Polynomial Regression Model for Predicting PVC Ceiling Sales Based on Historical Data: A Case Study at CV. Kilaw Maju Bersama

Abstract

The growth of the building materials industry, particularly PVC ceilings, requires accurate sales forecasting to support production stability, distribution efficiency, and inventory management. CV. Kilaw Maju Bersama, a PVC ceiling distributor, faces weekly sales fluctuations based on 104 weekly observations from January 2023 to December 2024. This study develops a Polynomial Regression model to forecast PVC ceiling sales using average selling price, number of retail stores, and promotional activities as independent variables. A quantitative approach was applied through data collection, data cleaning, exploratory data analysis (EDA), chronological time-series train-test splitting without shuffling, Z-score standardization, polynomial degree selection using TimeSeriesSplit, and model evaluation using MAPE, RMSE, and R 2 . The results show that the degree-1 Polynomial Regression model achieved a training MAPE of 3.12%, RMSE of 129.741, and R 2 of 0.643, while the testing set achieved a MAPE of 4.57%, RMSE of 187.311, and R 2 of 0.380. These results indicate low prediction error and satisfactory generalization to unseen data. The model therefore provides a practical decision-support tool for weekly sales forecasting, inventory planning, and data-driven business decision-making in the building materials industry.