AI-Empowered Manufacturing – A Move Towards Sustainable Development

The Covid-19 pandemic has driven a sharper focus on digitalisation and sustainability as two critical areas for sustainable economic growth and development.

Artificial Intelligence (AI) shows promise in assisting the achievement of Sustainable Development Goals (SDGs). Research shows that AI – more specifically Internet of Things (IoT) – can help develop smart and low-carbon cities with a range of interconnected technologies and appliances, that optimize, for example, demand responses in the electricity sector. More importantly, it was found that manufacturing processes optimized by AI lead to significant savings in energy, contributing to environmental sustainability.

AI can exponentially increase manufacturing efficiency through automation and data. A considerable impact is observed in developing a path towards a circular economy through closed-loop supply chains – supporting the concept of zero-waste manufacturing. By providing real-time product information, IoT assists in end-of-life activities such as refurbishment or recycling.

Countries are applying AI solutions to aid sustainable manufacturing and encouraging startups developing AI technologies. For instance, Fanuc of Japan, operates its factories continuously without stoppage, using robotic workers. The robots produce components for CNCs and motors and continuously monitor all operations – thereby achieving optimal productivity. Startups and multinationals alike have made forays into the space, example Siemens’ partnership with Google to increase shop floor productivity using cloud-based analytics, AI algorithms and machine learning, driving considerable automation.

AI assisted manufacturing is burgeoning within India as well. The Central Government’s Budget proposals earlier this year encouraged investments in startups developing AIs that assist in achieving sustainable development. AI startups have also been boosted with the involvement of venture capital firms and global companies, with investments into the local AI start-up ecosystem rising to $1.1 billion in 2021 as compared to the $836.3 million in 2020, representing a year-over-year growth of 32.5%.

India’s Covacsis Technologies Ltd has developed the Intelligent Plant Framework (IPF) which is used in plants across various sectors. Working without any peer assistance, IPF relies on Big Data analytics and IoT to provide real-time outputs and continuously monitors data from all machines to optimize for higher OEE. On the other hand, Tech Mahindra has released its automation platform called Automation, Quality, Time (AQT) which helps increase business effectiveness and efficiency for stakeholders using consolidated automation platforms. Panasonic also recently opened their ‘Technopark’ in Jhajjar, Haryana, where production activities and testing processes are automated and controlled by AI.

The global AI in manufacturing market size was estimated to be $1.82 billion in 2019, and is forecast at $9.89 billion by 2027, a CAGR of 24.2%. In comparison, in India, AI penetration in the manufacturing industry stood at $157 million in 2020, and is estimated to reach $512 million by 2025, a CAGR of 4.83%. Currently, the Asia-Pacific region contributes the largest share of AI in the manufacturing market, much of it contributed by major manufacturing companies based in China, South Korea and Japan, leaving considerable room for improvement for Indian manufacturers.

A lack of robust domestic infrastructure is one key issue. For instance, cloud computing infrastructure, required by AI for storing massive amounts of data and computing power, exists primarily outside India, and companies providing AI solutions in India are largely dependent on foreign infrastructure. Consequently, the absence of such infrastructure in India has made AI and cloud computing services relatively expensive, further disincentivizing investments in the field.

India’s cultural environment poses a challenge as well. Many investors are still hesitant to invest, absent a better understanding of AI systems, as well as the security and data privacy risks that arise from the use of such technologies. Moreover, the level of skill and education required to operate these technologies at the shop-floor level is severely lacking, and a shift to AI-integrated processes will require companies to reskill and educate their workforce calling for further investment.

In addition to these inherent weaknesses, the Government’s emphasis on Industry 4.0 drives increasing levels of automation in factories but could have negative connotations for the growing employable population. The shift to Industry 4.0 and the streamlining of manufacturing processes could serve to exacerbate the issue of growing unemployment, an issue that could serve as a barrier to the implementation of AI, with the government having to then balance citizen welfare and the free market.

Although there continues to exist numerous barriers to entry, various steps have been taken to mitigate these, such as NASSCOM’s proposal to set up AI and data science centers in Hyderabad and Bengaluru, as well as their agreement with the Dalian Municipal People’s Government, which provides India with an opportunity to delve into China’s AI market. Furthermore, initiatives such as Skill India assist in educating and reskilling the workforce with competencies required for a shift to Industry 4.0. Thus, in spite of the challenges faced, the functioning and complete development of AI, is dependent on the Government working alongside the private sector to ensure adequate education, reskilling, and investments in native infrastructure – a goal which is now clearly in sight, with AI projected to add $500 billion to India’s GDP by 2025, accounting for 10% of the government’s $5 trillion economy aspiration.

In order to achieve this goal, moving forward there needs to be increased integration of AI and sustainable development in education curriculums in order to bring about changes in attitudes, as well as accelerated removal of regulatory barriers which impact the level of AI implementation and investments in the field.

This has been co-authored by Malvika Jain and Prajit Sarkar.