Comparison of time series forecasting methods for automotive spare parts demand
DOI:
https://doi.org/10.33064/iycuaa2025945451Keywords:
demand forecast, spare parts, demand patterns, supply chain, time seriesAbstract
The inevitable need for companies in the automotive spare parts sector to forecast their production levels is the basis for the development of this work. This article shows a probabilistic analysis of the demand of three Mexican auto parts companies focused on production and sale at a national and international level. 312 time series were analyzed based on the monthly historical demand by applying forecasting methods such as moving averages, Winters, multiplicative decomposition, ARIMA (Box - Jenkis), Croston, and Syntetos-Boylan Approximation (SBA) to find the best goodness-of-fit measure. The performance of each method was evaluated through the absolute percentage error (MAPE) and the mean squared error (MSE). The results showed a reduction of 60.77% of MAPE in the smooth pattern and 70.60% in the erratic. This research contributes to the literature of auto parts sector to support decision making.
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