Sales Forecasting of Plug-in Board Products Using Linear Regression: A Case Study of Yaqi Factory
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Abstract
This study aims to develop a sales forecasting model for Yaqi Factory's plug-in board products, thereby optimizing inventory management and enhancing operational efficiency, by collecting sales data for 31 months, selecting five best-selling products, and using the linear regression method to analyze the relationship between sales volume and key factors such as price, inventory, and production volume. The results showed that the model constructed based on six-month data had high predictive ability (R value of 0.816), explained 81.6% of sales variation, and had significantly lower prediction error than the model based on three-month data (absolute error of 607 vs. 1528). In addition, simple moving average graphical analysis shows that the model still has room for optimization when facing products with significant sales fluctuations, such as B5330. This study provides empirical evidence and methodological references for establishing effective sales forecasting and inventory management systems for small and medium-sized manufacturing enterprises.
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