Raghavendra K.A. – Global Head of Engineering, IOT and Blockchain practice, Infosys shares how digital twins are advantageous to production lines
In an era where efficiency and precision matter, digital twins are redefining production lines. This technology transforms business operations, offering benefits that extend beyond mere cost savings.
A digital twin is a virtual replica of a physical product, system, or process, created using real-time data from sensors and IoT devices. These models allow businesses to simulate, test scenarios, and predict outcomes without interrupting operations. Essentially, digital twins provide a mirror image of the physical world, enabling companies to make data-driven decisions with remarkable accuracy.
Transformative applications in production lines
As factories grow old, components wear out and production efficiency suffers. Digital twins are transforming maintenance strategies and workflow optimisation in production lines, improving efficiency and reducing costs across various industries. The technology provides real-time insights into production lines, enhancing Operational Equipment Effectiveness (OEE) by comparing ‘As-Planned’ and ‘As-Manufactured’ metrics.
These insights help troubleshoot production and quality issues, reduce downtime, and improve plant reliability. By predicting component failures, businesses can stock replacements in advance, ensuring production isn’t hindered by unforeseen issues. They also simplify remote inspections, minimising the need for physical presence. By improving supply chain management and optimising equipment usage, digital twins significantly boost operational efficiency.
Digital twins also promote sustainability by lowering energy and water consumption while minimising waste. Additionally, they enhance safety through real-time equipment monitoring and support traceability and regulatory compliance by making operational data accessible for audits.
Companies like Rolls-Royce and General Motors are adopting digital twins to monitor and maintain machinery. By analysing real-time data, they can predict when a component might fail, allowing for timely maintenance that prevents costly downtime and ensures smooth production lines.
The role of AI and Cloud solutions
While digital twins generate vast amounts of data, the real magic happens when this data is analysed by AI. Advanced machine learning algorithms can sift through the data to identify patterns and make predictions that human analysts might miss. Deep learning excels at extracting contextual insights from time series data, utilising both structured data (such as sensor information) and unstructured data (like maintenance manuals). At the equipment level, knowledge reasoners identify specific issues, while Generative AI (GenAI) further optimises their effectiveness.
This synergy between digital twins and AI enables real-time optimisation, predictive analytics, and advanced simulations that take production efficiency to new heights.
Cloud platforms play a crucial role in this ecosystem by providing the necessary storage and computational power. Companies like Siemens and Unilever leverage scalable Cloud solutions to store and analyse the data generated by their digital twins. This not only makes the technology more accessible but also ensures that insights are available whenever and wherever they are needed.
Advantages that go beyond just saving money
The advantages of digital twins go beyond cost reductions. They provide immersive, low-risk training environments where operators can develop their skills without impacting actual production. Digital twins also enhance safety with real-time monitoring of equipment health and improve traceability to ensure regulatory compliance.
Additionally, they enable global site visits and inspections without travel, saving time and money—especially beneficial for multinational corporations managing multiple production sites across various regions.
As noted, several industry giants have already benefited from digital twins. In the consumer goods sector, companies like Unilever use them to simulate production lines, speeding up time to market and reducing waste.
These real-world applications showcase the versatility and effectiveness of digital twins across industries, making a strong case for their adoption.
Future advancements
The future of digital twins and AI in manufacturing is poised to revolutionise the industry by enhancing efficiency, innovation, and sustainability. By embracing technologies such as predictive maintenance, real-time optimisation, and advanced simulations, manufacturers can significantly reduce downtime, extend equipment life, and lower production costs.
Key advancements include:
- Predictive maintenance: leveraging improved analytics and self-healing systems to minimise downtime
- Real-time optimisation: utilising AI for dynamic process adjustments in response to real-time conditions, enabling more adaptive manufacturing
- Advanced simulations: implementing high-fidelity simulations and virtual prototyping to boost accuracy and efficiency
- Integrated supply chain management: achieving end-to-end visibility and resilience in supply chains through digital twins
- Quality control: enhancing product quality with automated inspections and predictive quality management
- Human-machine collaboration: improving operator safety and efficiency through AR/VR training and collaborative robots
- Sustainable manufacturing: using AI to optimise energy consumption and promote recycling, thereby supporting a circular economy
- Customisation: catering to specific customer needs with mass customisation and flexible manufacturing systems
- Data-driven decisions: accelerating decision-making processes through enhanced analytics and intelligent automation
- Cybersecurity: prioritising the security of digital twins as they become deeply integrated into manufacturing operations
The industry 4.0 maturity index outlines four phases: visibility, transparency, predictability, and adoptability, with AI as the backbone for the latter three. Through knowledge engineering, machine learning, and deep learning, AI enhances digital twins by using historical data to improve model accuracy.
GenAI also plays a key role by leveraging structured and unstructured data, including sensor information, to optimise reasoning and create user-friendly interfaces. As we look ahead, especially with GenAI on Edge, we see its potential to transform the industry. However, achieving commercial viability will require further reductions in storage and computing costs.
Advancements in deep learning and user interfaces will refine digital twins, enabling more accurate simulations and streamline production lines. Incorporating digital twins into supply chain management can significantly improve resilience and risk oversight, enabling businesses to swiftly adjust to evolving market dynamics and maintain their competitive edge. This technological evolution is not just exciting; it’s essential for the future of manufacturing.
Raghav leads the engineering, IoT and Blockchain practice at Infosys, supporting clients through their product lifecycle transformations. With over 20 years’ experience in the automotive sector, he has worked with both traditional and next-gen OEMs and Tier 1s.
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