Overview

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.

Full Product Details

Author:   Richard Hill ,  Stuart Berry
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Weight:   0.462kg
ISBN:  

9783030791063

ISBN 10:   3030791068
Pages:   275
Publication Date:   29 September 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

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.- 11. Building a Business Case for Sensing Technology Adoption.- 12. Improving Throughput by Utilising Sensor Data.- 13. Constructing a Digital Factory Model to Optimise Production.- 14. Examining a Digital Supply Chain.- 15. Probability Essentials.- 16. Building Simulations with Spreadsheets.- 17. Using Python to Construct Manufacturing Models.- 18. Predictive Analytics with KNIME.- 19. Using R to Evaluate Data.- 20. Example Case Study Data.

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Author Information

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.

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