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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A ROADMAP FOR ENABLING INDUSTRY 4.0 BY ARTIFICAIAL INTELLIGENCE
The book presents comprehensive and up-to-date technological solutions to the main aspects regarding the applications of artificial intelligence to Industry 4.0.
The industry 4.0 vision has been discussed for quite a while and the enabling technologies are now mature enough to turn this vision into a grand reality sooner rather than later. The fourth industrial revolution, or Industry 4.0, involves the infusion of technology-enabled deeper and decisive automation into manufacturing processes and activities. Several information and communication technologies (ICT) are being integrated and used towards attaining manufacturing process acceleration and augmentation. This book explores and educates the recent advancements in blockchain technology, artificial intelligence, supply chains in manufacturing, cryptocurrencies, and their crucial impact on realizing the Industry 4.0 goals. The book thus provides a conceptual framework and roadmap for decision-makers for implementing this transformation.
Audience
Computer and artificial intelligence scientists, information and communication technology specialists, and engineers in electronics and industrial manufacturing will find this book very useful.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 Artificial Intelligence—The Driving Force of Industry 4.0
1.1 Introduction
1.2 Methodology
1.3 Scope of AI in Global Economy and Industry 4.0
1.4 Artificial Intelligence—Manufacturing Sector
1.5 Conclusion
References
2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview
2.1 Introduction
2.2 Industrial Transformation/Value Chain Transformation
2.3 IIoT Reference Architecture
2.4 IIoT Technical Concepts
2.5 IIoT and Cloud Computing
2.6 IIoT and Security
References
3 Artificial Intelligence of Things (AIoT) and Industry 4.0–Based Supply Chain (FMCG Industry)
3.1 Introduction
3.2 Concepts
3.3 AIoT-Based Supply Chain
3.4 Conclusion
References
4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0
4.1 Introduction
4.2 Literature Review
4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting
4.4 Proposed Approach
4.5 Discussion and Conclusion
References
5 Integrating IoT and Deep Learning— The Driving Force of Industry 4.0
5.1 Motivation and Background
5.2 Bringing Intelligence Into IoT Devices
5.3 The Foundation of CR-IoT Network
5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network
5.5 Realization of CR-IoT Network in Daily Life Examples
5.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study
5.7 Conclusion
References
6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment
6.1 Introduction
6.2 Overview of Blockchain
6.3 Components of Blockchain
6.4 Safety Issues in Blockchain Technology
6.5 Usage of Big Data Framework in Dynamic Supply Chain System
6.6 Machine Learning and Big Data
6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems
6.8 IoT-Enabled Blockchains
6.9 Conclusions
References
7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet
7.1 Introduction
7.2 Related Work
7.3 Methodology
7.4 Results
7.5 Conclusion and Future Scope
References
8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems
8.1 Introduction
8.2 Related Work
8.3 Proposed Mechanism
8.4 Experimental Results
8.5 Future Directions
8.6 Conclusion
References
9 Environmental and Industrial Applications Using Internet of Things (IoT)
9.1 Introduction
9.2 IoT-Based Environmental Applications
9.3 Smart Environmental Monitoring
9.4 Applications of Sensors Network in Agro-Industrial System
9.5 Applications of IoT in Industry
9.6 Challenges of IoT Applications in Environmental and Industrial Applications
9.7 Conclusions and Recommendations
Acknowledgments
References
10 An Introduction to Security in Internet of Things (IoT) and Big Data
10.1 Introduction
10.2 Allusion Design of IoT
10.3 Vulnerabilities of IoT
10.4 Challenges in Technology
10.5 Analysis of IoT Security
10.6 Improvements and Enhancements Needed for IoT Applications in the Future
10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS)
10.8 Conclusion
References
11 Potential, Scope, and Challenges of Industry 4.0
11.1 Introduction
11.2 Key Aspects for a Successful Production
11.3 Opportunities with Industry 4.0
11.4 Issues in Implementation of Industry 4.0
11.5 Potential Tools Utilized in Industry 4.0
11.6 Conclusion
References
12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges
12.1 Introduction
12.2 Changing Market Demands
12.3 Recent Technological Advancements
12.4 Industrial Revolution 4.0
12.5 Challenges to Industry 4.0
12.6 Conclusion
References
13 The Role of Multiagent System in Industry 4.0
13.1 Introduction
13.2 Characteristics and Goals of Industry 4.0 Conception
13.3 Artificial Intelligence
13.4 Multiagent Systems
13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns
13.6 Conclusion
References
14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security
14.1 Introduction
14.2 Reviews of Related Works
14.3 Materials and Methods
14.4 Discussion and Conclusion
References
15 Market Trends with Cryptocurrency Trading in Industry 4.0
15.1 Introduction
15.2 Industry Overview
15.3 Cryptocurrency Market
15.4 Cryptocurrency Trading
15.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain
15.6 Conclusion
References
16 Blockchain and Its Applications in Industry 4.0
16.1 Introduction
16.2 About Cryptocurrency
16.3 History of Blockchain and Cryptocurrency
16.4 Background of Industrial Revolution
16.5 Trends of Blockchain
16.6 Applications of Blockchain in Industry 4.0
16.7 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Contributing factors driving the 4 IR analyzed from literature.
Table 1.2 Companies showing areas AI uses for enhancement in their manufacturi...
Table 1.3 Information technology companies using AI-enabled technologies emplo...
Chapter 4
Table 4.1 Nine technologies that are used in Industry 4.0 [19, 20].
Chapter 7
Table 7.1 Exploratory data analysis output.
Table 7.2 The output of the Prophet Model.
Table 7.3 The output of training data.
Chapter 9
Table 9.1 Commercially available sensors for smart agricultural use.
Table 9.2 Autonomous field applications.
Table 9.3 Software field applications.
Chapter 10
Table 10.1 Comparative analysis of techniques for intrusion detection.
Chapter 15
Table 15.1 Nomics website.
Chapter 16
Table 16.1 The trend of mentions of the term “blockchain” over the years, from...
List of Illustrations
Chapter 1
Figure 1.1 World regions gaining impact from artificial intelligence through G...
Figure 1.2 Funding on different AI applications and percent share on global in...
Figure 1.3 Classification of AI technology and its allied functions showing ap...
Chapter 2
Figure 2.1 Industrial revolution adapted from the study of Horvath [5].
Figure 2.2 IIoT reference architecture adapted from Microsoft Azure [9].
Figure 2.3 IoT data analytics. Adapted from AWS IoT [3].
Chapter 3
Figure 3.1 IoT ecosystem.
Figure 3.2 Elements of AIoT.
Figure 3.3 Big data generation sources in the FMCG industry.
Figure 3.4 Communication framework for the AIoT supply chain.
Chapter 4
Figure 4.1 An overview of the four industrial revolutions.
Figure 4.2 Nine technologies that are changing industrial production [18].
Figure 4.3 The general structure of the studded supply chain.
Figure 4.4 The proposed steps of forecasting supply chain demand.
Chapter 5
Figure 5.1 IoT—A network that connects everything to a single device and facil...
Figure 5.2 IoT growth in certain applications in certain applications [7].
Figure 5.3 Introducing intelligence into IoT devices.
Figure 5.4 A simple CNN architecture consisting of several layers [43].
Figure 5.5 AI-enabled smart irrigation system.
Figure 5.6 Temperature data of the December month received from sensors instal...
Figure 5.7 Temperature data of the January month received from sensors install...
Figure 5.8 Temperature data of the February month received from sensors instal...
Figure 5.9 Temperature data of the May month received from sensors installed i...
Figure 5.10 Temperature data of the June month received from sensors installed...
Figure 5.11 Temperature data of the July month received from sensors installed...
Figure 5.12 Time-Frequency analysis of the temperature data received from sens...
Figure 5.13 Predicted output by the LSTM network.
Chapter 6
Figure 6.1 Blockchain connection framework.
Figure 6.2 The data block structure.
Figure 6.3 Represents the transaction in bitcoin.
Figure 6.4 Chan’s big data framework analysis.
Figure 6.5 Modules in an Internet of Things scenario.
Chapter 7
Figure 7.1 PJM data set.
Figure 7.2 Plotting of trends and features.
Figure 7.3 Train/test split.
Figure 7.4 PJM data set with bad data.
Figure 7.5 PJM data set with bad data removed.
Figure 7.6 Forecast of energy consumption.
Figure 7.7 Forecast for weekly, yearly, daily, and trends energy consumption.
Figure 7.8 Forecast of energy consumption with actual reading.
Figure 7.9 Forecast of energy consumption with actual reading for first month.
Figure 7.10 Forecast of energy consumption with actual reading for the first w...
Figure 7.11 Forecast for weekly, yearly, daily, trends along with holidays ene...
Figure 7.12 Forecast vs. actual.
Figure 7.13 Forecast vs. actual for 4th of July of 2015 to 2018.
Chapter 8
Figure 8.1 Impact of number of cohorts against percentage of privacy leak.
Figure 8.2 Impact of number of ribbon filters against percentage of privacy le...
Figure 8.3 Comparative performance of popular approaches for privacy preservat...
Chapter 9
Figure 9.1 Internet of Things (IoT) concept.
Figure 9.2 Number of publications concerning (a) IoT different applications, (...
Figure 9.3 An overview of the industrial revolutions.
Figure 9.4 IoT-based smart environment.
Figure 9.5 Connection of sensor with microcontroller.
Figure 9.6 Capabilities of sensors in Industry 4.0.
Figure 9.7 Various sensor types for Industry 4.0.
Figure 9.8 Challenges of IoT application in environmental issues.
Chapter 10
Figure 10.1 A scenario of Internet of Things (IoT).
Figure 10.2 Different components of the Internet of Things.
Chapter 13
Figure 13.1 Stages of industrial development.
Figure 13.2 Parts of artificial intelligence.
Figure 13.3 Traditional control of manufacturing systems.
Figure 13.4 Relationship between the main architectural elements.
Figure 13.5 Agent container AMS.
Chapter 15
Figure 15.1 Monetary base in USD [1].
Figure 15.2 Workflow of blockchain technology.
Figure 15.3 PoW Vs PoS.
Figure 15.4 Bitcoin mining’s carbon footprint (Susanne Köhler and Massimo Piz...
Figure 15.5 Comparison between bitcoin and Ethereum carbon footprint (2019).
Chapter 16
Figure 16.1 Working steps of Blockchain [53]. This depicts the different proce...
Figure 16.2 Industrial revolution from Industry 1.0 to Industry 4.0. This figu...
Figure 16.3 Visualization of the trend of mentions of the term “blockchain” ov...
Figure 16.4 Four elements or constituents of the healthcare industries.
Figure 16.5 Blockchain in logistics. This illustration depicts how blockchain ...
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
A Roadmap for Enabling Industry 4.0 by Artificial Intelligence
Edited by
Jyotir Moy ChatterjeeHarish GargandR. N. Thakur
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-90485-4
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
The Industry 4.0 vision has been baking for quite a while, but the enabling technologies are now ripe enough to turn this vision into a grand reality sooner rather than later. Known by many as the fourth industrial revolution, it involves the infusion of deeper and more decisive technology-enabled automation into manufacturing processes and activities. Several illustrious information and communication technologies (ICT) are being used to attain the acceleration and augmentation of the manufacturing process, and this book explores and discloses the recent advancements in blockchain technology and artificial intelligence and their crucial impact on realizing the goals of Industry 4.0. In other words, it provides a conceptual framework and roadmap for decision makers to make this transformation.
In addition to undergraduate and postgraduate students with big data analytics as part of their curriculum, this book will be useful to students who want to develop and deploy their skills to achieve a thorough knowledge of analytics and have an in-depth education experience that pertains to their unique interests. Moreover, acknowledging the huge demands of industry professionals today, this book has also been designed to create trained computer science graduates to fulfill industry requirements. Furthermore, artificial intelligence applications in the industrial sector are explored, including IoT technology. Up-to-date technology and solutions in regard to the main aspects of applications of artificial intelligence techniques in Industry 4.0 are also presented, thereby covering the training needs of the scientific research community. Finally, for your convenience, the information presented in the book’s well-conceived chapters is summarized below.
Chapter 1
is a systematic literature review on how Industry 4.0 has been remarkably successful due to the multifaceted specialisms in digitalization and artificial intelligence (AI).
Chapter 2
starts with an overview of the evolution of Industry 4.0 that enabled digital transformation in manufacturing and other related industries. Next, the chapter dwells on how cloud computing can economically support IoT on a large scale.
Chapter 3
discusses the high importance of the supply chain in manufacturing businesses, especially in fast-moving consumer goods (FMCG) industries, which are one of the most important industries in our daily lives. It provides a framework for using this technology in the supply chain in these industries.
Chapter 4
provides a comprehensive framework for accelerating supply chain decisions with respect to Industry 4.0.
Chapter 5
briefly introduces IoT and deep learning models. Subsequently, a detailed description of the deployment of deep learning methods in IoT networks for industrial application is presented.
Chapter 6
attempts to show several ideas about the function-alities of blockchain technology that could be widely applied in a wide range of circumstances.
Chapter 7
aims to identify the approaches that can be used to identify value depending on the factors in the datasets and correct the missing data value, which can be used later on to analyze the data better.
Chapter 8
presents the novel privacy issues caused by the advent of AI techniques in a cyber-physical system ecosystem. It then moves on to discuss the measures used to mitigate such issues.
Chapter 9
reviews most IoT-based environmental applications for a smart environment, agriculture, and smart environmental monitoring of air, soil, and water.
Chapter 10
outlines many of the IoT protection threats or problems and countermeasures in a level-by-level manner.
Chapter 11
reviews all the opportunities presented by Industry 4.0 and the challenges involved in implementing it.
Chapter 12
discusses the manufacturing techniques of Industry 4.0 along with their various opportunities and challenges.
Chapter 13
describes the essence of Industry 4.0 along with its primary objectives and basic features.
Chapter 14
proposes methods for improving multimedia encryption standards in explainable artificial intelligence using residue number systems for security.
Chapters 15
and
16
appropriately complete the book focusing on “Market Trends with Cryptocurrency Trading in Industry 4.0” and Blockchain and Its Applications in Industry 4.0.
Jyotir Moy Chatterjee
Kathmandu, Nepal
Harish Garg
Patiala, India
R.N. Thakur
Kathmandu, Nepal
October 2022
1Artificial Intelligence—The Driving Force of Industry 4.0
Hesham Magd1*, Henry Jonathan2, Shad Ahmad Khan3 and Mohamed El Geddawy4
1Quality Assurance & Accreditation, Faculty of Business and Economics, Muscat, Sultanate of Oman
2Department of Transportation, Logistics, and Safety Management, Faculty of Business and Economics, Muscat, Sultanate of Oman
3College of Business, University of Buraimi, Al-Buraimi, Sultanate of Oman
4Prince Mohammed University, Al Khobar, Kingdom of Saudi Arabia
Abstract
The 21st century reminds the dawn of the 4th industrial revolution (IR), which has brought new frontiers of technology utilization to industries. Industry 4.0 chiefly relays from the 3rd revolution, taking further the application of computer and automation domain to the current century. Industry 4.0 already has shown remarkable success due to the multifaceted specialisms in digitalization and artificial intelligence (AI). However, in the face of rapidly changing technology, the growth of industrialization depends significantly on the progressive involvement of artificial intelligence applications in wide areas of industrial products and processes. AI technology potential contribution to the global economy is $15.7 trillion currently with 2% contribution from the middle east region. The manufacturing sector investment and funding on project gained in USA and China through AI-enabled technologies by automotive and IT sectors leading GDP by 115% accounting to $77.5 billion in 2021. The factors driving the industry 4.0 mainly are competitiveness and innovation, cost reduction, and performance improvement, which led to the use of AI technology applications to industrial sector. AI-enabled applications are explored in automotive, consumer products, industrial manufacturing, telecommunication sectors to enhance product quality, design in the process. Despite the benefits offered by the AI applications to industries, the barriers for effective adoption of industry 4.0 ideology to all sectors is a time-taking process.
Keywords: Technology, organization, manufacturing, industry, sector, application, tools
1.1 Introduction
The current century marks the renaissance of fourth industrial revolution the term that was first introduced by Klaus Schwab in 2016 to denote the future trend of industrial world [1]. The industry 4.0 principles are built upon the developments of third industrial revolution that already has introduced computerization technology to the world. This paved the way to the most prominent applications, such as automation and artificial intelligence, and in addition to digital transformation of process and business operations along the different industrial revolutions [2]. The first industrial revolution marks the advent of mechanization from steam and waterpower during 1750 and 1850, followed by second revolution bringing in electrical power and mass production systems during 1870 to 1914 and third revolution in the 20th century gave entry of computer and electronics into industrial process after 1970.
Currently, the industry 4.0 began during 2014, where it works on conceptualizing the use of innovative technology and advanced digital production together contributing to a sustainable industrial development [3, 4]. According to research studies by UNIDO, industry 4.0 scope has not made full advancement in all sectors as of yet, in Argentina and Brazil claims that 3% to 4% of firms have adopted few applications, while countries like Ghana, Vietnam and Thailand has very much behind in exploiting the benefits of the fourth revolution.
In this viewpoint, this chapter will deal in describing the role of artificial intelligence technology particularly in spearheading the industry 4.0 concept in the context of manufacturing sector globally. In combination, the chapter will likewise attempt to provide a comprehensive account on the factors that are considered contributing to the employment of industry 4.0 across different organizations.
1.2 Methodology
The purpose of the chapter is to provide an overview and in-depth understanding of the factors that are driving the industry 4.0 in the perspective of artificial intelligence technology. To elaborate the details, published literature and reports from previous studies are referred to meet the objectives of the study, and secondary sources of information by visiting different organizational websites were conducted to examine the factors contributing to the industry 4.0. Thorough searches from previously published work by various researchers was conducted to examine the different driving factors for industry 4.0 and were analyzed. Information related to the industry 4.0 technologies, their roles toward manufacturing sector, statistical information on status global economy with artificial intelligence technology were collected from secondary data published in government information portals and global private organizations like price Waterhouse coopers, McKinsey & Company, Deloitte etc. For presenting the findings from the analysis, the authors have maximized the available information published in researchers and details provide in various organizational websites.
1.3 Scope of AI in Global Economy and Industry 4.0
The fourth industrial revolution is fueled from its predecessor industrial phase essentially pushing the trend toward automation and fully integrating the digitalization process in industries, expanding the applicability through some of the significant applications like internet of things (IoT), cyber physical systems, smart factory, cloud computing, cognitive computing, artificial intelligence, etc. Theoretically, the fourth industrial revolution is driven by four key ideologies, enabling interconnection between different technological applications, transparency in information and procurement, technical assistance, and regionalized decisions enabling the industry 4.0 concept a distinctive approach from the third revolution [5].
Artificial intelligence (AI) can be explained as the use of computer programming to mimic human thought and actions to create human-like responses through machine learning, logic, perception, and reasoning. Legendary physicist Stephen Hawking quotes “Success in creating effective AI could be the biggest event in the history of our civilization. Or the worst. We just don’t know. So, we cannot know if we will be infinitely helped by AI, or ignored by it and side-lined, or conceivably destroyed by it.”
Artificial intelligence (AI) obviously is a significant technology among the other technological applications in the industry 4.0 as it principally works on the foundation of computer learning and augmentation pertinent to the entire industry ecosystem. Most importantly, AI technology has tremendous scope for its application in manufacturing industries in the production and design stages enabling reduction in human errors, production efficiency and reduction in losses. Moreover, the use of AI techniques, such as machine learning and deep learning, have substantial contribution toward advancement of global economy as the global race for embedding AI into businesses have already started reaching milestones in some countries, for example 82% of Spanish companies have explored AI technology in their industrial system.
Following the prospective growth in utilizing AI technology, global projection shows the technology has the potential to contribute $15.7 trillion to the economy by 2030 of which middle east expects to claim 2% of the benefits accounting to $320 billion. AI Technologies in the GCC four countries (Bahrain, Kuwait, Oman, and Qatar) is likely to contribute around 8.2% of GDP by 2030, while in comparison to no middle east economies, the share of AI to the GDP is large, 26.1% in China, and 14.5% in North America. Notwithstanding to say, the prospects of AI technology to industries especially in the developing nations is tremendous as the potential for embedding AI in manufacturing and allied sectors is increasing gradually. In addition, in the UAE, KSA, GCC, and Egypt, the annual growth of AI from 2018 to 2030 is expected to be 33.5%, 31.3% 28.8%, and 25.5%, respectively [6].
In developed nations, the importance of AI technology to business achievement shows more promising growth in the UK from 14 % to 45%, United States 11% to 42%, Germany 4% to 32%, Australia 12% to 29% in the next few years [7]. Regional analysis on the investment scenario on AI in Asian markets shows $0.69 billion in 2019 with the global market expected to reach $9.89 billion by 2027, in the UK, investment on AI stands at $1.3 billion; further, there are growth returns expected from the increasing investment on AI north and south America according to Market research report of Fortune Business Insights [8].
1.3.1 Artificial Intelligence—Evolution and Implications
The period from 1950 to 1960 marks the birth and evolution of AI to the world, later in the six decades of its journey, the technology has taken leaps and bounds along with the industrial revolutions [9]. Presently, in the 21st century, AI is certainly the prominent technology for the industry 4.0 and for the future. Global economists also estimate the wide applications AI will be very much instrumental for global expansion in the future, and quite obviously AI will be one of the most preferred technologies to be used by companies by the year 2025 according to the reports by world economic forum [10]. Added to this, COVID-19 pandemic has necessitated remote work and work for home policies, which has created potential demand for industries to rely upon some of the allied technology applications of AI to business operations. Symbiotically, AI technology came to the aid of many researchers, planners, and decision makers in tracking, predicting the movement and evolution of COVID pandemic from 2020 [9].
In the present industrial revolution, AI technology is making remarkable contribution in multiple sectors boosting production and enhancing business operations in healthcare, financial and banking, agriculture, transport, science, and research etc., [11]. AI in the next industrial revolutions prospectively will take a leap into many businesses and industries making better integration between humans and machines. In contrast to the diverse benefits AI would provide, the disadvantages from the use of AI in certain fields far outweigh the advantages, and employing AI in some sensitive fields certainly have considerable risks and implications that will pose to societies, and in general to low-economic countries particularly escalating unemployment further and other repercussions [12].
1.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy
The global industrial sector is bound to get complete overhaul from the invasion of digitalization transformation and digitalization of operations in the industry 4.0 era where artificial intelligence is going to be the futuristic concept in the next decades. Global economic empires, the United States of America (USA) and China, are leading the way on total investments in different AI-enabled technologies across all industrial sectors. We can convincingly say that the global AI in terms of investment and funding on projects is at a remarkable position. The global investment and finance on AI were $40 billion in 2017, of which 70% were in China alone followed by the US, resulted in a projected increase of GDP in manufacturing and construction sector by 6.5% by 2030 [13].
Reports from anonymous studies show global investment on AI-enabled companies increased by 115% that accounts to $77.5 billion in 2021 over the previous years. The GDP growth from AI-enabled technologies would be 26.1% in China and 14.5% in North America together contributing $10.7 trillion of global economy by 2030. Leading business analysts predict that the global GDP will rise to 14% by 2030, the highest effect to USA and China in the forefront; however, all regions would obviously benefit [6] (Figure 1.1).
In the manufacturing sector, the AI investments mainly in software, hardware, and services were $2.9 billion in 2018 and expected at $13.2 billion by 2021 with a projected spending of $9.5 during the year 2021 in the sector standing second to banking and financial services [14]. The increasing funding and returns from investment on AI show an increasing curve during the current industry 4.0 because of increasing AI-enabled companies and business startups globally, which stand at 4925 enterprises and 3465 startups by 2018. USA and China are currently global leaders in having the highest number of AI enterprises and startups by 2018, and the overall funding on AI across all industries in the USA from 2012 to 2018 rose from $595 million to $4218 million, while the funding on AI startups in the first half of 2021 was $38 million [13].
Figure 1.1 World regions gaining impact from artificial intelligence through GDP growth in percent and trillion US dollars.
Figure 1.2 Funding on different AI applications and percent share on global investment and financing projects up to 2018.
In China, the funding on AI-related projects by the end of 2018 was $428.9 million with the AI market size gaining an increase from $1.6 billion in 2015 to $14.3 billion in 2020. Leading the way are top global companies like Google, Amazon, Apple, Intel, IBM, Microsoft, Facebook, Twitter, etc. are in the forefront of investments and development in AI across all applications. The projected GDP increase from funding on AI in Chinese manufacturing and construction sector by 2030 would experience 12% in productivity and 11.5% in consumption. Such futuristic scenario is also going to boost employment opportunities for AI professionals in all sectors with estimates reporting around 1.9 million in 2017 with highest recorded in USA, followed by India and UK [13] (Figure 1.2).
1.3.3 The Driving Forces for Industry 4.0
Industry 4.0 synonymously known as fourth industrial revolution (4 IR) is tagged as the era of digital transformation and digitalization of industrial ecosystems globally. The present industrial revolution is grounded on the concept of automation, cyber physical systems, cloud computing, cognitive computing, and artificial intelligence with different components and systems employed to operate them. These concepts form the source for deploying different technological tools and applications across a wide range of sectors, for example, in manufacturing, production, transportation, healthcare, tourism, hospitality, etc.
Contrary to the concepts that is imminent in the fourth industrial revolution, examining the factors that are influencing the industrial ecosystem to implement industry 4.0 is more important to know the needs of the society and customers rather than the industry [15]. However, from the nature of business operation, there would be specific driving forces that can be cited from every industrial sector contributing to industry 4.0 apart from the general driving factors. The number of sources from literature review have also described the driving factors in specific industrial sectors (includes both large firms and small and medium enterprises SMEs) that are encouraging them toward industry 4.0 concept. Horvath and Szabo 2019 [16] point to several factors from his literature analysis that are promoting the four IR irrespective of the industrial sector. [15] also from his analysis describes five contributing factors that are generally influencing industry 4.0 implementation across different industrial sectors. Garcia (2021) from his review depicted eight factors that are driving the industry 4.0 landscape in organizations. While Camarinha-Matos et al. [17] indicate two major driving forces leading 4 IR. Literature analysis by Ghadge et al. [18] reveals four main driving forces for implementation of industry 4.0. The comparation and coincidence of different driving forces to industry 4.0 described by various researcher from their literature analysis is presented in Table 1.1.
Table 1.1 Contributing factors driving the 4 IR analyzed from literature.
Driving factors
Source
Competitive and business model innovation
Horvath and Szabo 2019; Herceg
et al.
, 2020; Garcia 2021; Ghadge
et al.
, 2020
Cost reduction and performance improvement
Herceg
et al.
, 2020; Garcia 2021; Ghadge
et al.
, 2020
Customer needs
Horvath and Szabo 2019; Herceg
et al.
, 2020
Market changes and demand
Herceg
et al.
, 2020; Matos
et al.
, 2017
Sustainability in energy
Horvath and Szabo 2019; Garcia 2021
Financial factors
Horvath and Szabo 2019; Ghadge
et al.
, 2020
Support to management functions
Horvath and Szabo 2019; Garcia 2021
Increased innovation capacity and productivity
Horvath and Szabo 2019; Garcia 2021
Supply chain
Garcia 2021
Efficiency, quality and flexibility
Garcia 2021; Ghadge
et al.
, 2020
Government policies
Garcia 2021
Optimized production practices and better working conditions
Garcia 2021
New technological possibilities
Matos
et al.
, 2017
1.4 Artificial Intelligence—Manufacturing Sector
Manufacturing and service sectors are known as the largest sectors globally and by far also the leading sectors in terms of diversity of operations, workforce employed and output to the economy. Manufacturing and service sectors takes a critical position among other sectors in all types of economies and gains more prominence to the developing countries. Currently, in industry 4.0, the manufacturing sector takes a significant spot contributing 16% of global GDP and 14% toward employment, with share of 30% to 55% in service jobs [19]. Moreover, the prospects of manufacturing sector in the next few decades would continue to raise with the increase in global consumption and usage, probably leading to shift in demand on certain products and goods. At the same time, these sectors must move with the changing pace of technology and developments along with the industrial revolutions. Besides, the core area of industry 4.0 is to bring a shift toward digital transformation in the manufacturing, process and information sharing, value creation, the different technological tools and applications under each concept are best compatible to bring tremendous improvement and momentum to today’s manufacturing sector.
Artificial intelligence is being the most advanced technological application in the 4 IR is claimed as the powerhouses influencing the manufacturing industry currently and extensively being used to deliver quality, efficiency, and consistent management in supply chain. Likewise global studies reveal that there are as many as 25 different applications that AI can render to manufacturing through operations and other services. Subsequent to the positive impact of AI had on manufacturing sector, market research reports claim that the global AI in manufacturing market was $1.82 billion in 2019, increased to $2.1 billion in 2020 and is expected to reach $11.5 billion by 2027 with huge investments planned on AI in Asia pacific region economies (Market research report 2020). Analyzing the application areas of AI in manufacturing sector globally, the market share contribution is highest in production planning followed by predictive maintenance and machinery inspection, logistics and inventory management and process control.
1.4.1 AI Diversity—Applications to Manufacturing Sector
Artificial intelligence is gaining extensive application in manufacturing sector right through the onset of four IR from the diversity, scope of application and technologies available that can be employed in different phases of production process. Machine learning (ML), a branch of AI, is the mostly wide used application in the manufacturing sector, enabling companies to modulate the production process and enhance quality. Research studies by Capgemini shows that there is increasing trend of AI uses in manufacturing sector globally, nearly 29% of use cases are observed in maintenance and 27% in quality [20].
Overall, global surveys indicate that 60% of manufacturing companies have embarked on using AI into their businesses to enhance product quality, 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 shift toward AI-enabled operations across many companies. Fundamentally, AI technology, in the manufacturing sector, contributes to the growth of companies through predicting the quality and output, predicting maintenance requirements and schedules, human robot interface, generating custom made designs, market adaptation strategies and value addition in supply chain, all that can be achieved through applying various technologies driving the 4 IR (Table 1.2).
Table 1.2 Companies showing areas AI uses for enhancement in their manufacturing process.
Company
Area for enhancement
Sector
BMW
Product quality
Automotive
Ford
Predictive quality & maintenance
Autonomous vehicles
GM
Intelligent maintenance
Generative design
Nissan
Product validation
Carlberg
Product enhancement
Consumer products
Kellogg’s
New product development
Canon
Product quality inspection
Industrial manufacturing
Thales SA
Intelligent maintenance
Aerospace
Boeing
Product quality inspection
Nokia
Realtime optimization of process parameters
Industrial manufacturing
Bombardier
Verizon
Autonomous vehicles
Telecommunications
GE
Generative design
Industrial manufacturing
AI applications to the manufacturing sectors attract some strategic benefits as well, which are instrumental in reducing human errors, helping in taking faster decisions, facilitating workforce in repetitive jobs, quick assistance in trouble shooting problems, round-the-clock access, scope for new inventions and entries, ability to execute tasks in remote working conditions, digital assistance, and in certain fields, improving security. AI-enabled technologies use cases related to manufacturing sector include both direct and indirect applications covering upstream and downstream operations. Some of these applications of AI finds best fit are in 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, procurement of raw materials (Table 1.3).
Apart from multiple use cases that AI can extend to different type of manufacturing industries, it also has the agility in performing quality checks, forecasting product demands based on the customer demands, product inventory through some of the systems like digital twins, virtual agents, biometrics, process automation, image recognition, machine learning etc. in the manufacturing companies. The success of AI tools in today’s industrial sector is mainly from its ability to penetrate significantly in the manufacturing sector because of the compatibility to merge easily with some of its subset technologies, such as machine learning, deep learning, artificial neural networks, computer vision etc. enabling the technology for wider reach (Figure 1.3).
Lastly, while AI technology-specific applications in 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 4IR. In the end, with the diversity of applications and variability AI technology is going to provide to manufacturing sector, AI-enabled operations would take a significant position in future industrial revolutions.
Table 1.3 Information technology companies using AI-enabled technologies employed in product enhancement.
Company
Application
Apple
Optimal character recognition
Speech recognition
IBM
Cognitive service technology
Speech recognition
Microsoft
Deep learning
Machine learning
Deep learning
Optimized hardware
AT & T
Image recognition
Dell
Generative design
Predictive maintenance
Intel
Generative design
Product quality and yield
Nvidia
Optimized hardware
Deep learning
1.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0
The future of artificial intelligence holds very bright for the industrial sectors notably with the introduction of automation and digital transformation to the business operations. Projections indicate by 2035, AI application technologies may accelerate production by 40% globally that is bound to bring more wider utilization in manufacturing more than any other sector. In this changeover, most large firms are going to gain significantly than smaller firms in terms of use cases and adoption rate, regardless of investment and funding on AI-enabled projects in future. With the progressing industry 4.0 era, AI will be able to explore its applicability to all nature of industries, including service and hospitality fields to substantial extent, which can bring new benefits and opportunities in employment generation in some fields. In the manufacturing sector, AI would take inroads into automotive industry in design and innovation toward autonomous vehicles, autopilot, smart cars in public and private transportation. AI technology among nonindustrial sectors, AI-enabled applications will gain a tremendous breakthrough into agriculture, better surveillance in natural calamities, in disaster management and mitigation, education, banking and finance operations, etc. From a futuristic standpoint, leading global economies should shed their focus on the adoption rates of AI technology applications by industries subject to the measures taken to overcome the different challenges industries face at organizational and individual level.
Figure 1.3 Classification of AI technology and its allied functions showing applications in manufacturing sector.
1.5 Conclusion
The fourth industrial revolution that has currently started and taken effect globally is a tremendous boost to industrial sector to modernize and improvise their production and operations to meet the global standards. Consequently, there is also substantial improvement in the global economy due to the various technological applications that has brought enhancement in the industrial sector. From the industrial perspective, many factors had led to the implementation of industry 4.0 in organizations, mainly due to the competitiveness in business operation, increasing efficiency in production and quality, flexibility in using new technologies and meeting customer demands, etc. In this framework, artificial intelligence technology and its subdisciplines have a major role to play in the industry 4.0 era and is a significant driving force to manufacturing sector due to the wide applications and diversity in utilization.
Artificial intelligence technology provides opportunity for automation, increase productivity with efficiency and reduced errors and operational expenditure in addition to high returns to companies from investment in the technology. However, the degree and extent of adopting AI-enabled technologies especially in SMEs, low investment companies are not very progressive, due to the lack of technical knowledge and skills, low capital investment, inadequate support from government, opposition from organizations, inadequate trained workforce are some of the barriers that are limiting the employability of AI in the industry 4.0 concept.
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Note
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Corresponding author
:
2Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview
Sachi Pandey1, Vijay Laxmi2* and Rajendra Prasad Mahapatra1
1SRM Institute of Science and Technology, Delhi – NCR Campus, Modinagar, India
2Information Technology, Boston, MA, United States
Abstract
This chapter starts with an overview of the evolution of Industry 4.0 that enabled digital transformation in manufacturing and other related industries. Provides concepts, definitions, and real-life examples in the intelligent manufacturing industry. Next, describes how the ecosystem of intelligent sensors, devices, and applications increase productivity and streamline business operations. Highlights the integration of manufacturing operational technology with information technology using data science, machine learning, and artificial intelligence. Next, the chapter dwells on how cloud computing can support IoT at a large scale and economically. Finally, it finishes with an overview of security controls and best practices in realizing smart manufacturing.
Keywords: Smart manufacturing, Internet of Things, connected world, cloud computing, data science, machine learning, artificial intelligence, predictive analytics
2.1 Introduction
Digitization, or digital transformation, has become necessary for any firm, industry, or country seeking relevance in the new digital economy [1]. Driven by changes in the digital economy and information technology, the industrial transformation is replacing the traditional nonflexible value chain with flexible, dynamic, and connected value networks [2]. Industrial IoT (IIoT), the industrial transformation, brings devices, cloud computing, data analysis, and human intelligence together to improve the performance and productivity of industrial operations [3]. This chapter summarizes all these concepts and terminologies in industrial automation.
This chapter has five sections. The first section discusses industrial transformation along with value and supply chains, including suppliers, producers or manufacturers, distributors, resellers, and consumers. The second section defines the Internet of Things and introduces the reference architecture (IoT). The third section defines technical terms, such as Internet of Things (IoT) devices, stream processing, big data, machine learning, and artificial intelligence. The fourth section examines associate with cloud computing. The fifth section discusses common security controls to handle data security in this scenario.
2.2 Industrial Transformation/Value Chain Transformation
The term “Industry 4.0” refers to a significant fourth industrial revolution in which current industrial processes are transformed through the use of digital technologies to boost productivity and efficiency. The first industrial revolution refers to the steam engine that changed the manufacturing process by introducing human-operated machines and transforming agrarian society into an industrial society. The second industrial revolution included mass production, assembly line, and electricity that benefited large-scale manufacturing, mechanical engineering, and automotive industries. The third industrial revolution, use of electronics and information technology to automate the manufacturing process further and led to the “Supply Chain Management” concept. The fourth industrial revolution refers to the end-to-end digital transformation of the industrial process and entire value creation process (Figure 2.1).
Value creation is a process that produces more valuable outputs than its inputs and creates efficiency and productivity [4]. One way to add value to an industry is to minimize waste, improve turnaround time, increase uptime and efficiency, and optimize productivity and production capacity in order to reduce input costs. The second option is to reinvent industrial business models by shifting away from product-based to outcome-based (usage-based) [4].
Figure 2.1 Industrial revolution adapted from the study of Horvath [5].
2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT
Let us use the Food and Logistics industries as an example to show how the Industrial Internet of Things contributes to waste reduction and productivity growth. This value chain process in the Food and Logistics Industry is an example of “Vertical IIoT Integration,” in which each layer adds value to the preceding layer. The value chain begins with farmers and ends with distributors, resellers, and customers. To increase crop yields and reduce waste, producers use embedded software and IoT sensors to gather data about soil variability and adjust the fertilizer mix to produce better food. With mounted cameras, drones can help detect anomalies in fields, such as variations in moisture or color [6].
Once the food reaches the distribution stations, IoT sensors and data analytics help follow the journey of items to market and help in identifying the safest and quickest route to market. Consumers are interested in learning about the origins of food and its ingredients. They get this information through the use of food tags, IoT readers, and sensors in smart refrigerators.
A combination of IoT sensors in the manufacturing and transportation processes, barcode and smart label technologies for consumers, and cloud-based technology to store and process the data can help decrease food waste, improve consumer health, and protect firms’ brands and reputations.
2.2.2 Second Scenario: Selling Outcome (User Demand)–Based Services Using IIoT
Customers’ behavior and demand are reshaping resellers and distributors to expand their network. That ultimately forces manufacturers to react more and more rapidly to create consumer and investment goods and reduce the innovation and product cycle. To handle this unexpected temporary rise in demand, businesses may:
Either leverages a flexible network of other manufacturers to help meet that demand surge where devices, systems, and processes are intelligent and can interoperate within the network requiring no additional investment. The flexible network of producers is like “horizontal IIoT Integration among suppliers” and requires three key assumptions–open ecosystem based on standards, interoperability among various systems and decentralization & focus on specialization. For example, with manufacturing covid vaccines, Pfizer and BioNTech collaborated to build a massive number of vaccines leveraging existing infrastructure [
7
].
OR use adaptable manufacturing configuration within a factory to meet the fluctuation in demand. In adaptable manufacturing, the system is modular, and the machine provider focuses on enhancing machines/devices that can integrate and interoperate. Machines use standard internet protocol (TCP IP) to communicate with other machines, devices, processes, and exchange data in a standard way (interoperability).
In summary, the IIoT enables industrial organizations to leverage intelligent devices and connect device data with operational and supply chain data to get actionable insights.
2.3 IIoT Reference Architecture
Industry 4.0, in its simplest form, refers to the intelligent networking of the physical devices, machines, processes, and software systems that are interconnected and exchange data continuously to drive productivity and sustainability throughout the supply chain [8]. Both above-defined scenarios–whether reducing waste to add value or shifting to outcome-based selling—follow common theme across the different components of the IIoT reference architecture.
Open Ecosystem
. The IIoT architecture must accommodate heterogeneity in terms of hardware and software, while processing data patterns, devices, and standards.
Flexible
. The reference architecture must be modular to allow for the coexistence of diverse first-party and third-party technologies.
Scalable
. The reference architecture must support millions of linked devices, allowing initiatives to start small and scale up.
Secure
. To design secure systems, the reference model must consider data security into account across all IIoT components, including device identification, device configuration, data protection—data at rest and data in motion.
Figure 2.2 shows the IIoT reference architecture based on the above reference architecture principles.
The Internet of Things application/framework/architecture comprises the following subcomponents:
Figure 2.2 IIoT reference architecture adapted from Microsoft Azure [9].
Internet of Things (IoT) devices
that communicate with an IoT Hub or Cloud gateway in order to exchange data with the Hub or Cloud.
An IoT Hub or Cloud gateway
accepts data from IoT devices and integrates functions, such as device management, secure networking, and data ingestion.
Stream processors
consume device data from the Hub and apply rules to the data stream to trigger immediate actions and alarms before storing the data.
Data transformation
manipulates the data in the telemetry stream, such as converting from one format to another format or merging data points from many devices.
Storage
stores telemetry data for the long term that is used for reporting and visualization when needed.
The
machine learning (ML)
method analyzes data and correlates it to previous outcomes in order to predict future outcomes.
Business intelligence
makes use of machine learning predictions to guide decision making across the company.
A graphical
user interface
for visualizing device telemetry data and facilitating device maintenance or business intelligence reporting.
2.4 IIoT Technical Concepts
This section discusses the technical aspects of each component in the IIoT reference architecture, beginning with the IoT device and on to data processing, storage, and analytics.
IoT Device and Connectivity: An Internet of Things (IoT) device is a computing device that connects to a network by cable or wireless means and exchanges data over the internet. For instance, a remote camera can detect physical entry or a remote sensor mounted on a food delivery vehicle, as we explained in the food and logistics example. To function, an IoT device must have an inbuilt CPU, network adapter, and firmware, as well as an IP addresses (Internet Protocol). Certain Internet of Things devices communicate and exchange data via the public internet, while others do not. To minimize cyber dangers, most Industrial IoT devices are not accessible via the public internet.
IoT devices communicate with other devices, the IoT Hub, or the Cloud gateway via transport communication protocols. LoRa, NB-IoT, Zigbee, Wi-Fi, Thread, and Bluetooth Low Energy are all common protocols for IoT transport level communication. Depending on the frequency, data rate, bandwidth, power consumption, and cost, one can choose the communication protocol at the transport layer.
Once connected, IoT device is ready to publish or exchange the data, which entails transmitting messages to and from other apps or back-end services with low latency and high throughput. These devices communicate with one another via a variety of data communication protocols, including MQTT, AMQP, HTTP, CoAP, and DDS. Depending on the priority–low latency, message type support, lightweight, built-in security, and encryption–one can use the right data protocol for data transfer.
The entire IoT stack collapses if the communication or data protocol is incorrect. As a result, selection of the IoT transport and data communication protocols for various IoT scenarios is essential.
Data Processing, Storage and Analytics: The IoT device transmits data to gateway, which forward it to stream processors for additional processing. Three actions are available to the data stream processor:
Either stream processor can send telemetry data for visualization and reporting component.
And/or stream processor analyzes data, identifies anomalies, and triggers actions or alerts in response. A stream processor is a fast process that displays and analyzes incoming messages in order to generate essential information and actions, such as alarms.
Or, in a more complex circumstance, a stream processor discovers patterns between various devices and processes. In this situation, stream processor is a slow process conducting complex analysis, such as aggregating various data points and over a longer period (e.g., hours or days), and generating new patterns and business insights [
9
].
Big Data: IoT systems can create large amounts of data (time series), depending on the number of devices in the solution, the frequency with which they communicate data, and the amount of the data records sent by devices. Big Data is a process of collecting, processing, and analyzing enormous amounts of structured (such as telemetry data) and unstructured data (such as IoT device metadata) in order to get insight into varying business patterns. Stream processing works on a continuous stream of data, whereas Big Data works on both continuous and stored data. The following is the sequence for processing large amounts of data (Figure 2.3):
Figure 2.3 IoT data analytics. Adapted from AWS IoT [3].
Collect data
