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

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.