Digital twins bridge the gap between physical and virtual worlds by providing real-time data insights, predictive analytics and simulation capabilities. This, in turn, enables organisations (and even cities) to optimise performance, reduce downtime and improve decision-making.
Building upon a lecture from our Digital MBA for Technology Leaders, this article explores the functionality of digital twin technology, its applications in predictive maintenance, remote troubleshooting and product development, as well as the implementation challenges within complex systems. Through real-world case studies, it highlights practical applications and solutions technology managers can leverage to drive operational efficiency.
First things first…
A digital twin is a virtual representation of a physical asset, system or process that uses real-time data, simulation and analytics to mirror its real-world counterpart. By utilising twins, organisations optimise performance, predict failures and improve decision-making across various industries.
Creating a digital twin involves integrating multiple data sources, including sensors, IoT devices and historical records, to generate a detailed model of the physical asset. The process typically follows these steps:
Predictive maintenance relies on digital twin technology to forecast equipment failures before they occur. By leveraging real-time data and historical trends, digital twins help businesses reduce downtime, extend asset lifespan, and lower maintenance costs.
Shanghai Automobile Gear Works implemented a digital twin in their assembly lines. By analysing sensor data and running digital twin simulations, the company detected early signs of equipment wear and prevented failures. As a result, they reduced maintenance costs by 25% and improved operational efficiency across multiple production sites.
Siemens’ Insights Hub (previously MindSphere), for instance, is an industrial IoT-as-a-service solution that collects and analyses sensor data in real-time, facilitating predictive maintenance strategies.
With digital twins, companies can diagnose problems remotely, reducing the need for on-site inspections and interventions. This capability is particularly useful in industries where equipment is located in remote or hazardous environments; for example, wind farms.
A wind farm operator can use, for instance, Akselos’ digital twin technology to detect an anomaly. Engineers can analyse the twin remotely, pinpoint the issue and deploy targeted maintenance crews to fix the problem. This approach reduces the downtime and, subsequently, increases the energy output.
Digital twins streamline product development by allowing engineers to test and refine designs in a virtual environment before physical prototyping. This reduces costs, speeds up development cycles and enhances product quality, positively impacting the product lifecycle.
For many, a tool of choice is GE Digital’s Proficy Plant Applications which assists in optimising product development processes.
A good example is Rolls-Roys. The company utilises digital twins to simulate engine behaviours under extreme conditions, enhancing their understanding and predictive capabilities.
Digital twins enhance supply chains by improving efficiency, predicting disruptions, and optimizing processes. They provide real-time monitoring for inventory, production and logistics, enabling companies to track operations more effectively.
Predictive analytics help forecast demand and mitigate risks, allowing businesses to proactively address potential challenges.
Companies use digital twins for scenario planning, simulating disruptions to assess risk and develop contingency strategies. This helps identify bottlenecks early and improve decision-making. Inventory optimisation ensures balanced stock levels, reducing holding costs and minimising stockouts.
In logistics, digital twins improve route planning and facility management, leading to more efficient transportation networks. They also facilitate integration and collaboration, especially during mergers and acquisitions, by providing a unified data platform.
Major companies like Microsoft, Nestlé, and Vita Coco leverage digital twins to enhance agility, reduce emissions and optimise sourcing strategies. Microsoft, for example, integrates carbon data into supply chain decisions to reduce its environmental impact. Nestlé employs digital twins to improve responsiveness, while Vita Coco uses them to optimise global sourcing and maximise profitability.
Despite their benefits, digital twins present several challenges, including data integration, security concerns and high implementation costs.
Integrating data from various sources, especially in legacy systems, is often a struggle. However, by implementing a unified data platform that consolidates information from IoT devices, cloud storage and enterprise systems, companies can simplify integration.
Digital twins rely on vast amounts of real-time data, making them attractive targets for cyber threats. Technology managers need to employ robust encryption, access control mechanisms and continuous monitoring to mitigate security risks.
This is where the aforementioned Akselos solution helps. It emphasises security in its digital twin solutions, incorporating advanced protection measures.
Developing and maintaining a digital twin requires significant investment in hardware, software and expertise. The solution is the incremental design. That is, start with small-scale pilot projects to demonstrate ROI before expanding to your own twin technology initiative.
For example, Rolls-Royce adopted this approach by first testing digital twins on select jet engines before fully integrating them into their fleet-wide maintenance strategy. Learn more about their case study here.
In 2014, Singapore implemented digital twin technology for urban planning, integrating traffic data, infrastructure models and environmental analytics through two phases. Initially, the project faced challenges with data integration across multiple departments. There was also a security risk because the implementation process included sensitive and private data.
However, by adopting a centralised data platform, implementing cybersecurity measures and addressing data privacy regulations, the city successfully optimised traffic flow, reduced emissions, improved public services and even facilitated disaster management.
Digital twins enhance predictive maintenance, streamline remote troubleshooting and improve product development in several critical areas which, ultimately, lead to better-optimised business models.
However, the implementation process isn’t without its challenges. To address them, technology managers should ensure:
In Module 9, Digital Trends and Innovation, of our Digital MBA for Technology Leaders, Arno Hollosi, CTO of Blackshark and a seasoned academic, delivers an insightful lecture on Digital Twins.
Focusing on the definition and critical components of digital twins, Arno examines their ability to mirror objects, systems, processes or combinations of these entities. Through practical examples and critical insights, he showcases real-world use cases and outlines steps for creating a digital twin. Learn more about our Technology Leadership Program here.
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