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Guide to Industrial Analytics Richard Hill

Guide to Industrial Analytics By Richard Hill

Guide to Industrial Analytics by Richard Hill


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Guide to Industrial Analytics Summary

Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things by Richard Hill

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.


About Richard Hill

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.


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.

Additional information

NPB9783030791032
9783030791032
3030791033
Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things by Richard Hill
New
Hardback
Springer Nature Switzerland AG
2021-09-28
275
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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