Demand pattern identification of automotive spare parts
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Abstract
Purpose: To classify the type of product demand placed on the market by auto parts companies in Mexico focusing on the assembly and sale of auto parts at national and international level, which is the basis for the adequate provision of materials in the studied supply chain.
Methodologyical design : From a total of 14,895 products, 326 components were selected through the ABC method to perform the demand pattern analysis which was carried out according to the average demand interval and the square coefficient of variation using the monthly demands of each product.
Results: The probabilistic analysis of the demand for the 14,895 products shows smoothed (63.80%), erratic (19.94%), lumpy (11.35%) and intermittent (4.91%) demand patterns from which it is concluded that the demand patterns for these companies are mainly of the smoothed type.
Research limitations: The probabilistic analysis conducted is based on the data provided by three autoparts companies in México, of which, after the ABC analysis, only the articles of category A were considered for the results obtained. Proposing a different technique other than ABC analysis is limited by the type of data provided by companies.
Findings: Due to the number of factors involved in demand variability, it is vital to rely on tools that aid in reaching trustworthy demand forecasts, maintaining companies competitive in customer service quality. Further, the complexity of forecasting automotive spare parts is a challenge which the automotive industry is currently facing. The classification of demand patterns resulting from the study allows for the selection of an appropriate forecasting method for each pattern and improvement of supply conditions of the different companies. This type of study and data analysis permits better decision-making by those responsible for the supply components.
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