Application Of AI In the Manufacturing Industry
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Artificial intelligence (AI) has brought disruptive innovations in every industry. Technological advancements are changing the way business is done in every sector including healthcare, life science, real estate, education, BFSI, manufacturing, etc. Through the AI revolution, large amounts of data can be converted into actionable insights and predictions that can provide impetus to data-driven fields like genetics, robotics, connected and smart systems, etc.
As per IDC's 2019 Cognitive AI Adoption Survey, 37 per cent of AI spending in India is done by BFSI and manufacturing verticals. The 4th industrial revolution powered by AI offers an ocean of untapped opportunities leading to improved process quality, optimized supply chain and adaptability. Internet-of-Things (IoT) platforms are expected to rise from $745 billion annually in 2019 to over $1 trillion in 2022 as per IDC’s survey, thereby providing huge volumes of data and thereby immense opportunities to utilize machine learning. The manufacturing industry spawns around 1800 petabytes of data every year, which is higher than various industries such as finance, retail, etc. The surge in this complex data over the last 10 years has delayed the decision-making process for various sectors.
The ongoing COVID-19 pandemic has opened doors to newer opportunities for the country to be self-reliant. Today, with initiatives such as ‘Make in India’ and ‘Atmanirbhar Bharat’, the country is gearing towards becoming a manufacturing hub. It is imperative for the manufacturing industry to adopt novel technologies such as AI and to develop efficient and systematic processes. Improper inventory management has been a prevailing problem with the manufacturing sector. The imposition of lockdown during the COVID-19 epidemic caused mayhem in the inventory management and supply chain of various commodities in the market. Transformation to digital solutions and next-generation technology to manage inventory stock is obligatory to meet the growing demands of consumers in the volatile environment.
AI use cases in the manufacturing industry
A number of leading automobile companies such as Volvo and Maserati have benefited from the real-time data and virtual simulations provided by digital twins, which provided inputs to train AI models to improve operational efficiencies and reduce time to design.
The automobile manufacturing sector is developing self-driving cars that would monitor their own condition rather than rely on their owner-driver to spot problems and take the vehicle to a service station.
AI is being utilized in the pharmaceutical industry to track and trace their products from the product line to the end patient, thereby assisting the pharmaceutical firms to adhere to the stringent rules and regulations.
AI can assist manufacturing companies by reducing recall thereby saving companies from reputational risk. Manufacturing companies are using AI solutions like machine vision to monitor major or minor defects in the production line. Manufacturers of agricultural products are also utilizing high-performance computer vision-enabled fruits sorting machines to classify skin defects and sort fruits in sophisticated pack grades.
AI is also used for product inspection and quality control in sectors like semiconductor manufacturing where the cost of testing and failures make up for up to 20-30 per cent, as per McKinsey report. According to Forbes, automated quality testing done with machine learning can increase detection rates by up to 90 per cent.
While we see a lot of companies are digitizing their operations now, a 2018 survey report by PWC shows that only 9 per cent of companies have adopted AI for operational decision making. By 2026, AI in the manufacturing industry is expected to be valued at $16.7 billion. According to a Deloitte survey, 83 per cent of companies think AI has made or will make a practical and visible impact. Among these, 27 per cent believe AI projects have already brought value to their companies and 56 per cent think these projects will bring value in 2-5 years.
Companies in the coming two-three years will capitalize fully on the hybrid and connected smart systems for optimization of production, costs, inventory or inspection, to predict sales and prices or perform predictive maintenance. This doesn’t mean that automation technologies like AI will outplace the human workforce. Instead, these novel technologies have only created more jobs. There was an era when computers were considered a threat to the workforce but with passing time these technological advancements have benefited humankind and business in general, making upskilling a necessity and not a luxury. In fact, as per the World Economic Forum, half of the workers in the manufacturing field would require some degree of reskilling and upskilling in the next five years as AI will potentially automate 30 per cent of jobs. Hence it will become imperative for employers to invest heavily in the upskilling of their workforce to make them future-ready.
All innovations to date have only changed the way we humans work. In fact, one of the major challenges that the industry is currently facing is the dearth of talent in these domains, especially in AI/ML. The complexity of AI systems, mandates the workforce to have an understanding of technologies such as cognitive computing, machine learning, deep learning, and image recognition. This poses an excellent opportunity for employees currently associated with the manufacturing industry to upskill. Today, companies are investing heavily in manpower training and development.
AI and robotic automation has widened the future scope of the manufacturing industry. Human intelligence coupled with artificial intelligence will open new avenues in today’s paradigm and support India in becoming the manufacturing destination for the world. As they say, change is the only thing constant, AI transformations in the workforce is not a distant future. Today we all have adapted to working from home which was not imaginable in many industries until COVID-19. Similarly, the time has come for professionals to upskill and upgrade for the newer prospects in the AI world.