Future of Manufacturing in the AI Era

Future of Manufacturing in the AI Era

AI has already taken significant strides in the manufacturing arena. With further capabilities being developed as AI platforms mature, advancements in other technologies together with integration with other technologies will create the necessary momentum for further transformation in the manufacturing sector. Let us examine some of these dimensions of potential impact due to advancements in AI and allied technologies.

Autonomous Robots

Robots are already replacing humans in performing repetitive tasks in factories, thereby resulting in higher levels of accuracy, desired quality levels and avoidance of human errors. As the next step, robots will become autonomous in the production arena, equipped with the required skills and intelligence to adapt to new tasks to cater to custom orders and personalised productions without the need for extensive programming interventions. For complex tasks to be performed, a swarm of robots would be required to work in a coordinated fashion. Robotics-as-a-service (RaaS) is another strand of development that is likely to be tapped into for tasks that are commonly carried out and plugged into their respective production systems. The presence of collaborative robots (cobots) in the production environment will increase, and they would be expected to work alongside humans, learning from them and adapting to changes, thus improving overall performance.

Flexibility in Production Centres

With enhanced automation of production processes and deployment of robots for the requisite skills, for certain products, it may be possible to take back production to those locations where they were being originally manufactured and reduce the dependence on a global supply chain. AI-driven systems could make local production economically viable and environment-friendly as there would be reduced carbon footprints.

Customised Manufacturing

On the one hand, AI-driven systems would provide tools to customers to define their individual requirements. On the other hand, the combination of advanced robotics and smart factories would enable factories to adapt production facilities to cater to individual needs. The combination of 3D printing and GenAI design along with smart robotics would make it feasible for manufacturers to move towards mass customisation.

Sustainability-Centric Production

The realisation of having to conserve resources and energy is increasingly driving consumers and producers to adopt production methods to reduce consumption and costs. With the network of suppliers, producers and customers adopting AI technologies that would enable them to use analytics for improved resource management leading to reduced wastage and scrap and overall better prediction, forecasting and planning, it is anticipated that AI will act as the catalyst towards sustainability goals.

Workers’ Safety

Wearables with predictive analytics would enable manufacturing organisations to be forewarned about potential accidents or injuries to workers and take real-time and proactive action to prevent accidents. Timely action may include alteration in the plant and machinery and fine-tuning the performance of certain equipment to ensure a safe working environment for employees.

Skill Development for an AI-Driven Environment

Workers would be required to be trained continually. With the goal of customisation and resource conservation being paramount, AI systems would enable workers to be trained for new skills, new tasks and techniques of production and mentor them in real time. They would also need to learn to work alongside smart robots to perform certain tasks together and use data-driven insights to fine-tune the production processes for better outcomes.

Edge AI in Manufacturing

Deployment of AI at the edge of the network where data is generated for enhancing real-time decision-making would be path-breaking. This could result in instantaneous processing and analytics with minimal communication delays. This would also mean less dependence on cloud computing and empowering decision-making on the edge. With enhanced edge AI solutions, the possibilities for real-time inventory management, further optimisation of supply chain, real-time preventive maintenance actions and real-time optimisation of energy consumption would become feasible. However, a lot of work remains to be done to overcome several challenges, namely, dealing with the diversity and complexities of edge devices, security issues and integration requirements.

Role of Quantum Computing in Manufacturing

Quantum computing could be helpful to address complex logistics, speed and precision-driven predictive analytics and granular defect detection in highly sensitive and critical scenarios. To move in this direction, we need to overcome the challenges of quantum control and scalability, security concerns and error correction methods and acquire the skilled resources required for developing products and technology. Leading technology majors such as Microsoft, IBM and Google are working in this area, which may result in some breakthrough technologies in the near future.

Transitioning to Industry 5.0

As AI enables manufacturing businesses to progress towards Industry 5.0, innovation and human creativity would have to be in the driving seat to take advantage of the automation and analytics capabilities and the connectedness of everything. Thus, it would be the test of human ingenuity to identify how humans are able to get the most out of the machines designed, created and trained by them, sharing their own intelligence with them. In turn, the machines would be able to generate intelligence of a new kind and possibly of a higher order, the depth of insights and the vastness of which are humanly impossible to achieve alone. However, decision-making and the ability to make sense of all the possible options and insights made available will remain with the humans. Hence, there is a need for a healthy collaboration between the humans and the machines.