How AI serves as a cornerstone of Industry 4.0

For manufacturing environments to be included in Industry 4.0, they must adopt up-to-date technologies to improve operations. AI should be foremost among them.

The Fourth Industrial Revolution, or Industry 4.0, entails using the most up-to-date versions of technologies such as AI, IoT, cloud computing and big data within industrial environments and operations. For context, the First Industrial Revolution began in the latter part of the 18th century when mechanization from steam and waterpower was revolutionary. Then came the Second Industrial Revolution, which saw the advent of electrical power and mass production systems. Finally, the 20th-century Third Industrial Revolution introduced computers to business processes. The current level of digitization in industries such as manufacturing, healthcare, finance and agriculture is at a level that was once considered futuristic.

In chapter 1 of A Roadmap for Enabling Industry 4.0 by Artificial Intelligence, author Jyotir Moy Chatterjee elaborates on the increasingly important role AI plays in industries. Of all Industry 4.0 technologies, AI benefits the manufacturing sector most, in areas like production planning, predictive maintenance, machinery inspection, logistics, inventory management and process control, according to Chatterjee.

Chatterjee provides an optimistic outlook for the future of AI in industrial sectors, as new automation opportunities that complement workers come to fruition. There will even be advances in nonindustrial sectors, as AI technology makes breakthroughs in education, finance, banking, agriculture and natural disaster mitigation. The only caveat is the disparity between large enterprises and small and midsize enterprises (SMEs) in terms of uptake of Industry 4.0 practices and technologies.

"The low pace of Industry 4.0 thinking and technology adoption among SMEs is characterized as a common problem for the overall industrial development in all European regions," Chatterjee wrote. While companies everywhere increasingly adopt Industry 4.0, SMEs have a harder time for myriad reasons, including a lack of skilled workforces, cybersecurity and R&D investment.

The book delves into technologies like IoT and cloud computing, as well as how to successfully integrate them with various Industry 4.0 environments. It also describes opportunities and challenges within Industry 4.0. Readers, developers and tech enthusiasts alike can fully grasp the concept of Industry 4.0 and the technologies it encompasses.

"The book provides a conceptual framework and roadmap for decision-makers for this transformation. This book can also be useful for undergraduate and graduate students who have taken big data analytics in their academic curriculum," Chatterjee said.

The following excerpt from chapter 1 makes the case for AI as essential to Industry 4.0.

Editor's note: The following excerpt was reprinted with permission from the Wiley-Scrivener publishing partnership.

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Artificial intelligence is gaining extensive application in the manufacturing sector during the onset of the fourth industrial revolution due to the diversity, scope of application and available technologies that can be employed in different phases of the production process. Machine learning (ML), a branch of AI, is the mostly widely used application in the manufacturing sector, enabling companies to modulate the production process and enhance quality. Research studies by Capgemini show that there is an increasing trend of AI uses in the manufacturing sector globally, where nearly 29% of use cases are observed in maintenance and 27% in quality.

Overall, global surveys indicate that 60% of manufacturing companies have embarked on using AI in their businesses to enhance product quality and achieve faster production in addition to the significant benefits the technology offers regardless of the type and nature of business operation. To some extent, the COVID-19 pandemic situation has created a trend among manufacturers to employ AI applications more intensively, which caused a shift toward AI-enabled operations across many companies. Fundamentally, AI technology in the manufacturing sector contributes to the growth of companies through: predicting quality and output, predicting maintenance requirements and schedules, human-robot interfaces, generating custom-made designs, market adaptation strategies and value addition in supply chains, all of which can be achieved through applying various technologies driving the fourth industrial revolution.

AI applications in the manufacturing sector attract some strategic benefits as well, which are instrumental in: reducing human errors, helping to make faster decisions, facilitating workforces in repetitive jobs, quick assistance to troubleshooting problems, round-the-clock access, improving scope for new inventions and entries, the ability to execute tasks in remote working conditions, digital assistance and in certain fields, improving security. AI-enabled technology use cases related to the manufacturing sector include both direct and indirect applications covering upstream and downstream operations. Some of these applications of AI are best fit for logistics, robotics, supply chain management, autonomous vehicles, factory automation, IT, design and manufacturing, warehouse management, process automation, product development, visual inspection, quality control, cybersecurity, etc. In the upstream sector, AI provides assistance in supporting supply chain management operations by facilitating warehouse and logistics functions, maintaining seamless communications with suppliers, manufacturers and customers as well as procuring raw materials.

Apart from multiple use cases that AI can extend to different types of manufacturing industries, AI also has the ability to perform quality checks, forecast product demands based on customer demands and handle product inventory through some systems like digital twins, virtual agents, biometrics, process automation, image recognition and machine learning in manufacturing settings. The success of AI tools in today's industrial sector is mainly due to its ability to penetrate significantly in the manufacturing sector when easily merging with some of its subset technologies, such as machine learning, deep learning, artificial neural networks and computer vision, allowing the technology a wider reach.

Lastly, while AI-specific applications in the manufacturing sector are elaborately discussed, there are many more applications and use cases in other sectors as well, which are also being explored extensively in the current fourth industrial revolution. In the end, with the diversity of applications and variability that AI technology is going to provide to the manufacturing sector, AI-enabled operations will also play a significant role in future industrial revolutions.

Excerpted with the permission of the publisher, Wiley-Scrivener, from A Roadmap for Enabling Industry 4.0 by Artificial Intelligence by Jyotir Moy Chatterjee, Harish Garg and R.N. Thakur. Copyright 2023 by Scrivener Publishing. All rights reserved. This book is available wherever books and eBooks are sold.

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