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2024 | OriginalPaper | Chapter

Prediction Model of Product Quality in Production Company: Based on PCA and Logistic Regression

Author : Katarzyna Antosz

Published in: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems

Publisher: Springer Nature Switzerland

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Abstract

The paper presents the possibility of implementation the Principle Component Analysis (PCA) and logistic regression to develop the decision tool supporting quality of oil inserts for candles production process. The research methodology was divided into three stages: collecting data from the production process, using the PCA method to reduce the dimensionality of the production process parameters, and using logistic regression to develop a predictive model for assessing the quality of products. The results of the research showed that it was enough to have five main components to keep about 84% of the information on variability. In the third stage the logistic regression to develop the predictive model of product quality in production company was used. The developed model explain the impact of the production parameters on product quality. In addition, this decision tool will help the managers in the company identification the improvement actions.

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Title: Prediction Model of Product Quality in Production Company: Based on PCA and Logistic Regression
Author: Katarzyna Antosz
Publisher: Springer Nature Switzerland
Book: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems
Print ISBN: 978-3-031-38164-5

Electronic ISBN: 978-3-031-38165-2

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-38165-2_50