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Innovation in Scientific Knowledge Based on Forecasting Assessment: A Case Study on Automotive Spare Parts Demand
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Author(s): Ignacio Aranís Mahuzier (Universidad Técnica Federico Santa María, Chile), Pablo A. Viveros Gunckel (Universidad Técnica Federico Santa María, Chile), Rodrigo Mena Bustos (Universidad Técnica Federico Santa María, Chile), Christopher Nikulin Chandía (Universidad Técnica Federico Santa María, Chile)and Vicente González-Prida Díaz (University of Sevilla, Spain & Universidad Nacional de Educación a Distancia (UNED), Spain)
Copyright: 2019
Pages: 17
Source title:
Handbook of Research on Industrial Advancement in Scientific Knowledge
Source Author(s)/Editor(s): Vicente González-Prida Diaz (Universidad de Sevilla, Spain & Universidad Nacional de Educación a Distancia (UNED), Spain)and Jesus Pedro Zamora Bonilla (Universidad Nacional de Educación a Distancia (UNED), Spain)
DOI: 10.4018/978-1-5225-7152-0.ch013
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Abstract
This chapter presents a study of forecasting methods applicable to the spare parts demand faced by an automotive company that maintains a share of nearly 25% of the automotive market and sells approximately 13,000 parts per year. These parts are characterized by having intermittent demand and, in some cases, low demand, which makes it difficult for such companies to perform well and to obtain accurate forecasts. Therefore, this chapter includes a study of methods such as the Croston, Syntetos and Boylan, and Teunter methods, which are known to resolve these issues. Furthermore, the rolling Grey method is included, which is usually used in environments with short historical series and great uncertainty. In this study, traditional methods of prognosis, such as moving averages, exponential smoothing, and exponential smoothing with tendency and seasonality, are not neglected.
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