Analyzing the Factors Affecting Plastic Cup Production using Multiple Regression Models

Abstract

The plastic manufacturing industry is under constant pressure to enhance productivity and optimize resources. This study investigates the key operational factors influencing production quantity in a plastic cup manufacturing process. Specifically, it analyzes the impact of training hours, idle time, and equipment efficiency on output. Data from 120 production samples were collected and analyzed using three statistical models: Multiple Linear Regression, Quadratic Regression, and Cubic Regression. The models were evaluated based on R-squared values, p-values, and residual analysis. The results indicate that all three independent variables have a statistically significant positive impact on production quantity. The Multiple Linear Regression model was identified as the most effective, achieving an R-squared value of 91.34% and demonstrating significance for all predictors without unnecessary complexity. The study concludes that optimizing staff training, minimizing machine idle time through predictive maintenance, and maintaining equipment efficiency above 90% are critical strategies for significantly enhancing production output and reducing operational waste.