Cantitate/Preț
Produs
Total produse
Vezi coșul
Supply Chain Management in Manufacturing and Service Systems: Advanced Analytics for Smarter Decisions de Sharan Srinivas

Supply Chain Management in Manufacturing and Service Systems: Advanced Analytics for Smarter Decisions: International Series in Operations Research & Management Science, cartea 304

Editat de Sharan Srinivas, Suchithra Rajendran, Hans Ziegler
en Limba Engleză Paperback – 26 iun 2022
Management of supply chains has been evolving rapidly over the last few years due to the inception of Industry 4.0, where businesses adopt automation technologies and data exchanges leading to dynamic and interconnected supply chain systems. Emphasizing on analytical approaches such as predictive and prescriptive modeling, this book presents state-of-the-art original research work dealing with advanced analytical models for the design, planning, and operation of the supply chain to provide faster and smarter decisions in the era of digitization.
In particular, the book integrates machine learning and operations research models for faster and smarter decisions, presents prescriptive analytics models for strategic, tactical, and operational decision making in the supply chain, and addresses recent challenges such as sustainability in the supply chain, supply chain visibility, and supply chain digitalization. Key concepts are illustrated using real-life case studies, making thebook a valuable reference for researchers, technical professionals, and students.

Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 108558 lei  6-8 săpt.
  Springer International Publishing – 26 iun 2022 108558 lei  6-8 săpt.
Hardback (1) 109144 lei  6-8 săpt.
  Springer International Publishing – 25 iun 2021 109144 lei  6-8 săpt.

Din seria International Series in Operations Research & Management Science

Preț: 108558 lei

Preț vechi: 132388 lei
-18% Nou

Puncte Express: 1628
Preț estimativ în valută:
20782€ • 21634$ • 17109£

Carte tipărită la comandă

Livrare economică 31 ianuarie-14 februarie 25

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783030692674
ISBN-10: 3030692671
Pagini: 278
Ilustrații: XVIII, 278 p. 99 illus., 79 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.42 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria International Series in Operations Research & Management Science

Locul publicării:Cham, Switzerland

Cuprins

An Overview of Decisions, Performance and Analytics in Supply Chain Management.- Intelligent Digital Supply Chains.- Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design.- Supply Chain Risk Management in Indian Manufacturing Industries: An Empirical Study and a Fuzzy Approach.- Improving Service Supply Chain of Internet Services by Analyzing Online Customer Reviews.- An Integrated Problem of Production Scheduling and Transportation in a Two-Stage Supply Chain with Carbon Emission Consideration.- A Simulation-Based Evaluation of Drone Integrated Delivery Strategies for Improving Pharmaceutical Service.- Pro-Active Strategies in Online Routing.- Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces.

Notă biografică

Dr. Sharan Srinivas is an Assistant Professor with a joint appointment in the Department of Industrial & Manufacturing Systems Engineering and the Department of Marketing at the University of Missouri. Dr. Srinivas received his Ph.D. in industrial engineering and operations research from Pennsylvania State University. He holds a Bachelor’s degree in industrial engineering from College of Engineering, Guindy, Anna University, India, a MS in industrial and systems engineering from Binghamton University, State University of New York (SUNY), and a MEng. in industrial engineering and operations research from the Pennsylvania State University.
Dr. Srinivas' area of specialization is data analytics and operations research with research interests in healthcare operations management, logistics, smart service systems, and supply chain. He has been an investigator on industry-based research projects. He has published over 20 scholarly articles in journals and his research work hasappeared in leading journals such as Computers and Industrial Engineering, Expert Systems with Applications, Transportation Research Part C: Emerging Technologies, Transportation Research Part E: Logistics and Transportation Review, International Journal of Medical Informatics. Dr. Srinivas has taught undergraduate, graduate, and MBA level courses that include topics pertaining to data analytics, machine learning, simulation, service systems, and supply chain optimization. He is also an active member of INFORMS and IISE professional societies, and has served numerous times as a session chair in their annual conferences. Dr. Srinivas is a certified six sigma black belt and recipient of multiple awards (INFORMS Koopman prize, Winemiller Excellence Award, Richard Wallace Faculty Grant, Penn State Doctoral Fellowship, Service Enterprise Engineering Fellowship). 

Dr. Suchithra Rajendran is an Assistant Professor with a joint appointment in the Departmentof Industrial and Manufacturing Systems Engineering and the Department of Marketing at the University of Missouri, Columbia, USA. Prior to that, she served as a consultant for many private and public organizations on various collaborative projects. She holds a Bachelor's degree in industrial engineering from Anna University in India. Her graduate degrees are from the Pennsylvania State University, where she received a M.S. and a Ph.D. in industrial engineering and operations research.
Dr. Rajendran's research interests include healthcare systems engineering, big data analytics, multiple criteria decision-making, and quality assurance. She is a Penn State National Science Foundation Center for Health Organization Transformation (NSF-CHOT) scholar, Service Enterprise Engineering fellow and also a recipient of the Richard Wallace Faculty Incentive Grant and DAAD-WISE Fellowship.

Prof. Dr. Hans Ziegler held the Chair for Production, Operations and Logistics Management in the School of Business, Economics and Information Systems at the University of Passau, Germany. He received a diploma in industrial engineering from the University of Karlsruhe (TH), Germany (now called Karlsruhe Institute of Technology), a doctoral degree in business and economics and a post-doctoral habilitation in business, both from the University of Paderborn, Germany. He had been on the faculty of the University of Paderborn and the Technical University of Darmstadt, Germany, before moving to the University of Passau. He has research interests in production, operations and logistics management. Professor Ziegler has published over 60 articles in peer-reviewed journals, conference proceedings and books.

Textul de pe ultima copertă

Management of supply chains has been evolving rapidly over the last few years due to the inception of Industry 4.0, where businesses adopt automation technologies and data exchanges leading to dynamic and interconnected supply chain systems. Emphasizing on analytical approaches such as predictive and prescriptive modeling, this book presents state-of-the-art original research work dealing with advanced analytical models for the design, planning, and operation of the supply chain to provide faster and smarter decisions in the era of digitization. In particular, the book integrates machine learning and operations research models for faster and smarter decisions, presents prescriptive analytics models for strategic, tactical, and operational decision making in the supply chain, and addresses recent challenges such as sustainability in the supply chain, supply chain visibility, and supply chain digitalization. Key concepts are illustrated using real-life case studies, making the book a valuable reference for researchers, technical professionals, and students.

Caracteristici

Provides advanced analytical solutions for managing supply chain in the era of Industry 4.0 Integrates machine learning and operations research models for faster and smarter decisions Illustrates key concepts using real-life case studies