Guide to Industrial Analytics
Solving Data Science Problems for Manufacturing and the Internet of Things
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Beschreibung
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
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.
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.
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
Autor*in
Richard Hill
Themen in »Guide to Industrial Analytics«
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