Advanced Analytics for Industry 4.0, Two Volume Set
CRC Press
978-1-032-03351-8 (ISBN)
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Digital solutions are needed to develop advanced analytics applications in different industries. The main objectives of this two volume set are presenting the scientific concepts and providing industrial case studies for different applications of advanced analytics, which can be grouped into three main areas namely descriptive, predictive and prescriptive analytics. Main prerogatives include improving understanding of the business value and applicability of different analytic approaches, and business framework to assess the value, cost, and risk of potential analytic solutions. It covers pertinent aspects of data analytics for mother and technology industries.
Provides a concise overview of state of the art for mother and technology industries executives and managers
Highlights and describes critical opportunity areas for industries operations optimization
Explains how to implement advanced data analytics through case studies and examples
Provides approaches and methods to improve data-driven decision making
Brings experience and learning in digital transformation from adjacent sectors
This two volume set aims at researchers, professionals, graduate students in data science, manufacturing, automation and computer engineering
Ali Soofastaei is a Global Projects Leader at Vale Artificial Intelligence Centre.Vale is a multinational corporation engaged in metals and mining. It is one of the world’s foremost producers of iron ore and the largest producer of nickel. Soofastaei leads innovative industrial projects in artificial intelligence (AI) applications to improve safety, productivity, and energy efficiency and reduce maintenance costs. Soofastaei completed his Ph.D. at The University of Queensland (UQ) in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning (DL) and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As an assistant professor, he has provided undergraduate and postgraduate students with practical guidance in engineering and information technology. In the past 15 years, he has conducted various research studies in academic and industrial environments. He has acquired in-depth knowledge of energy efficiency opportunities (EEO) and advanced analytics. He is an expert in using DL and AI methods in data analysis to develop predictive, optimization, and decision models of complex systems. Soofastaei has been involved in industrial research and development projects in several industries, including oil and gas (Royal Dutch Shell); steel (Danieli); and mining (BHP, Rio Tinto, Anglo American, and Vale). His extensive practical experience in the industry has equipped him to work with complex industrial problems in highly technical and multi-disciplinary teams. As a research and development team member, Soofastaei has been actively involved in site inspections, business problem identification, and root cause analysis. He has experience in managing brainstorming sessions with operators, supervisors, managers, and original equipment manufacturers (OEMs) in the areas of automation (e.g., with electrical and computer systems engineers), maintenance (e.g., with mechanical engineers and maintenance supervisors), and production (e.g., process engineers, metallurgists). Soofastaei has more than ten years of academic experience as an assistant professor and a global research leader. His research and development projects have been published in international journals and keynote presentations; He has presented his practical achievements at conferences in the United States, Europe, Asia, and Australia.
Volume 1:
Chapter 1: Navigating the Fourth Industrial Revolution: The Advent of Advanced Analytics in Traditional Industries Chapter 2: Transforming Mining Operations: Harnessing Advanced Analytics for Optimal Decision-Making Chapter 3: Designing Intelligence: Harnessing Soft Sensors and Advanced Analytics in Petroleum Refining for Industry 4.0 Chapter 4: Harnessing the Convergence of Information Technology and Operational Technology for Digital Transformation: An Integrated Framework for Effective Project Management, Skill Development, Team Coordination, and Collaboration in Manufacturing Industry Chapter 5: Harnessing Industrial Internet of Things: Enabling Artificial Intelligence and Machine Learning for Optimized Industrial and Manufacturing Processes Chapter 6: Digitizing the Palate: Exploring Opportunities for Digital Transformation in the Food Industry Chapter 7: Constructing Tomorrow: Exploring the Future of Construction in the Era of Industry 4.0 Chapter 8: Leveraging Advanced Analytics for Transforming Logistics: The Road to Logistics 4.0 Chapter 9: Revolutionizing Chemical Engineering 4.0: Artificial Intelligence Innovations and Machine Learning Chapter 10: Harvesting Tomorrow: The Future of Agriculture in Industry 4.0 Chapter 11: Artificial Intelligence in Insurance: Transforming Risk Management and Customer Experience
Volume 2:
1. Introduction 2. Aerospace Industry 3. Car Manufacturing 4. Marketing 5. Steel Manufacturing 6. Biomechanics Industry 7. Utilities 8. Energy Industry 9. Infrastructure Industry 10. Shipping Industry
| Erscheint lt. Verlag | 31.3.2026 |
|---|---|
| Zusatzinfo | 63 Tables, black and white; 202 Line drawings, black and white; 35 Halftones, black and white; 237 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Technik ► Bauwesen | |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 1-032-03351-7 / 1032033517 |
| ISBN-13 | 978-1-032-03351-8 / 9781032033518 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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