Using Multiple Linear Regression to Predict the Sales Value of the Zliten Cement Plant During the Period 1992-2022

Abstract

Sales forecasting is considered a strategic process in industrial institutions. It is a complex task, particularly when selecting independent variables that significantly affect sales value. Many Libyan industrial institutions still rely on traditional sales forecasting methods despite their ineffectiveness, which causes an imbalance between production value and sales value. This study aims to use the multiple linear regression method as a statistical tool to study and analyze the factors affecting the value of sales at the Zliten Cement Factory during the period from 1992 to 2022. The study relies on data obtained from the factory’s records, which include variables such as production value, capital, wages, number of workers, raw materials, and machinery and equipment. Through data analysis using the SPSS program, the results showed a strong relationship between the value of sales and these variables. The most influential factor was machinery and equipment, followed by production value, while capital did not show a statistically significant effect. The coefficient of determination (R²) indicated that 89.4% of the variation in sales value can be explained by the model. Based on these findings, it is recommended to focus on developing equipment and improving production efficiency as key factors that can increase sales value, as well as paying attention to periodic data analysis to support decision-making within the factory.