Advanced Analytics for Industry 4.0

By Ali Soofastaei

ISBN: 
Publisher: CRC Press
Published: 
DOI: 
Number of pages: About 1500

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These books aims to provide a reference for industry executive managers, R&D specialists, advanced data analyzers, professors, and students who are working in the field of advanced data analytics and digital transformation.

Digital solutions are needed to develop advanced analytics applications in different industries. Advanced analytics has gained massive momentum in the industrial sector. Its evolution and conquest of the markets is unstoppable, along with its presence and importance as an essential tool.

The book is aimed at providing (I) industry executives with an understanding of the business value and applicability of different analytic approaches, and (II) data analytics leads with a business framework in which to assess the value, cost, and risk of potential analytic solutions as well and (III) undergraduate and graduate student of engineering with an understanding of data analytics applied to the different industries.

The main objectives of this book are presenting the scientific concepts and providing industrial case studies for different applications of advanced analytics.

 

  1. Digital Transformation for Traditional Industries
  2. Mining Industry
  3. Oil And Gas Industry
  4. Steel Industry
  5. Manufacturing Industry
  6. Food Industry
  7. Construction Industry
  8. Transport And Logistics Industry
  9. Chemical Industry
  10. Agriculture Industry
  11. Digital Transformation For Technology Industries
  12. Computer Industry
  13. Telecommunication Industry
  14. Aerospace Industry
  15. Electronics Industry
  16. Robotics Industry
  17. Ecommerce Industry
  18. Insurance Industry
  19. Information Technology Industry
  20. Biomechanics Industry

Dr. Ali Soofastaei is a global artificial intelligence (AI) projects leader, an international keynote speaker, and a professional author.

He completed his Ph.D. and Postdoctoral Research Fellow at The University of Queensland, Australia, in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a scientific supervisor, for many years, he has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology.

Dr. Soofastaei has more than fifteen years of academic experience as an Assistant Professor and leader of global research activities. Results from 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.

He 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.

Dr. Soofastaei is working actively with some prestigious global publishers same as Mc Graw-Hill Education, Intech Open, Springer, and CRC Press as an author and academic editor.

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Advanced Analytics for Industry 4.0

Slide 1

An Invitation for Contribution!

Advanced Analytics for Industry 4.0

These books aim to provide a reference for industry executive managers, R&D specialists, advanced data analyzers, professors, and students who are working in the field of advanced data analytics and digital transformation.

These books aim to provide a reference for industry executive managers, R&D specialists, advanced data analyzers, professors, and students who are working in the field of advanced data analytics and digital transformation.
Digital solutions are needed to develop advanced analytics applications in different industries. Advanced analytics has gained massive momentum in the industrial sector. Its evolution and conquest of the markets is unstoppable, along with its presence and importance as an essential tool.

These books are aimed at providing (I) industry executives with an understanding of the business value and applicability of different analytic approaches, and (II) data analytics leads with a business framework in which to assess the value, cost, and risk of potential analytic solutions as well and (III) undergraduate and graduate student of engineering with an understanding of data analytics applied to the different industries.

The main objectives of these books are to present the scientific concepts and provide industrial case studies for different applications of advanced analytics, which can be grouped into four main areas:

1. Descriptive Analytics; Its function is to describe, diagnose, and discover what trends and patterns occur in a given process, thanks to the real-time study of historical data. The most significant descriptive analytics applications are:

  • Real-time visualization of data;
  • Advanced visualization of data (e.g., creation of benchmark tables offering flexibility in terms of variables, generation of ad hoc reports, etc.); and
  • Descriptive statistics of processes and detection through PCA (e.g., detection of production anomalies).

2. Predictive Analytics; Based on more advanced mathematical methods that include statistical analyses, data mining, predictive models, and machine learning, among others. Its function consists of predicting events that can occur in the future, thanks to developing a predictive model. The major applications of predictive analytics are:

  • Prediction of anomalies and alerts;
  • Demand estimation; and
  • Forecasting process outcomes based on the values of variables (e.g., model for detecting product quality issues)

3. Prescriptive analytics; Its function consists of defining the actions to take to obtain the best results in a process. It relies on predictive models, scenario simulations, localized rules, and technical optimization to transform data and recommends taking to obtain the desired result. This level of analytics is completer and more robust. It uses complex event processing, neural networks, heuristic learning, and “machine learning,” among others. The most significant applications of prescriptive analytics are:

  • Generation of scenarios to recommend actions;
  • Identification of the best results in an autonomous way; and
  • Proactive updating of recommendations for action due to changing events.

4. Optimization; Optimization is one of the most important categories of advanced analytics.
The most significant applications of optimization are:

  • Process and scenario simulations; and
  • Analysis of the evolution and search for maximum and minimum key values

Project Time Table

Date Task
31st Dec 2021 Finalizing the project team members and assign the chapters to the authors
17th Jan 2022 Kick-off the project
25th Feb 2022 Project progress assessment
18th Mar 2022 Submit the first draft of the manuscript
15th Apr 2022 Receive the editorial team comments and feedback
30th Jun 2022 Submit the final version of manuscripts
29th Jul 2022 Received the finalized chapters

Chapters

  • 6 Parts

    Chapter(1): Digital Transformation for Traditional Industries

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    Modern technology is growing very fast, and it affects all industries’ dimensions. Traditional industries play a critical role in providing the initial materials for other…
  • 6 Parts

    Chapter(2): Mining Industry

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    Last activity on October 11, 2021
    The unprecedented demand for natural resources drives mining companies to strive for a step-change in optimizing their business processes. Advanced analytics techniques are being applied…
  • 6 Parts

    Chapter(3): Oil And Gas Industry

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    Advanced analytics is one of the critical components in the digitalization of the oil and gas (O&G) industry. Its focus is managing and processing a…
  • 6 Parts

    Chapter(4): Steel Industry

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    With an increasingly competitive environment, global steel companies are searching for ways to gain a competitive advantage. Steel companies have employed analytics for years, from…
  • 6 Parts

    Chapter(5): Manufacturing Industry

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    Advanced analytics can enable manufacturing engineers to improve product quality and achieve equipment and resource efficiency gains using large amounts of data collected during manufacturing.…
  • 6 Parts

    Chapter(6): Food Industry

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    Food Industries have to cater to a plethora of consumers having a variety of tastes. For sustaining in such an environment, companies create their unique…
  • 6 Parts

    Chapter(7): Construction Industry

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    The construction industry deals with large volumes of heterogeneous data, which is expected to increase exponentially as technologies such as sensor networks and the Internet…
  • 6 Parts

    Chapter(8): Transport And Logistics Industry

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    To face the worldwide competition and meet the new information technologies era’s recent requirements, digitalization and adoption of new information techniques have become a must…
  • 6 Parts

    Chapter(9): Chemical Industry

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    Data and analytics are playing a revolutionary role in the chemical industry. This chapter provides an overview of the challenges confronting the chemical industry and…
  • 6 Parts

    Chapter(10): Agriculture Industry

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    The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land,…
  • 7 Parts

    Chapter(11): Digital Transformation For Technology Industries

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    Advanced analytics has gained massive momentum in the industrial sector. Its evolution and conquest of the markets is unstoppable, along with its presence and importance…
  • 6 Parts

    Chapter(12): Computer Industry

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    In recent years “Big Data” has become something of a buzzword in business, computer science, information studies, information systems, statistics, and many other fields. As…
  • 6 Parts

    Chapter(13): Telecommunication Industry

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    Data is one of the most strategic assets for Telecom Operators today. With the rampant adoption of smartphones and growth in mobile internet, Telecom Operators…
  • 6 Parts

    Chapter(14): Aerospace Industry

    16% Complete
    Last activity on April 8, 2021
    The aerospace industry is poised to capitalize on Big Data and machine learning, which excels at solving the types of multi-objective, constrained optimization problems that…
  • 6 Parts

    Chapter(15): Electronics Industry

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    Advanced Analytics with Big Data has been widely used in the lifecycle of electronic products that range from the design and production stages to the…
  • 6 Parts

    Chapter(16): Robotics Industry

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    RoboticProcessAutomation(RPA)isgoingintoa”maturitymarket.”Theleading vendor providers surpassed billion dollars in the evaluation, and the research they are launching these days on the market will change again radically the…
  • 6 Parts

    Chapter(17): Ecommerce Industry

    0% Complete
    Last activity on April 5, 2021
    E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Advanced analytics has acted as a catalyst in their growth…
  • 6 Parts

    Chapter(18): Insurance Industry

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    Advances in Big Data analytics, artificial intelligence, and the Internet of Things transform the insurance industry, and data play a critical role in insurance strategy…
  • 6 Parts

    Chapter(19): Information Technology Industry

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    Nowadays a Big Data analytics is an extensive area for both academia and industry. Big Data analytics has attracted intense interest from all academia and…
  • 6 Parts

    Chapter(20): Biomechanics Industry

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    Much of the biomechanical research over the past 20 years has investigated the influence of potential injury risk factors in isolation. More likely, multiple biomechanical…

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