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Guide to industrial analyticssolving...

Berry, Stuart.

Guide to industrial analyticssolving data science problems for manufacturing and the internet of things /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Guide to industrial analyticsby Richard Hill, Stuart Berry.
Reminder of title:	solving data science problems for manufacturing and the internet of things /
Author:	Hill, Richard.
other author:	Berry, Stuart.
Published:	Cham :Springer International Publishing :2021.
Description:	xxv, 275 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Manufacturing processesData processing.
Online resource:	https://doi.org/10.1007/978-3-030-79104-9
ISBN:	9783030791049$q(electronic bk.)

Guide to industrial analyticssolving data science problems for manufacturing and the internet of things /
Hill, Richard.

Guide to industrial analyticssolving data science problems for manufacturing and the internet of things /[electronic resource] :by Richard Hill, Stuart Berry. - Cham :Springer International Publishing :2021. - xxv, 275 p. :ill., digital ;24 cm. - Texts in computer science,1868-095X. - Texts in computer science..

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.

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 Computing. Dr. 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.

ISBN: 9783030791049$q(electronic bk.)

Standard No.: 10.1007/978-3-030-79104-9doiSubjects--Topical Terms:

404285
Manufacturing processes
--Data processing.

LC Class. No.: TS183

Dewey Class. No.: 670.285

Guide to industrial analyticssolving data science problems for manufacturing and the internet of things /
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