AI transforms manufacturing in 2024 by enhancing efficiency and innovation
The manufacturing industry has undergone a remarkable transformation over the decades. Once which was heavily reliant on manual labour and mechanical automation, now it stands on the brink of a new industrial revolution, characterised by smart machines and data-driven approaches. Emerging technologies like artificial intelligence (AI) are reshaping production systems, heralding a new era of innovation and efficiency.
The role of AI in 2024
2024 has been a pivotal year for the manufacturing sector, marked by the widespread adoption of AI and automation across industries. AI has evolved from merely automating repetitive tasks to enhancing virtually every aspect of manufacturing. With AI-powered machines capable of learning from vast amounts of data, manufacturers can increase efficiency, reduce costs, and deliver products that are more personalised and precise than ever before.
“In the manufacturing industry, AI is playing an important role for us. From the performance of the vehicle to the security of the people, everything is monitored by our 24/7 command control centre,” says Santosh TG, Chief Digital Officer, Switch Mobility. One groundbreaking application of AI is predictive maintenance, a revolutionary concept that uses AI, machine learning, and IoT to identify equipment failures before they occur. Unlike traditional maintenance methods, such as reactive (fixing issues after they arise) or preventative (scheduled maintenance), predictive maintenance allows real-time monitoring and intervention, preventing costly breakdowns.
“Today there is no need for manual intervention to check on that. Systems equipped with data collected in the cloud can alert the command centre about the condition of the vehicle,” adds Santosh.
Digital twinning: A game changer
Digital twinning has emerged as another revolutionary approach in modern manufacturing. A digital twin is a mirrored virtual model or simulation of a physical item, technique, or method. Constantly synchronised with real-time data from sensors and IoT devices, digital twins enable manufacturers to predict, control, and enhance the behaviour of the physical object throughout its life cycle.
“Apart from all emerging trends, I believe the digital twin is extremely a game changer for us. We can predict how a product will look even before it is born and conduct crash-tests digitally without worrying about the effort and money invested into it,” shares Abhinav Srivastava, Chief Information Officer, Daimler India Commercial Vehicles.
AI-powered digital twins not only enhance product design but also improve productivity, quality, and employee morale. They facilitate error-proofing, reduce defects, and minimise the need for rework, allowing manufacturers to produce more vehicles or products in less time.
Transformative applications of AI
AI’s influence extends beyond predictive maintenance and digital twinning. Ravi Peddhibhotla, CDO, TVS Electronics, highlights AI’s potential to address inefficiencies and enhance productivity. He says, “AI is generating a lot of value. For example, you can have a centralised repository that reduces productivity loss, saving at least an hour by eliminating inefficiencies. AI allows machines to talk to each other and operate autonomously, solving problems of quality control and process continuity. The vision for AI in manufacturing is unlimited, it can completely transform the manufacturing process.”
Shankar Viswanathan, CIO, Sundaram Clayton, underscores AI’s role in safety and quality assurance. He shares, “One of the simplest use cases we’re looking at is safety. AI-powered cameras can detect unsafe behaviour among workers in sensitive areas. If someone is not wearing PPE or is unauthorised to work in certain areas, the AI flags the behaviour and notifies safety officers, enabling proactive intervention.”
Viswanathanalso emphasises AI’s application in quality prediction for casting processes. AI identifies defects in large aluminum surfaces that are often too subtle for the human eye to detect. By automating this inspection process, manufacturers can significantly improve quality control. Furthermore, the integration of AI with 5G and automated robotics offers immense potential for streamlining operations. For example, robots can autonomously move materials within plants, enhancing efficiency and reducing dependence on manual labour.
The double-edged sword of AI in manufacturing
While AI is revolutionising manufacturing, it comes with challenges. Automation reduces human error and increases efficiency, but it also raises concerns about job displacement. A report by the McKinsey Global Institute estimates that by 2030, up to 800 million workers could lose their jobs to robots, with the manufacturing sector being one of the most affected.
However, industry leaders remain optimistic. Srivastava of Daimler believes that the narrative needs to shift. “People will soon realise that AI will not replace humans. Instead, humans with AI will replace humans without AI, and that is going to be the game changer once the mindset changes,” he adds.
The future of manufacturing with AI
The manufacturing industry has always been synonymous with innovation. AI represents the next stage in its evolution, following the mechanisation of production lines and the advent of robotics. Today’s AI systems perform complex tasks such as predictive maintenance, quality control, supply chain management, and generative design.
Looking ahead, AI’s integration with the Internet of Things (IoT) will deepen, enabling machines to capture and analyse real-time data. This continuous feedback loop will allow AI systems to learn and enhance their performance, driving further improvements in efficiency, accuracy, and innovation.
The future of manufacturing with AI and emerging technologies like 5G holds immense promise. By blending human ingenuity with AI capabilities, the industry can achieve unprecedented productivity, safety, and sustainability levels, paving the way for a smarter and more resilient manufacturing ecosystem.
