The rapid advancement of digital transformation, artificial intelligence (AI), and cyber-physical systems is fundamentally changing the way organizations design, operate, and optimize their processes and assets. Among the most influential technological and managerial concepts driving this transformation is the digital twin, which has evolved from a technical monitoring tool to a strategic enabler of intelligent decision-making, predictive analytics, and adaptive business models. As data availability and computational capabilities continue to grow, digital twins are progressing toward cognitive and intelligent models capable of learning, adapting, and supporting autonomous or semi-autonomous decision-making. The concept of the cognitive enterprise represents the next stage of this evolution. Despite the growing importance of these developments, there remains a need for comprehensive academic and applied research that connects digital twin technologies with enterprise transformation, business intelligence, and more. Transforming Physical Assets to Cognitive Enterprises With Digital Twins brings together diverse perspectives to explore how digital twins can transform physical assets, operational systems, and organizational structures into intelligent, adaptive, and cognitive enterprises. It investigates managerial, organizational, and strategic implications of digital twin adoption, as well as the challenges related to implementation, governance, ethics, trust, and human-AI collaboration. Covering topics such as agentic intelligence, crisis management, and organizational transformation, this book is an excellent resource for researchers, academicians, graduate and doctoral students, industry professionals, consultants, policymakers, engineers, technology specialists, and more.
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Associate Professor Dr. Zornitsa Yordanova is a leading expert in Business Information Systems, Innovation, and Digital Transformation at the University of National and World Economy (Bulgaria). She brings extensive teaching experience from various European universities and has authored over 100 academic publications.
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Hardcover. Zustand: new. Hardcover. The rapid advancement of digital transformation, artificial intelligence (AI), and cyber-physical systems is fundamentally changing the way organizations design, operate, and optimize their processes and assets. Among the most influential technological and managerial concepts driving this transformation is the digital twin, which has evolved from a technical monitoring tool to a strategic enabler of intelligent decision-making, predictive analytics, and adaptive business models. As data availability and computational capabilities continue to grow, digital twins are progressing toward cognitive and intelligent models capable of learning, adapting, and supporting autonomous or semi-autonomous decision-making. The concept of the cognitive enterprise represents the next stage of this evolution. Despite the growing importance of these developments, there remains a need for comprehensive academic and applied research that connects digital twin technologies with enterprise transformation, business intelligence, and more. Transforming Physical Assets to Cognitive Enterprises With Digital Twins brings together diverse perspectives to explore how digital twins can transform physical assets, operational systems, and organizational structures into intelligent, adaptive, and cognitive enterprises. It investigates managerial, organizational, and strategic implications of digital twin adoption, as well as the challenges related to implementation, governance, ethics, trust, and human-AI collaboration. Covering topics such as agentic intelligence, crisis management, and organizational transformation, this book is an excellent resource for researchers, academicians, graduate and doctoral students, industry professionals, consultants, policymakers, engineers, technology specialists, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798337399980
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