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Machine learning approach for predicting production delays: a quarry company case study.

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This study applies machine learning and big data analytics for predictive maintenance in Industry 4.0. It successfully predicts production delays in a quarry, improving decision-making and reducing downtime.

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Area of Science:

  • Industrial Engineering
  • Data Science
  • Machine Learning

Background:

  • Industry 4.0 necessitates a shift from reactive to predictive manufacturing.
  • Implementing predictive maintenance presents challenges for industrial businesses.
  • Big data analytics and machine learning are key enablers for Industry 4.0.

Purpose of the Study:

  • To demonstrate the application of data analytics and machine learning for predicting production delays.
  • To provide a case study in a quarry firm for predictive modeling.
  • To enhance decision-making processes in manufacturing environments.

Main Methods:

  • Utilized the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology.
  • Developed five predictive models using algorithms: Decision Tree, Neural Network, Random Forest, Nave Bayes, and Logistic Regression.
  • Analyzed a dataset of six months of production records from two machines.

Main Results:

  • Multilayer Perceptron Neural Network and Logistic Regression models showed superior performance.
  • Accurate prediction of production delays achieved with a F-measure score of 0.973.
  • Identified key machine learning algorithms for effective production delay prediction.

Conclusions:

  • Machine learning and data analytics effectively predict production delays in industrial settings.
  • The study validates the value of predictive modeling for optimizing quarry operations.
  • Improved decision-making through accurate delay prediction leads to reduced production line interruptions.