Paper Info
Title
Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: a support vector machine model
Abstract
Intermittent demand occurs commonly for spare parts in the heavy-duty vehicle industry. Demand uncertainty and intermittency pose challenges to demand forecasting by conventional models. Support vector machine (SVM) models have been observed to yield competitive accuracy with existing models. However, there are still limitations for basic SVM models. First, the time-consuming computation does not bring a statistically significant accuracy improvement. Second, the forecasting-based inventory performance has not been sufficiently explored. Third, scarce explanations of model robustness are offered for demand forecasting. We build an adaptive univariate SVM (AUSVM) model to forecast intermittent demand. Its effectiveness, compared to 12 existing models and an improved neural-network, is demonstrated by real-world data from a heavy-duty vehicle spare-part company. AUSVM has an apparent advantage in computation time over basic SVM and neural networks. The computational results of the heavy-duty vehicle case indicate that, compared to well-known parametric models, AUSVM achieves a statistically significant accuracy improvement and better inventory performance for the group of non-smooth demand series. Discussions are presented on why AUSVM works for demand forecasting and inventory control of heavy-duty vehicle spare parts. Several insights are revealed for practitioners in the heavy-duty vehicle industry.
Year
DOI
Venue
2021
10.1080/00207543.2020.1842936
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Keywords
DocType
Volume
Forecasting, intermittent demand, spare parts, inventory&#160, &#8204, performance, heavy-duty vehicle, support vector machine
Journal
59
Issue
ISSN
Citations 
24
0020-7543
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Peng Jiang100.34
Yibin Huang200.34
Xiao LIU3598.40