In today's fast-paced world, businesses must keep up with ever-changing trends and consumer expectations to stay relevant. The solution lies in Supply Chain 5.0 — a concept that prioritizes customer-centricity, sustainability, and digitization.In this groundbreaking book, the authors have evolved the essential ingredients of Supply Chain 5.0, defining the next generation of business performance. With a focus on digital technology, this guide explores the use of quantum computing and robotics, AI, Blockchain, and digitization of procurement to optimize operations and achieve business objectives.But that's not all. The authors also delve into the critical importance of sustainability and human rights in today's supply chain management. Discover how to create a socially responsible and sustainable supply chain while providing exceptional customer service.From the impact of robotics to the future of healthcare supply chains, this comprehensive guide covers a wide range of topics. It provides actionable insights and strategies that businesses can use to improve supply chain efficiency, sustainability, and resilience.Whether you're a supply chain professional, a business owner, or simply interested in the future of global commerce, this book is a must-read. Get ready to stay ahead of the curve and transform your supply chain management in the face of an ever-changing landscape.

Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems. Non-convex Optimization for Machine Learning is as self-contained as possible while not losing focus of the main topic of non-convex optimization techniques. The monograph initiates the discussion with entire chapters devoted to presenting a tutorial-like treatment of basic concepts in convex analysis and optimization, as well as their non-convex counterparts. The monograph concludes with a look at four interesting applications in the areas of machine learning and signal processing, and exploring how the non-convex optimization techniques introduced earlier can be used to solve these problems. The monograph also contains, for each of the topics discussed, exercises and figures designed to engage the reader, as well as extensive bibliographic notes pointing towards classical works and recent advances. Non-convex Optimization for Machine Learning can be used for a semester-length course on the basics of non-convex optimization with applications to machine learning. On the other hand, it is also possible to cherry pick individual portions, such the chapter on sparse recovery, or the EM algorithm, for inclusion in a broader course. Several courses such as those in machine learning, optimization, and signal processing may benefit from the inclusion of such topics.