risk management. Coca-Cola operates from 18 plants worldwide, shipping to about 1,000 locations in 170 countries. This is challenging enough even in times when the supply chain is consistent and predictable, never mind the disruption the world's supply chains have faced in recent years. Early in 2021, Coca-Cola built a digital twin of their manufacturing network to support business continuity planning and network optimization. By digitizing the network, they could bring all the data into a single model, test out different scenarios, and be better equipped to react. Using a digital twin also removes human bias from the analytics and the decision-making and reveals new opportunities for continuity of supply.
Product risk and resilience
Using digital twin technology, organizations can predict the future performance and analyze potential process failures of a product, even before the final design is approved. This scenario-based testing allows engineers to predict the failures and risks and apply mitigation in simulation labs.
The digital thread produced by digital twins enables data flows. It provides an integrated view of asset data, helping to optimize product life cycles by identifying gaps in operational efficiencies and producing a wealth of process improvement opportunities.
What is the future of the digital twin?
For companies and organizations already using smart technology, digital twins are the next step in the digital journey. Today’s digital twin technology can be used in new and mature ways, integrating smart sensors, AI, and ML to solve the most prominent organizational challenges, while improving efficiencies, optimizing processes, detecting problems before they occur, and innovating for the future. It’s important to note, however, that maximizing the usefulness of digital twins requires high-performing databases that can pull together and process various data sets in real time.
If your organization is interested in producing not only better business outcomes, better resilience outcomes, better environmental outcomes, and better social outcomes, digital twins are undoubtedly worth exploring.
Matthew Margetts is Director of Sales and Marketing at Smarter Technologies. His background includes working for blue-chip companies such as AppNexus, AOL/ Verizon, and Microsoft in the UK, Far East and Australia. Smarter Technologies tracks, monitors and recovers assets across the globe in real time, providing asset tracking systems to the open market and fulfilling the world’s most complex asset tracking requirements.