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Simulating business processes for descriptive, predictive, and prescriptive analytics / : библиография

Полный текст
База данных: Каталог библиотеки СФУ (658.472  S61)
Библиографическое описание: Greasley, Andrew. Simulating business processes for descriptive, predictive, and prescriptive analytics / : библиография / Andrew, Greasley. - Электрон. текстовые дан. - [S. l. : s. n.], 2019. - 1 online resource (x, 341 pages) : il. - Загл. с титул. экрана. - Includes bibliographical references (pages 336-337) and index. - ISBN 9781547400713. - ISBN 1547400714. - Текст : электронный. Примечания о происхождении: Коллекция цифровых книг Ebsco ebook (централизованная подписка 2023 г., бессрочный доступ)., НБ СФУ
Аннотация: This book outlines the benefits and limitations of simulation, what is involved in setting up a simulation capability in an organization, the steps involved in developing a simulation model and how to ensure model results are implemented. In addition, detailed example applications are provided to show where the tool is useful and what it can offer the decision maker. --
Год издания: 2019
Авторы: Greasley Andrew
Количество экземпляров:
Издание имеется только в электронном виде

Цифровые и тематические книжные коллекции издания

Содержание

  • Frontmatter
  • Preface
  • Acknowledgments
  • About the author
  • part 1: Understanding simulation and analytics. Analytics and simulation basics
  • Simulation and business processes
  • Build the conceptual model
  • Build the simulation
  • Use simulation for descriptive, predictive and prescriptive analytics
  • part 2: Simulation case studies. Case study: a simulation of a police call center
  • Case study: A simulation of a "Last Mile" logistics system
  • Case Study: A simulation of an enterprise resource planning system
  • Case study: A simulation of a snacks process production system
  • Case study: A simulation of a police arrest process
  • Case study: A simulation of a food retail distribution network
  • Case study: A simulation of a proposed textile plant
  • Case study: A simulation of a road traffic accident process
  • Case study: A simulation of a rail carriage maintenance depot
  • Case study: A simulation of a rail vehicle bogie production facility
  • Case study: A simulation of advanced service provision
  • Case study: Generating simulation analytics with process mining
  • Chapter 18. Case study: Using simulation with data envelopment analysis
  • Case study: Agent-based modeling in discrete-event simulation
  • Appendix A
  • Appendix B
  • Index.