A Comparison of Forecasting Methods for Oil Filter Replacement Demand in Eco Cars Using Moving Average and Simple Exponential Smoothing
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Abstract
This study aims to forecast the demand for oil filter replacement for eco-cars using monthly data from a service center case study from January 2020 to July 2023. The study focuses on comparing the performance of two primary forecasting methods: Moving Average (MA) with 3- and 5-month windows, and Simple Exponential Smoothing (SES) with alpha (α) values of 0.1 and 0.5, to identify the most suitable approach for predicting future demand. The performance of each method was evaluated using three accuracy metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that the Simple Exponential Smoothing method with an alpha value of 0.5 provides the most accurate forecasts across all metrics, with an MAE of 8.50, MSE of 120.00, and MAPE of 7.70%, which are significantly lower than other methods. The Moving Average method with a 3-month window (MA3) was the second-best performer.
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