Advanced Analytics For Industry 4.0

An Invitation for Contribution!

Advanced Analytics for Industry 4.0

These books aims to provide a reference for industry exective 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, which can be grouped into four main areas:

1. Descriptive Analytics; Its function is to dscribe, diagnose, and discover what trends and pattern soccur 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

These books aims to provide a reference for industry exective 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, which can be grouped into four main areas:

1. Descriptive Analytics; Its function is to dscribe, diagnose, and discover what trends and pattern soccur 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
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