Overview
- 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
Part of the book series: Texts in Computer Science (TCS)
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Table of contents (10 chapters)
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Front Matter
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Introductory Concepts
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Front Matter
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Back Matter
Keywords
- Analytics
- Simulation
- Modeling
- Optimization
- Batch Manufacture
- Industrial Internet of Things (IIoT)
- Internet of Things (IoT)
- Industry 4.0
- Machine Learning (ML)
- Statistics
- Manufacturing Systems
- Production Control
- Planning
- Forecasting/Prediction
- Scheduling
- Manufacturing Resource Planning (MRP II)
- Capacity Planning
About this book
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.
Authors and Affiliations
About the authors
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 Systems, Guide to Security in SDN and NFV, Guide to Security Assurance for Cloud Computing, Big-Data Analytics and Cloud Computing, Guide 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.
Bibliographic Information
Book Title: Guide to Industrial Analytics
Book Subtitle: Solving Data Science Problems for Manufacturing and the Internet of Things
Authors: Richard Hill, Stuart Berry
Series Title: Texts in Computer Science
DOI: https://doi.org/10.1007/978-3-030-79104-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-79103-2Published: 28 September 2021
Softcover ISBN: 978-3-030-79106-3Published: 29 September 2022
eBook ISBN: 978-3-030-79104-9Published: 27 September 2021
Series ISSN: 1868-0941
Series E-ISSN: 1868-095X
Edition Number: 1
Number of Pages: XXV, 275
Number of Illustrations: 64 b/w illustrations, 108 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Big Data, Manufacturing, Machines, Tools, Processes, Machine Learning, Computer Communication Networks, Simulation and Modeling