This book is a collection of selected papers presented at the First International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC), held as an online conference due to COVID-19 (initially to be held in Macao, Special Administration Region (SAR) of China), during September 15–17, 2020. It includes novel and innovative work from experts, practitioners, scientists and decision-makers from academia and industry. It brings multi-disciplines together on IIoT, data science, cloud computing, software engineering approaches to design, development, testing and quality of products and services.
This book is a collection of selected papers presented at the First International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC), held as an online conference due to COVID-19 (initially to be held in Macao, Special Administration Region (SAR) of China), during September 15-17, 2020. It includes novel and innovative work from experts, practitioners, scientists and decision-makers from academia and industry. It brings multi-disciplines together on IIoT, data science, cloud computing, software engineering approaches to design, development, testing and quality of products and services.
By enabling the conversion of traditional manufacturing systems into contemporary digitalized ones, Internet of Things (IoT) adoption in manufacturing creates huge economic prospects through reshaping industries. Modern businesses can more readily implement new data-driven strategies and deal with the pressure of international competition thanks to Industrial IoT. But as the use of IoT grows, the amount of created data rises, turning industrial data into Industrial Big Data. Internet of Things and Big Data Analytics-Based Manufacturing shows how Industrial Big Data can be produced as a result of IoT usage in manufacturing, considering sensing systems and mobile devices. Different IoT applications that have been developed are demonstrated and it is shown how genuine industrial data can be produced, leading to Industrial Big Data. This book is organized into four sections discussing IoT and technology, the future of Big Data, algorithms, and case studies demonstrating the use of IoT and Big Data in a variety of industries, including automation, industrial manufacturing, and healthcare. This reference title brings all related technologies into a single source so that researchers, undergraduate and postgraduate students, academicians, and those in the industry can easily understand the topic and further their knowledge.
The Industrial Internet of Things (IIoT) is a platform that allows a network of devices (sensors, smart meters, etc ) to communicate, analyze data and process information collaboratively in the service of individuals or organizations Big Data (BD) has core values of volume, velocity, variety and veracity Supply Chain requires the modern advancement and support from IIoT and Big Data, to make all services efficient, fast, accurate and reliable These three can combine and work together to produce greater impacts and contributions as follows First, the issues surrounding IIoT devices, their interconnectedness and services they may offer, including efficient, effective and secure analysis of the data IIoT produces Often machine learning and other advanced techniques, models and tools, and issues of security and trust are related The maturity of IoT technologies can grow and become part of our everyday lives Second, BD can be jointly used with machine learning, AI, statistical and ot
The concepts for Industry 4.0 and the Industrial Internet of Things (IIoT) will fundamentally change supply chains, production processes and industries. Intelligent technologies such as IoT, edge and cloud computing, big data, artificial intelligence and digital assistance systems are drivers of this change. This book provides a comprehensive overview of IoT use cases with illustrative practical examples of how digitization or innovation projects can be successfully implemented. It takes into consideration that processes are getting more flexible and efficient, and new digital technologies allow seamless, location-independent communication in near real time between things, processes and people through the digitization of physical objects and processes. Considering these changes, the book provides a guideline on how companies should position themselves for the future with industrial IoT in order to still play a decisive role in the industry in a few years' time. The book is aimed at both decision-makers and practitioners who, on the one hand, recognize the opportunities and possibilities for their company and, on the other hand, want to learn how to use the appropriate technologies. With this in mind it will be valuable for entrepreneurs, managers, architects and also developers in the field of Industry 4.0.
The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience. Features The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners
Mastering the art of leveraging IoT for industry transformation KEY FEATURES ● Learn IoT principles, strategies, and tech for advanced manufacturing and supply chain. ● Understand IoT's role in enhancing competitiveness and innovation. ● Gain insights through real-world case studies and practical examples. DESCRIPTION In the world of industrial manufacturing and supply chain, the lack of real-time visibility and insights into processes poses a significant challenge. However, IoT is set to bring about a profound transformation. This technological revolution promises efficiency gains, operational optimization, and unprecedented business insights. Step into the world of Industry 4.0 and 5.0 with IoT and discover how it revolutionizes production and logistics. Learn about real-time monitoring, predictive maintenance, and quality control while ensuring a secure IoT infrastructure. Explore practical examples in manufacturing, including smart factories, personalized transit, and sustainability practices. Use the potential of AI, predictive analytics, and 3D printing to align your IoT strategies with business goals for enhanced performance. Completing this book equips readers to excel in leveraging IoT for industrial manufacturing and supply chain advancements. They will master IoT concepts, optimize processes, and handle integration challenges. With the acquired knowledge, readers can develop strong IoT strategies, assess project outcomes effectively, and introduce significant improvements to their manufacturing and supply chain operations. WHAT YOU WILL LEARN ● Understanding IoT's role in advanced manufacturing and supply chain. ● Applying IoT for real-time monitoring and predictive maintenance. ● Enhancing production efficiency through IoT-driven solutions. ● Leveraging IoT for supply chain optimization and transparency. ● Overcoming IoT implementation challenges and ensuring security. ● Exploring the future possibilities of IoT and AI in manufacturing. WHO THIS BOOK IS FOR This book is intended for manufacturing, supply chain management, and IoT specialists and enthusiasts with intermediate to advanced knowledge of IoT and its industrial applications. TABLE OF CONTENTS 1. IoT Fundamentals, Architecture, and Protocols 2. Embracing IoT in Manufacturing 3. The Power of IoT in Supply Chain 4. IoT: Use Cases in Smart Factories 5. Business Factors and Optimization for IoT Implementation 6. Challenges and Solutions 7. Artificial Intelligence in Manufacturing 8. The Future of IoT 9. Key Takeaways
Technological advancements in recent years have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Supply Chain Management in the Big Data Era is an authoritative reference source for the latest scholarly material on the implementation of big data analytics for improved operations and supply chain processes. Highlighting emerging strategies from different industry perspectives, this book is ideally designed for managers, professionals, practitioners, and students interested in the most recent research on supply chain innovations.
In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.
