Platform Supply Chains in the AI Era – serie

This open access book turns fragmented insights on platform supply chains into a practical integration framework and guidelines. It shows how to design and govern four interlocking pillars, technology, logistics, suppliers, and customers, in platform supply chains to remove value-creation bottlenecks and deliver measurable performance. Readers get clear design rules, governance options, and metrics they can apply across digital-intelligent, complex and end-to-end platform networks.What’s new is a platform-specific lens on integration. The book frames blockchain as a complex adaptive systems enabler and a governance substitute, explains how 3PL coordination scales into platform logistics orchestration, and unpacks trust–contract interactions under data-rich uncertainty, including the role of data breaches in supplier replacement. It also links cross-functional coordination to customer integration and free-trial strategies, clarifying when network effects amplify or erode value.The scope begins with foundations and the rise of platform supply chains, then develops a four-pillar framework: technology integration (from IT integration to blockchain-enabled coordination and performance effects), logistics integration (relational and economic mechanisms and platform models), supplier integration (balancing trust and contracts under platform-specific uncertainty), and customer integration (systems, processes, teams, and value creation via trials and communities). The intended readership includes advanced students and researchers in operations, supply chain, and information systems, as well as leaders in platform firms, logistics, and procurement. Core benefits are a unifying framework tailored to platforms, evidence-based governance choices across the pillars, and concise checklists and measures to test and refine integration decisions.

This open access book delves into methodologies for addressing prevalent statistical problems in supply chains—including prediction with multi-source data, density response prediction and ranking problems—leveraging model averaging techniques and numerical experiments. Its innovation lies in addressing uncertainty in model design and variable selection by integrating multiple viable candidate models instead of relying on a single one, while enhancing model performance through tailored model-averaging weight criteria.The intended readership of this book includes undergraduate students in universities, graduates, and academic researchers in the field of management science, data science and statistics. This book is suitable for those with a master's degree or above in management science or statistics.