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Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things: Texts in Computer Science

Autor Richard Hill, Stuart Berry
en Limba Engleză Paperback – 29 sep 2022
This textbook describes the hands-on application of data science techniques to solve problems in manufacturing and the Industrial Internet of Things (IIoT). Monitoring and managing operational performance is a crucial activity for industrial and business organisations. The emergence of low-cost, accessible computing and storage, through Industrial Digital Technologies (IDT) and Industry 4.0, has generated considerable interest in innovative approaches to doing more with data.
Data science, predictive analytics, machine learning, artificial intelligence and general approaches to modelling, simulating and visualising industrial systems have often been considered topics only for research labs and academic departments.
This textbook debunks the mystique around applied data science and shows readers, using tutorial-style explanations and real-life case studies, how practitioners can develop their own understanding of performance to achieve tangible business improvements. All exercises can be completed with commonly available tools, many of which are free to install and use.
Readers will learn how to use tools to investigate, diagnose, propose and implement analytics solutions that will provide explainable results to deliver digital transformation.

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Specificații

ISBN-13: 9783030791063
ISBN-10: 3030791068
Pagini: 275
Ilustrații: XXV, 275 p. 172 illus., 108 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.43 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Texts in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

1. Introduction to Industrial Analytics.- 2. Measuring Performance.- 3. Modelling and Simulating Systems.- 4. Optimising Systems.- 5. Production Control and Scheduling.- 6. Simulating Demand Forecasts.- 7. Investigating Time Series Data.- 8. Determining the Minimum Information for Effective Control.- 9. Constructing Machine Learning Models for Prediction.- 10. Exploring Model Accuracy.

Notă biografică

Dr. Richard Hill is Professor of Intelligent Systems, Head of the Department of Computer Science, and the Director of the Centre for Industrial Analytics at the University of Huddersfield, UK. His other publications include the Springer titles Guide to Vulnerability Analysis for Computer Networks and SystemsGuide to Security in SDN and NFVGuide to Security Assurance for Cloud ComputingBig-Data Analytics and Cloud ComputingGuide to Cloud Computing, and Cloud Computing for Enterprise Architectures.
 
Dr. Stuart Berry is Emeritus Fellow in the Department of Computing and Mathematics at the University of Derby, UK. His other publications include the Springer title Guide to Computational Modelling for Decision Processes.


Textul de pe ultima copertă

Monitoring and managing operational performance is a crucial activity for industrial and business organisations. The emergence of low cost, accessible computing and storage through the Industrial Internet of Things (IIoT) has generated considerable interest in innovative approaches to doing more with data.
Data Science, predictive analytics, machine learning, artificial intelligence and the more general approaches to modelling, simulating and visualizing industrial systems have often been considered topics only for research labs and academic departments.  This book debunks the mystique around applied data science and shows readers, using tutorial-style explanations and real-life case studies, how practitioners can develop their own understanding of performance to achieve tangible business improvements. 
Topics and features:
  • Describes hands-on application of data-science techniques to solve problems in manufacturing and the IIoT
  • Presents relevant case study examples that make use of commonly available (and often free) software to solve real-world problems
  • Enables readers to rapidly acquire a practical understanding of essential modelling and analytics skills for system-oriented problem solving
  • Includes a schedule to organize content for semester-based university delivery, and end-of-chapter exercises to reinforce learning
This unique textbook/guide outlines how to use tools to investigate, diagnose, propose and implement analytics solutions that will provide the evidence for business cases, or to deliver explainable results that demonstrate positive impact within an organisation.  It will be invaluable to students, applications developers, researchers, technical consultants, and industrial managers and supervisors.
Dr. Richard Hill is a professor of Intelligent Systems, head of the Department of Computer Science, and director of the Centre for Industrial Analytics at the University of Huddersfield, UK. His other Springer titles include Guide to Vulnerability Analysis for Computer Networks and Systems and Big-Data Analytics and Cloud ComputingDr. Stuart Berry is Emeritus Fellow in the Department of Computing and Mathematics at the University of Derby, UK. He is a co-editor of the Springer title, Guide to Computational Modelling for Decision Processes.

Caracteristici

Describes data science techniques for solving problems in manufacturing and the Industrial Internet of Things Presents case study examples using commonly available software to solve real-world problems Empowers a practical understanding of essential modeling and analytics skills for system-oriented problem solving