An essential guide to creating a successful, fully integrated digital supply network Digital transformation has changed the way we live and work. The rapid evolution of digital technologies is fundamentally transforming supply chain management processes. Competitive success now depends on the strategic adoption of new technologies and the digitalization of demand-supply systems, more collaborative and connected processes, and smarter, more dynamic data-driven decision making-a fully integrated "Digital Supply Network." Digital Supply Network examines the impact new technology has had on supply chain management processes, sub-processes, technologies, and best practices. Drawn from real world-experience and stellar academic research, the book provides an in-depth account of the move to digitally connected supply chain management networks. Filled with expert insights and real-life case studies, this is an essential guide to fully integrating digital supply networks for maximum competitive advantage. You'll learn everything you need to know about: *How DSN redefines the principles of SCM for the digital era*Phases, roles, technology, and the benefits of DSN*Data quality, lifecycle, security, authority, and analytics *Practical application of technologies like Machine Learning, Artificial Intelligence, Blockchain, Robotics and Additive Manufacturing, and the Internet of Things (IoT)*Synchronized planning, demand forecasting, and responsive supply planning*Managerial aspects of digital technologies, and approaches for evaluation and use*Harnessing demand-supply-logistics technology for greater organizational success*Digital product development, and more

The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation, chemical and semiconductor). The results, which are reported in detail in this book, confirmed that it is possible to incorporate implicit process intra- and interrelations on both a process and programme level by applying SVM-based feature ranking. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control. Importantly, the method is neither limited to specific products, manufacturing processes or systems, nor by specific quality concepts.

The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation, chemical and semiconductor). The results, which are reported in detail in this book, confirmed that it is possible to incorporate implicit process intra- and interrelations on both a process and programme level by applying SVM-based feature ranking. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control. Importantly, the method is neither limited to specific products, manufacturing processes or systems, nor by specific quality concepts.