Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: a support vector machine model
Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: a support vector machine model
复制标题
重型汽车行业备件间歇性需求预测：支持向量机模型
DOI：
10.1080/00207543.2020.1842936
复制
发表时间：
2020-11-11
影响因子：
9.2
通讯作者：
Liu, Xiao
中科院分区：
文献类型：
--
作者：
Jiang, Peng;Huang, Yibin;Liu, Xiao
在重型汽车行业，对备件的需求经常是间歇性的。需求的不确定性和不确定性对传统的需求预测模型提出了挑战。已经观察到支持向量机（SVM）模型产生与现有模型竞争的准确性。然而，基本的SVM模型仍然存在局限性。首先，耗时的计算并没有带来统计上显著的精度提高。第二，基于预测的库存绩效没有得到充分的探讨。第三，缺乏对需求预测模型鲁棒性的解释。建立了一个自适应单变量支持向量机（AUSVM）模型来预测间歇性需求。与现有的12个模型和改进的神经网络相比，该方法的有效性通过一家重型汽车零部件公司的实际数据得到了证明。AUSVM在计算时间上明显优于基本SVM和神经网络。重型汽车案例的计算结果表明，与著名的参数模型相比，AUSVM实现了统计上显着的精度提高和更好的库存性能的一组非光滑的需求序列。讨论了AUSVM用于重型汽车备件需求预测和库存控制的原因。为重型汽车行业的从业人员提供了一些见解。
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.