The goal of this book is to gain a clear picture of the current status and future challenges with regard to the digitalization of the supply chain – from the perspective of the suppliers, the manufacturers, and the customers. They were the target groups of the book. Digitization has touched upon all aspects of businesses, including supply chains. Technologies such as RFID, GPS, and sensors have enabled organizations to transform their existing hybrid (combination of paper-based and IT-supported processes) supply chain structures into more f lexible, open, agile, and collaborative digital models. Unlike hybrid supply chain models, which have resulted in rigid organizational structures, unobtainable data, and disjointed relationships with partners, digital supply chains enable business process automation, organizational flexibility, and digital management of corporate assets. In order to reap maximum benefits from digital supply chain models, it is important that companies internalize it as an integral part of the overall business model and organizational structure. Localized disconnected projects and silo-based operations pose a serious threat to competitiveness in an increasingly digital world. The technologies discussed in this text – artificial intelligence, 3D printing, Internet of things, etc. – are beginning to come together to help digitize, automate, integrate, and improve the global supply chains. It’s certainly an exciting and challenging time for both new supply chain professionals and long-time supply chain professionals.

Master's Thesis from the year 2021 in the subject Business economics - Supply, Production, Logistics, grade: 2, , course: Supply Chain, language: English, abstract: The Supply chain process is an integrated business process that organizes and manages the flow of goods and services from the manufactures to the end-users. It and streamlines the supply chain activities by establishing linkage between suppliers, dealers, manufacturers, service providers, and end-users. The concept of supply chain management is adopted by different industries such as healthcare, retail, food, manufacturing, construction, and textile to enhance the working process and optimize customer value. The working of the supply chain process could be enhanced by adopting advanced technologies and digitalization in its working. While focusing on digitalization in the supply chain, it includes the introduction of digital technologies such as Big Data, the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, predictive analytics, and blockchain into the supply chain process. The technologies enhance the supply chain management system by coordinating and synchronizing the different supply chain parts such as order, raw materials, production, finished products, warehouse, logistics, packaging, delivery, retailer, and end-user. For instance, the adoption of the blockchain process into the supply chain management process helps in simplifying the complex supply chain work process and transferring paper-intensive work processes into digitalized formats. On the other hand, the introduction of the robotic process helps in digitizing the back-office process by 50% to 80% that helps in faster movement and précised tracking of products. Predictive analytics is based on advanced analytics that facilities the supply chain process by streamlining internal and external demand variables such as social media trends, sensor data, and demand plans. It helps in improving demand prediction accuracy and lowering prediction errors by 30% to 50%. It increases industry and business to adopt digitalization in supply chain process to experience technology maturity and accelerated functionaries. Digital technology such as Big Data is included in the supply chain process that synchronizes the different processes such as promotion, procurement, stockroom, and haulage.

Detailing the diverse aspects of digitalization in supply chain management, Digital Supply Chain Management helps business managers harness the cutting edge, guiding those early in their careers who seek a challenging new path whilst informing top-level managers who have their eye on the future.

The Digital Supply Chain is a thorough investigation of the underpinning technologies, systems, platforms and models that enable the design, management, and control of digitally connected supply chains. The book examines the origin, emergence and building blocks of the Digital Supply Chain, showing how and where the virtual and physical supply chain worlds interact. It reviews the enabling technologies that underpin digitally controlled supply chains and examines how the discipline of supply chain management is affected by enhanced digital connectivity, discussing purchasing and procurement, supply chain traceability, performance management, and supply chain cyber security. The book provides a rich set of cases on current digital practices and challenges across a range of industrial and business sectors including the retail, textiles and clothing, the automotive industry, food, shipping and international logistics, and SMEs. It concludes with research frontiers, discussing network science for supply chain analysis, challenges in Blockchain applications and in digital supply chain surveillance, as well as the need to re-conceptualize supply chain strategies for digitally transformed supply chains.

Information is one of the key enablers of modern business. The ever expanding availability of digital information, however, brings with it the challenge of handling this information appropriately. While related challenges now appear in our daily lives, this is even more the case along supply chains, where a multitude of actors is involved. This doctoral thesis addresses the topic by linking theoretical rigor with practical relevance. By assessing the current state of research in supply chain management represented in literature reviews, a range of under-represented areas of research as well as potential future research directions in the field of supply chain management are identified. Focusing on one selected exemplary under-represented area of research, the thesis takes the digital business transformation perspective, portraying the value and role of digital information in a business function context. As research on the intersection of Big Data Analytics and supply chain management is still scarce, the conceptual work offers first insights into an emerging topic, both on the internal operations level and on the supply chain level. This is beneficial from a scientific as well as a managerial perspective, as a thorough understanding of the constituents of a digital ecosystem is a key ingredient for the competitiveness and overall productivity of the company and ultimately of the supply chain as a whole.

In the rapidly evolving landscape of Industry 4.0, integrating digital technologies into supply chain management (SCM) presents opportunities and challenges. While Industry 4.0 promises increased efficiency, productivity, and competitiveness, its impact on sustainability within SCM remains a pressing concern. Existing literature often needs to look more into the holistic integration of Industry 4.0 technologies with sustainable practices in SCM, leaving a critical gap in understanding and implementation. This gap not only inhibits the realization of sustainable performance but also hinders firms from aligning with global sustainability agendas such as the United Nations Sustainable Development Goals (UNSDG) 2030. Digital Transformation for Improved Industry and Supply Chain Performance offers a comprehensive solution by examining the integration of Industry 4.0 technology and SCM sustainability. It addresses the urgent need for firms to undergo digital transformation to achieve sustainable performance. It provides insights into how Industry 4.0 technologies can be strategically leveraged to promote sustainability in SCM operations. Through in-depth analysis of critical topics such as cybersecurity, resilience, circular economy practices, and ethical considerations, this book equips readers with the knowledge and tools necessary to navigate the complexities of Industry 4.0-enabled SCM sustainability.

An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.