Utilization Of Ai Technology In Supply Chain Management

The surge in digital transformation and the integration of innovative technologies into manufacturing processes have given rise to a pressing issue in supply chain management. Businesses are in dire need of solutions to navigate this complexity and harness the true potential of intelligent supply chains. Utilization of AI Technology in Supply Chain Management is a comprehensive guide tailored for academic scholars seeking to unravel the mysteries of artificial intelligence (AI) and machine learning (ML) in the context of supply chain management. Amid the hype surrounding AI and ML, there exists a critical need to bridge the gap between human expertise and technological advancements. Utilization of AI Technology in Supply Chain Management addresses this necessity by delving into real-world instances where teams have successfully employed these innovative technologies to enhance supply chain performance, reduce inventory, and optimize routes. The adoption of AI and ML is not just a trend; it is the cornerstone of digital acceleration initiatives, making it imperative for scholars to understand and leverage these technologies effectively.

Supply chain management, often known as SCM, refers to the extensive variety of operations that are required to plan, monitor, and coordinate the movement of a product from its raw materials to its finished state in the most time- and money-efficient manner possible. How the supply chain is managed has an impact not only on the quality of the product and the service but also on the distribution, costs, and overall customer experience. Supply chain management is a massive undertaking that needs firms to reevaluate the method in which they operate their supply chains. Blockchain, IoT, and AI Technologies for Supply Chain Management discusses the problems and difficulties that the facilitators of the supply chain confront, in addition to the possible solutions to such problems and difficulties. This book will be the only one of its kind to address the impact of COVID-19 on supply chain systems involving different stakeholders such as producers, dealers, and manufacturers and will provide a foundation for future research opportunities that will allow for the unrestricted expansion and prosperity of business. It will serve as a foundation for academics, scientists, and educationists interested in the use of modern technologies in the field of supply chain management, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain. In addition to those engaged in research, undergraduate and postgraduate students in higher education can also use this publication as a reference book. This book also presents a multifaceted perspective for the general public, including topics such as computer science, the food business, hotel management, fashion, medical, inventory management, and agricultural spheres.

Master's Thesis from the year 2020 in the subject Business economics - Supply, Production, Logistics, , language: English, abstract: Industrialisation has greatly changed people's lives since the eighteenth century. At that time, the focus of activities shifted from the physical to the cognitive. We are now in the fourth industrial revolution. Through the development of ever better artificial systems, more and more cognitive activities are being carried out by machines. But what exactly does artificial intelligence mean? What changes has it already created in supply chain management and what is still possible in the future? What are the risks associated with advancing digitalisation? In his publication, Johannes Hangl creates a comprehensive picture of the status of current and future developments as well as trends in the use of artificial intelligence. He shows what possibilities, effects, opportunities and risks it has for supply chain management. From the contents: - AI; - Internet of Things; - Logistics; - Cyber-Physical Systems

The surge in digital transformation and the integration of innovative technologies into manufacturing processes have given rise to a pressing issue in supply chain management. Businesses are in dire need of solutions to navigate this complexity and harness the true potential of intelligent supply chains. Utilization of AI Technology in Supply Chain Management is a comprehensive guide tailored for academic scholars seeking to unravel the mysteries of artificial intelligence (AI) and machine learning (ML) in the context of supply chain management. Amid the hype surrounding AI and ML, there exists a critical need to bridge the gap between human expertise and technological advancements. Utilization of AI Technology in Supply Chain Management addresses this necessity by delving into real-world instances where teams have successfully employed these innovative technologies to enhance supply chain performance, reduce inventory, and optimize routes. The adoption of AI and ML is not just a trend; it is the cornerstone of digital acceleration initiatives, making it imperative for scholars to understand and leverage these technologies effectively.

This book considers new analytics and AI approaches in the areas of inventory control, logistics, and supply chain management. It provides valuable insights for the retailers and managers to improve business operations and make more realistic and better decisions. It also offers a number of smartly designed strategies related to inventory control and supply chain management for the optimal ordering and delivery policies. The book further uses detailed models and AI computing approaches for demand forecasting to planning optimization and digital execution tracking. One of its key features is use of real-life examples, case studies, practical models to ensure adoption of new solutions, data analytics, and AI-lead automation methodologies are included.The book can be utilized by retailers and managers to improve business operations and make more accurate and realistic decisions. The AI-based solution, agnostic assessment, and strategy will support the companies for better alignment and inventory control and capabilities to create a strategic road map for supply chain and logistics. The book is also useful for postgraduate students, researchers, and corporate executives. It addresses novel solutions for inventory to real-world supply chain and logistics that retailers, practitioners, educators, and scholars will find useful. It provides the theoretical and applicable subject matters for the senior undergraduate and graduate students, researchers, practitioners, and professionals in the area of artificial intelligent computing and its applications in inventory and supply chain management, inventory control, and logistics.

Structuring, or, as it is referred to in the title of this book, the art of structuring, is one of the core elements in the discipline of Information Systems. While the world is becoming increasingly complex, and a growing number of disciplines are evolving to help make it a better place, structure is what is needed in order to understand and combine the various perspectives and approaches involved. Structure is the essential component that allows us to bridge the gaps between these different worlds, and offers a medium for communication and exchange. The contributions in this book build these bridges, which are vital in order to communicate between different worlds of thought and methodology – be it between Information Systems (IS) research and practice, or between IS research and other research disciplines. They describe how structuring can be and should be done so as to foster communication and collaboration. The topics covered reflect various layers of structure that can serve as bridges: models, processes, data, organizations, and technologies. In turn, these aspects are complemented by visionary outlooks on how structure influences the field.

This book highlights the importance of data-driven technologies and artificial intelligence in supply chain management. It covers important concepts such as enabling technologies in Industry 4.0, the impact of artificial intelligence, and data-driven technologies in lean manufacturing. "Provides solutions to solve complex supply chain management issues using artificial intelligence and data-driven technologies" Emphasizes the impact of a data-driven supply chain on quality management "Discusses applications of artificial intelligence, and data-driven technologies in the service industry, and lean manufacturing" Highlights the barriers to implementing artificial intelligence in small and medium enterprises Presents a better understanding of different risks such as procurement risks, process risks, demand risks, transportation risks, and operational risks The book comprehensively discusses the applications of artificial intelligence and data-driven technologies in supply chain management for diverse fields such as service industries, manufacturing industries, and healthcare. It further covers the impact of artificial intelligence and data-driven technologies in managing the FMGC supply chain. It will be a valuable resource for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, industrial engineering, manufacturing engineering, production engineering, and computer engineering.

Part of our new "Quick Notes" series - this report answers your most pertinent questions of the topic. Do not be deceived by their short nature - these notes are only 22 pages or so. But these are 22 pages of potent dynamite that will supercharge your thinking in the right direction. Included are quick notes and some of the frequently asked questions (FAQs) on supply chain finance that we have encountered in our workshops, seminars, and other forums. Here are some of the topics and questions covered in these quick notes: What is Supply chain AI and why everyone is moving towards it? What is the advantage of integrating AI in a supply chain environment? What are the current applications of AI in SCM? What are the current trends in the field of AI which aid improvements in SCM? Which are the experimental models in AI meant for supply chain management? What is Supply chain AI and why everyone is moving towards it? What is the advantage of integrating AI in a supply chain environment? What are the current applications of AI in SCM? What are the current trends in the field of AI which aid improvements in SCM? Which are the experimental models in AI meant for supply chain management? What are the fully developed AI models that are currently used in the business environment? Out of the four stages of supply chain analytics - descriptive, diagnostic, predictive and prescriptive - which one has the most potential application of supply chain AI? How does the predictive nature of AI algorithms help in forecasting? What is the current cost reduction that AI brings into SCM? In this current ongoing COVID -19 crisis can we use AI to reduce the decision making time and to process huge amount of data. If yes how? What are the requirements for a company to incorporate the use of AI in its supply chain? What are the future trends that we need to look in AI? Which are the industries that can gain more from an AI-powered SCM? What is the role of the current ERP and DSS tools when deploying AI?