Logistics for Engineers

In today's rapidly evolving technological landscape, the field of logistics has undergone a significant transformation from a conventional supply chain operation to a highly intricate, data-driven network that is crucial for the efficient functioning of contemporary society. This new textbook seeks to facilitate this transition by providing a framework rooted in engineering fundamentals, empowering students to navigate the complexities of the modern logistics industry.Logistics for Engineers: Automatic Control, Network Science, and Industry 4.0 provides engineering students with a comprehensive and practical approach to mastering logistics by incorporating principles from Automatic Control, Network Science, and Industry 4.0. This new textbook discusses the intricate world of logistics as a complex interconnected system, where inventory levels, supply chains, and production processes act as signals within a control system. By integrating fundamental mathematical models such as differential equations and Laplace transforms, along with industry-specific applications, this textbook empowers future engineers with the necessary tools to optimize efficiency, reliability, and responsiveness within modern, data-driven logistics networks. Through a combination of real-world examples, challenging problem-solving exercises, and insights into cutting-edge technologies, this textbook prepares readers to excel in today's fast-paced industrial environment.Designed specifically for undergraduate engineering students, this textbook is tailored to those studying Industrial Engineering, Mechanical Engineering, Systems Engineering, and Operations Engineering. It serves as a foundational resource for courses in logistics, supply chain management, and operations engineering. The content is particularly beneficial for graduate students focusing on logistics, supply chain analytics, and smart manufacturing, as it offers a thorough exploration of logistics processes using a mathematical and systems-based methodology.The textbook provides a variety of resources to enhance learning, including end-of-chapter assessments, exercises, case studies and figure slides.

Javier Villalba-Diez is a Mechanical Engineer from Technische Universität München, Germany, and an Industrial Engineer from Universidad Politécnica de Madrid (UPM), Spain (2003). Dr. Villalba-Diez is a distinguished expert and holds two PhDs from UPM—both awarded for excellence as the best doctoral theses in their fields. His first Ph.D. in Engineering, Economics, and Organizational Innovation (2016) focuses on Strategic Organizational Design, while his second PhD in Quantum Computational Applications for Industry 4.0 (2022) underscores his cutting-edge expertise in future-ready technologies. Over the past 20 years, he has served as a professor, consultant, and production manager in German, American, and Japanese manufacturing environments, applying his deep technical and strategic knowledge to real-world challenges. His research interests span transformative areas such as Quantum Computing, Deep Learning, Hoshin Kanri, Strategic Organizational Design, and Business AI.Joaquín Ordieres-Meré is a Full Professor at the Universidad Politécnica de Madrid, Spain and a former Professor in industrial management with the Universidad de la Rioja, Spain, in 1997. He received a Ph.D. degree in industrial engineering from the Universidad Nacional de Educación a Distancia (UNED), in 1987. His research interests are connected to managerial dimensions of industry, including project management and business analytics. Dr. Ordieres-Meré’s focus is on process data modelling to improve the knowledge and optimize those processes. He was involved in over 50 research projects, most of them international and competitive. He has published over 100 research papers, participates in ISO TC groups, and serves regularly as a Reviewer for different journals, including the Editor Member Board in some journals. He also represents his country as a member of European Union-established committees such as RFCS TSG8.