The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand
The performance of alternative forecasting methods that use hierarchical and direct forecasting strategies for predicting spare parts demand depends on the demand features. This paper uses data obtained from the South Korean Navy to identify the demand features of the spare parts that influence on the relative performance of the alternative forecasting methods. A logistic regression classification model for predicting the relative performance of the alternative forecasting methods for the spare parts demand by multivariate demand features was developed. This classification model minimised forecasting errors and inventory costs.
Year of publication: |
2013
|
---|---|
Authors: | Moon, Seongmin ; Simpson, Andrew ; Hicks, Christian |
Published in: |
International Journal of Production Economics. - Elsevier, ISSN 0925-5273. - Vol. 143.2013, 2, p. 449-454
|
Publisher: |
Elsevier |
Subject: | Hierarchical forecasting | Spare parts demand | Non-normal demand | Classification | Logistic regression |
Saved in:
Online Resource