The Fourth Industrial Revolution, also known as Industry 4.0, refers to the industrial paradigm bringing together the digital and physical worlds through the cyber-physical Systems, enhanced by the Internet of Things aimed to increase the effectiveness of human-machine cooperation (HMC). This book deals with issues related to the challenges of Industry 4.0 that are faced by enterprises and universities. Contrary to most publications on the subject, it covers both technological and business aspects of these challenges and shows how strong they are intertwined, bringing new value to readers. The book also presents new findings that will guide enterprises through Industry 4.0. This book offers readers an in-depth discussion of important areas of enterprises' activities in the context of Industry 4.0. The first area concerns human resources management; in particular, what new employee competencies will be needed on the labor market, how to use modern concepts (e.g. design thinking), and how to manage multi-national teams of employees. The second area is related to marketing and covers issues regarding customized products. The third area is devoted to technical aspects such as autonomous vehicles, Internet of Things (IoT), radio-frequency identification (RFID) systems, and Bluetooth Low Energy (BLE) technology. The fourth area concerns IT systems, including systems that support work and business management, strategic information systems, and cyber-physical systems. Aimed at researchers, academics, practitioners, and students, it will be of value to those in the fields of human resource management, marketing, organizational studies, and management of technology and innovation.

This book shows how common operation management methods and algorithms can be extended to deal with vague or imprecise information in decision-making problems. It describes how to combine decision trees, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of imprecise or vague information, and how to visualize such information. Moreover, it discusses a broad spectrum of real-life management problems including forecasting the apparent consumption of steel products, planning and scheduling of production processes, project portfolio selection and economic-risk estimation. It is a concise, yet comprehensive, reference source for researchers in decision-making and decision-makers in business organizations alike.

This book shows how common operation management methods and algorithms can be extended to deal with vague or imprecise information in decision-making problems. It describes how to combine decision trees, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of imprecise or vague information, and how to visualize such information. Moreover, it discusses a broad spectrum of real-life management problems including forecasting the apparent consumption of steel products, planning and scheduling of production processes, project portfolio selection and economic-risk estimation. It is a concise, yet comprehensive, reference source for researchers in decision-making and decision-makers in business organizations alike.