Constantly evolving Industrial automation and IoT, availability of big data, and access to improved computing power are some key factors that are driving the Artificial Intelligence (AI) in manufacturing market. Furthermore, this market is also promising for the investor community, leading to higher growth Markets and Markets predicts global AI in manufacturing market is estimated to reach US$16.7 billion by 2026 from US$1.1 billion in 2020, expected to grow at a CAGR of 57.2%.
Covid-19 pandemic impact and disruption
The pandemic, since its onset has been causing disruptions in the manufacturing ecosystems across the supply chain leading to halt in production and in some cases, even shutting down of manufacturing units. Shortage of labour force during long periods of lockdown has hampered production on a large scale. Manufacturers are exploring the adoption of new-age technologies such AI and Robots for restarting their business operations and ensuring business continuity.
Manufacturing in India scaling to unprecedented levels.
With customers abroad taking a China + 1 policy, Indian manufacturing is scaling to levels not heard of before, for exports, and the manufacturing plants do not have enough manpower to scale and meet the demands. This is compelling manufactures to look at other options beyond just manpower addition to meet the expectations of consistent quality at scale.
A paradigm shift in the shop floor with AI
AI with subsets Machine Learning and Deep Learning, leverage computing power to perform tasks but have to be controlled by manpower with domain expertise for data preparation. Predictive analysis and quality checks by deploying data and new-age tools can accurately predict failures and prepare teams to divert their efforts in preventive maintenance to save costs. AI can be leveraged for better collaboration between humans and machines and change the operation processes by adding value with an increase in productivity. In this new environment, routine tasks are efficiently performed by machines. The QC personnel can be reskilled to focus on critical thinking and decision-making roles to manage and oversee these AI systems or contribute in refining the defects of the product.
According to McKinsey, AI-based predictive maintenance typically generates a 10% reduction in annual maintenance costs and increases productivity upto 25%. This clearly indicates the tool is very beneficial for sustenance and future growth of the industry. Furthermore, machines neither fall sick nor have the need to socially distance themselves. As the sector is constantly evolving, manufacturers are preparing for the era of pervasive AI and shop floor QC workers have to upskill, where intellectually stimulating jobs can be taken up by a significant number of them.
Mundane visual inspection tasks get automated
Quality inspectors perform tasks of positioning, identifying, measuring and detecting flaws of products, but with limitations such as fatigue and inconsistency. Machine vision systems work 24X7 and perform 100% online inspection by spotting machine defects. Routine, repetitive tasks as well as complicated visual inspection jobs are automated with machine vision. It enables automatic inspection of objects or components across the automotive, electronics, pharma, food and beverage and other manufacturing industries by applying various digital processing techniques. This process increases throughput, improves product quality and reduces production costs. Consistence appearance and functionality of products increase too with improvement in quality as well, driving better customer experiences and higher revenues for the organisation. Machine Vision can also be used to arrest issues related to health and safety of workers, with little or no human intervention.
While many routine tasks are getting eliminated with AI and automation, in this era of fourth Industrial Revolution, traditional roles are getting altered. There is an urgent need for engineers in the areas of AI, Cloud Computing and Robotics among others to manage machines and automation. Upskilling and reskilling have to be embraced by the workforce to remain relevant and future-ready.
Some skills are foundational and simple and can be learnt with lesser effort. Others require deeper knowledge for specific roles. For instance, AI skills can be of 3 levels – AI literacy, Specific AI knowledge and AI solution development capabilities with workers getting deployed accordingly. Specialists in AI and ML, Big Data and Analytics, Data Science, Cyber security, Robotics and others are in constant demand for taking up more creative and supervisory roles in the manufacturing sector. Organisations and individuals have to jointly work towards developing appropriate training in new-age skills and their deployment to enhance productivity and drive better business outcomes.
The author is Vinodh Venkatesan, Co-founder and COO, Jidoka Technologies.