Leaning Towards Smarter Manufacturing with AI

In today’s dynamic world, the adoption of digital technologies like artificial intelligence (AI) is fundamental to improving productivity, reducing costs, enhancing workplace safety, and staying relevant. The use of AI is more pertinent than ever in the manufacturing industry, which has undergone a systemic shift in the last few years.

The manufacturing sector in India is on a trajectory toward substantial growth, with projections indicating an increase in its contribution to the Gross Value Added (GVA) from the current 14 per cent (USD 459 billion) to 21 per cent (USD 1,557 billion) by the year 2032. Government initiatives such as the Production Linked Incentives (PLI), the development of economic corridors, and the establishment of incubation centers focused on Industry 4.0 are strategically supporting manufacturers in enhancing both efficiency and quality in their operations.

Given that 90 per cent of manufacturing data remains unused, there is great scope for manufacturers to employ data analytics to derive meaningful insights from their data. Huge amounts of data are being generated every day, which can be leveraged by AI for predictive maintenance, recognizing inconsistencies in production processes, and for faster & more accurate decision-making. Manufacturing businesses can harness AI to enhance asset and IT management, unlock the full potential of data, and streamline customer experience, among others.

AI agents are a boon to manufacturing as they automate procedures, increase productivity, and lower operating expenses. By evaluating real-time data from sensors and Internet of Things devices, these agents optimize production workflows and enable predictive maintenance to reduce downtime and avoid equipment failures. Robotic systems driven by AI improve assembly line accuracy by managing repetitive tasks quickly and precisely while maintaining worker safety.

Rise of AI-Driven Robots

While production automation and robots have been part of manufacturing for decades, their limited success can be attributed to a lack of intelligence. Typically, manufacturing automation is based on control systems and programming languages that are not fully capable of adjusting to complex changing conditions, both internal and external. An isolated robot or cell might be efficient in executing its task. But that same robot or cell is not capable of optimizing customer orders or substantially impacting the overall equipment effectiveness (OEE) of the whole production facility.

As the automation industry moves toward more open protocols, collaborative robots (cobots) and other transformative enablers flourish. Automation facilitates innovations such as lot-size-one, self-healing factories, and putting robots to work in areas where human interaction is required. The latest automation technology can engage in data sharing and co-creation within the manufacturing framework, learning from other units, and enabling plant optimisation.

The demand for AI-driven robotics is increasing in manufacturing and warehouse settings to help organisations with data and analytics that identify problems in real time and improve decision-making. But to experience the true benefits, these operations need AI as close to the origin of the data as possible.

AI for Efficiency and Cost Optimization

Cost-effective digital manufacturing solutions are crucial for maintaining efficient supply chains and producing high-quality products. AI can also assist in sustainability, which is no longer just a corporate metric but a key competitive differentiator in the manufacturing sector. Companies must leverage data analytics to conserve energy and natural resources without compromising product quality.

AI-driven digital transformation trickles down data-led tactical decision making down the hierarchy, giving more time to supervisors and plant heads to drive strategic change. Digital transformation offers benefits that align on two different fronts: On one hand, manufacturers can drive ongoing operational improvements, including increasing production throughput, improving asset utilization, and enhancing product quality. On the other hand, they also have an opportunity to create greater customer value by revolutionizing manufacturing capabilities, delivering design improvements, and optimizing service.

Ethical Considerations

The integration of AI in manufacturing comes with several ethical considerations that must be addressed to ensure responsible and fair implementation. Replacement of human jobs is perhaps the most prominent ethical consideration one must make. Companies must prioritize reskilling and upskilling so that the workers can adapt to the new AI-driven roles and use it to augment their skills. Then, data privacy is a critical issue, as AI systems rely on vast amounts of operational and employee data, necessitating strict cybersecurity measures to prevent breaches and misuse. Finally, the environmental impact of AI in manufacturing must also be taken care of and must be mitigated through sustainable practices.

Having said that, there is no one size fits all approach. Manufacturers need to take an extremely objective view of the business value they want to drive with digital transformation in an age of rapid disruptive technology changes.