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How AI Powers Manufacturing Systems To Leapfrog Into The Future

With its potential to enhance and extend the capabilities of humans, and help businesses improve productivity, there’s no question that artificial intelligence (AI) and machine learning are transforming the manufacturing industry.

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With its potential to enhance and extend the capabilities of humans, and help businesses improve productivity, there’s no question that artificial intelligence (AI) and machine learning are transforming the manufacturing industry. 

According to Seagate’s latest survey on AI adoption in the Asia Pacific region – Data Pulse: Maximising the Potential of Artificial Intelligence – more than one-third (38%) of Indian organisations are already using AI in their supply chain, with almost all (99%) planning to implement the technology across their organisation over the next year. Further, over 90% of the respondents in India agree AI will have a growing impact on their organisations in the future, and they must adopt AI to stay relevant. 

AI for Growth 

The road to AI in the manufacturing sector makes economic sense. According to the World Economic Forum, nearly 80% of companies in the manufacturing sector worldwide predict that implementing AI initiatives will result in revenue increase of 22.6% and cost reduction of 17.6%[i]. Additionally it has been predicted that by 2025, ‘smart factories’ will generate close to $3.7 trillion in value.

Additionally, India is expected to become the fifth largest manufacturing country in the world by 2022, with the government aiming to have its manufacturing sector make up to 25% of its GDP by the same year[ii].

Our Data Pulse survey found 9 in 10 organisations in India are already utilising AI solutions in their businesses, one of the highest adoption rates in Asia Pacific. Several are already using AI in manufacturing, to operational advantage. Let’s look at some of them. 

  • Japanese electronics company Panasonic has set-up a ‘Technopark’ in Jhajjar in the state of Haryana where they manufacture air conditioners and washing machines. The production line and testing process is automated and controlled by AI.
  • GE’s Brilliant Factory in Pune harnesses technologies such as AI, IoT, big data analytics and cloud computing to support decision-making within the factory.
  • BHEL (Bharat Heavy Electricals Limited) has put into effect AI techniques for process monitoring, control and maintenance systems.
  • Bosch, the German company, has a main centre in Bengaluru and 14 manufacturing plants across India. It endeavours to replicate smart manufacturing across all locations in sync with the ‘Industry 4.0’ model by the end of 2018. 
  • Mitsubishi Electric India is investing in AI research, and has introduced Maisart (Mitsubishi Electric's AI state-of-the-art) – an AI system that can be adopted in manufacturing industries. The company has already implemented its AI solution on a chip in their air conditioning systems. This “3D i-See Sensor” system is capable of distinguishing human beings and pets existing in a given space. The system guarantees optimum comfort in terms of ambient temperature by using eight sensors to take measurements in 1,856 different spots in the space in 232 steps in order to eliminate negative conditions that may arise from temperature differences in the room

AI for Performance

Production quality is critical in the manufacturing sector, and defects can potentially erode up to 30% of an organisation’s annual revenue. The practice of predictive maintenance of industrial equipment using AI can also bring down annual maintenance costs by 10%, and reduce downtime by up to 20% as well as inspection costs by 25%. Our survey findings reinforce this, with 95% of the Indian organisations indicating that they see the possibility of AI driving productivity and performance.

With AI powering the production line, real-time analysis of data generated from sensors can help identify and rectify quality issues almost as soon as they arise. Data that is generated by a smart UPS (uninterruptible power supplies) source and processed by an AI engine can bring about better decisions not just concerning power consumption but machinery maintenance as well. Computer vision (CV) technology helps improve quality assurance through the detection of product defects in real time. Machine learning technology together with AI is widely used in industrial processes in oil refineries, chemical industries, plastics and semiconductor manufacturing.

While a traditional factory generally produces goods, a smart factory produces both goods and volumes of data. In a connected factory, data will be generated from in-house smart machines as well as the supply chain, with sensors and machines tracking material and inventories through the value chain. Organisations find that performance logs from a single machine can generate close to 5 GB of data each week, while a smart factory produces approximately 5 petabytes of data every week, equivalent to 5 million GB. 

AI for Competitive Advantage

Clearly, AI adoption is helping to drive ‘Industry 4.0’, enabling manufacturers to successfully implement predictive and preventive maintenance, flexible automation, automated quality control and demand-driven production. Large manufacturers rely on AI in decision-making in regard to material purchase, input allocation, production volumes, delivery dates, spare capacity utilisation, cost reduction and capital optimisation. 

However, being able to expand network storage capacity quickly and cost effectively without disrupting service level agreements is critical in today’s world of data. Our survey underlines the importance of managing data in an AI-led manufacturing economy, with an overwhelming 98% of India organisations indicating a need to invest in IT infrastructure to handle the ever-increasing stream of data.

According to the Data Age 2025 IDC report sponsored by Seagate, by 2025 the global datasphere subject to data analysis will grow to 5.2ZB, and the amount of analysed data that is processed by machine learning, natural language processing, and artificial intelligence will grow by a factor of 100 to 1.4 ZB. In the wake of what can be coined as a ‘tsunami of data’, businesses should ascertain their data storage needs to realise their artificial intelligence (AI) implementation ambitions, and stay not just in the game but ahead of the curve. 

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.

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B.S. Teh

The author is Senior Vice President of Global Sales & Sales Operations, Seagate Technology.

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