Optimizing Supply Chains Through Digital Twins (eBook, PDF)
128,95 €
inkl. MwSt.
Sofort per Download lieferbar
payback
64 °P sammeln
  • Format: PDF

This book serves as a comprehensive resource, offering valuable insights into the application of Digital Twins in supply chain management. It covers foundational concepts of supply chain optimization and delves into the technological frameworks that enable the integration of Digital Twins. Contributions from leading academics, industry experts, and researchers provide a multi-dimensional perspective on this transformative approach. Real-world case studies are included to showcase the practical benefits and challenges of implementing Digital Twins in various segments of modern supply chains,…mehr

  • Geräte: PC
  • ohne Kopierschutz
  • eBook Hilfe
  • Größe: 12.84MB
Andere Kunden interessierten sich auch für
Produktbeschreibung
This book serves as a comprehensive resource, offering valuable insights into the application of Digital Twins in supply chain management. It covers foundational concepts of supply chain optimization and delves into the technological frameworks that enable the integration of Digital Twins. Contributions from leading academics, industry experts, and researchers provide a multi-dimensional perspective on this transformative approach. Real-world case studies are included to showcase the practical benefits and challenges of implementing Digital Twins in various segments of modern supply chains, from manufacturing and distribution to inventory management. A set of practical guidelines and best practices for implementing Digital Twins in supply chain management round out the content.

The book offers a roadmap for organizations looking to adopt this technology for enhanced operational efficiency. To deepen the reader's understanding, it also features specialized sections on data security and ethical considerations when implementing Digital Twins in supply chains. By covering these aspects, it offers the "go-to" resource for a wide range of professionals including supply chain managers, data scientists, and IT specialists, as well as academics, researchers and students in related fields, who are looking to understand or implement Digital Twins in the context of supply chain optimization.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Autorenporträt
Ghaith Rabadi is the Director and a Professor at the School of Modeling, Simulation, and Training (SMST), University of Central Florida (UCF), USA. His research focuses on modeling complex systems and using optimization, simulation, and AI to find optimal and near-optimal solutions to complex systems including multimodal transportation systems, production, ports, disaster response logistics, and healthcare. He is a recipient of the NASA Faculty Fellowship, NASA Software Invention Award, the NASA Board Action Invention Award, as well as the Fulbright Specialist Program Award and NATO Innovation Challenge. His research has been funded by various agencies including NASA, NATO, Department of Homeland Security, Virginia Port Authority, MITRE Corporation, STIHL, Sentara Hospitals and Qatar Foundation. He is the co-founder and currently the Chief Editor of the International Journal of Planning and Scheduling. Bulent Soykan is a Postdoctoral Scholar at the School of Modeling, Simulation, and Training, University of Central Florida (UCF), USA. His research focuses on large-scale optimization, system simulation and modeling, digital twins, reinforcement learning, supply chains, transportation & logistics. He has a background in operations research and systems analysis, with specific training and expertise in modeling and simulation. He develops comprehensive, usable digital twins to support decision making to increase automation in solving complex real-world problems in supply chains, manufacturing, and various other domains. To accomplish this, he integrates principles and knowledge from the fields of operations research and data science, as well as drawing from various AI fields, such as automated planning, sequential decision making, machine learning, and deep reinforcement learning.