Forecasting Engine Oil Change Demand using Holt-Winters for Inventory Planning: A Case Study of a Truck Service Center in Songkhla
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Abstract
The forecasting demand for oil changes using holt-winters models: a case study of a truck service center in Songkhla province aims to develop a forecasting model for oil change demand for large trucks using Holt-Winters and linear trend models. The study uses monthly data from a truck service center in Songkhla Province from January 2019 to December 2021, comprising 36 months of data. This research compares the performance of several time series forecasting models, including Holt's Linear Trend, Brown's Linear Trend, Damped Linear Trend, Winter's Multiplicative, and Winter's Additive, to determine the most suitable method for future service demand planning. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The Winter's Additive model yielded the most accurate forecasts, exhibiting the lowest MAPE of 7.45% (95% Confidence Interval: 5.27 - 9.64) with a p-value of 0.159 between models. This highlights the model's ability to accurately forecast engine oil change demand. The findings are applicable to improving inventory management, resource allocation, service provision, and reducing inventory costs at the Songkhla truck service center.
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