Industrial AI: Data-Driven Applications in Smart Manufacturing (A Manager's Guide)
Around the same time in 2019, we published Industry 4.0: Navigating the Manufacturing Revolution in ASEAN with a strong belief that Industry 4.0 will take the way up to transform ASEAN's economy for the long term. With the fourth industrial revolution, we are moving from automation to highly connected, intelligent, cyber-physical-enabled ecosystems. Technologies automate and augment the human worker to elevate efficiency and productivity in all facilities. Technologies also enable new processes and business models that spur a new manufacturing age. In 2021, we published our 2nd book, "Smart Factory: Technologies and Applications", and an updated edition in 2023, focusing on manufacturing executives, engineers, operation managers, policymakers and future practitioners. We cover their interests as comprehensively as possible to enable the manufacturing communities to learn about the latest trends, technologies and use cases in smart factories.
Why did we write an AI book for manufacturing?
Industrial AI is one of the most promising and fast-growing artificial intelligence applications and machine learning technologies. However, transferring academic AI research into real-world industrial solutions poses unique technical and operational challenges. Having worked on numerous Industrial AI projects across various manufacturing sectors, we encountered several gaps between academic knowledge and industrial needs.
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While deep learning techniques have achieved human-level performance on vast academic datasets, industrial constraints require adaptation. The book explores approaches to applying use cases when faced with limited plant infrastructure and practical workflows and procedures.
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For example, we showcase how generative models like GANs can synthetically expand small image datasets for defect detection. These techniques empower readers to leverage cutting-edge models without prohibitive data requirements.
Various methodologies have grown increasingly important for productionising AI at scale. The book provides best practices for model development that facilitate collaboration while ensuring rigorous change management. Readers will learn data management and preparation as inputs evolve.
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The book also outlines the AI strategy framework that guides modern manufacturing firms through technology adoption. The book empowers manufacturing firms to cement AI as a competitive advantage by covering academically robust methods within industrial practicalities. End-to-end guidance drives continuous innovation through principles of systems thinking, reliability and transparency. While academic literature provided excellent foundations, practical knowledge on developing reliable AI systems for production environments could have been more extensive. Common mistakes could be avoided by learning from others' experiences. Furthermore, successful deployment requires seamlessly integrating AI with existing operational technologies (OT) used in factories - an aspect that has yet to be covered.
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We also found that cross-industry collaborations improved while individual companies shared knowledge internally. Each organisation struggled and re-invented solutions to similar problems in isolation. By openly documenting proven methodologies, the entire industry could progress faster.
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Thus, we saw an opportunity to bridge the gap between research and application through an authoritative book on Industrial AI. We aimed to offer comprehensive and practical guidance spanning the entire development lifecycle—from data collection and modelling to deployment, integration, and maintenance. We illustrated techniques through varied real-world case studies and highlighted best practices, patterns, and pitfalls to avoid.
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By codifying our learnings into an approachable reference, we wanted to help AI practitioners tackle industrial projects confidently and optimise professionals leveraging AI to optimise operations. Most importantly, by facilitating cross-pollination of knowledge, our book can accelerate the adoption of impactful and sustainable Industrial AI worldwide.?
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Structure and extract of this book
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Chapter 1: Manufacturing Industry Overview
Chapter 2: Manufacturing Data Revolution
Chapter 3: Value of Data in Industry 4.0 Era
Chapter 4: Evolvement to Data-driven IT/OT Convergence
Chapter 5: Introduction To Artificial Intelligence
Chapter 6: Demystify AI and ML
Chapter 7: Industrial AI Use Cases
Chapter 8: Industry AI – Trends and Forecast
Chapter 9: Drive Industrial AI Strategies
Chapter 10: Implementing Industrial AI
"Industrial AI: Data-Driven Applications in Smart Manufacturing" is now available at www.amazon.com
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For more information, visit www.scaleelement.com
We look forward to you joining us for a voyage into the future of "Smart Manufacturing".
Best Regards
Colin Koh & Michael Deng