A Comparison of Moving Average and Simple Exponential Smoothing Methods for Predicting Engine Oil Change Demand in Trucks: A Case Study in Songkhla Province
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Abstract
The objective of this research is to predict the demand for engine oil changes in large trucks using monthly data from a case study service center between 2019 and 2021. This study focuses on comparing the performance of various prediction methods to determine the most suitable approach for forecasting future service demands. The prediction methods employed in this research comprise two main approaches: Moving Average (MA) with 3 and 5-month periods, and Exponential Smoothing with alpha (α) values of 0.1 and 0.5. Each method was applied to the same dataset to generate predictions, followed by a performance evaluation. To assess prediction accuracy, three criteria were utilized: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The results demonstrate that the Exponential Smoothing method with an alpha value of 0.5 yields the most accurate predictions based on MAE, MSE, and MAPE, indicating its superior ability to predict values closely aligned with actual figures. This method is recommended for planning engine oil demand in large trucks at the case study company.
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