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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

Titel:	Guide to industrial analytics solving data science problems for manufacturing and the internet of things
Autor/in / Beteiligte Person:	Hill, Richard ; Berry, Stuart
Link:	http://lobid.org/resources/99371069133906441
Reihe:	Texts in Computer Science
Veröffentlichung:	Cham: Springer, 2021
Medientyp:	Buch
Umfang:	XXV, 274 Seiten
ISBN:	978-3-030-79103-2 (print)
Schlagwort:	Computer Science Data Mining and Knowledge Discovery Data Mining B Big Data Machines, Tools, Processes Produktionstechnik Manufacturing, Machines, Tools, Processes Machine Learning Maschinelles Lernen Computer Communication Networks Computerkommunikation & -vernetzung Simulation and Modeling Computersimulation & Modelle, 3-D Graphik Modellierung & Simulation Hardcover, Softcover / Informatik, EDV/Informatik Data mining Big data Manufactures Machine learning Computer communication systems Computer simulation Computer science
Sonstiges:	Nachgewiesen in: hbz-Verbundkatalog Sprachen: English Contents Note: 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. Document Type: Monograph File Description: Text Language: English

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Guide to industrial analytics solving data science problems for manufacturing and the internet of things