利用有监督多变量聚类改进间歇性需求供应链中下游数据缺失客户的需求预测

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-02-28 DOI:10.1002/for.3095
Corey Ducharme, Bruno Agard, Martin Trépanier
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引用次数: 0

摘要

在类似供应商管理库存的供应链协作安排中,销售点的产品需求信息有望在供应链成员之间共享。然而,在实践中,获取此类信息的成本可能很高,而且有些成员可能不愿意或无法提供必要的数据访问权限。因此,拥有多个成员的大型协作供应链可能会在混合信息的情况下运行,即并非所有客户的销售点需求信息都是已知的。使用工业 4.0 技术的供应链上存在其他需求信息来源,而且越来越多,可以作为替代,但这些数据可能存在噪声、失真和部分缺失。在信息混杂的情况下,利用现有客户的销售点需求来改进信息缺失客户的间歇性需求预测还有待探索。我们提出了一种有监督的需求预测方法,利用多变量时间序列聚类来映射多个需求数据源。通过对具有相似交付模式的客户进行平均,对下游需求数据缺失的成员的最终需求预测结果进行改进。我们的结果表明,与信息缺失的传统间歇性需求预测方法相比,准确率最多可提高 10%。
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Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering

In a collaborative supply chain arrangement like vendor-managed inventory, information on product demand at the point of sale is expected to be shared among members of the supply chain. However, in practice, obtaining such information can be costly, and some members may be unwilling or unable to provide the necessary access to the data. As such, large collaborative supply chains with multiple members may operate under a mixed-information scenario where point-of-sale demand information is not known for all customers. Other sources of demand information exist and are becoming more available along supply chains using Industry 4.0 technologies and can serve as a substitute, but the data may be noisy, distorted, and partially missing. Under mixed information, leveraging existing customers' point-of-sale demand to improve the intermittent demand forecast of customers with missing information has yet to be explored. We propose a supervised demand forecasting method that uses multivariate time series clustering to map multiple sources of demand data. Members with missing downstream demand data have their resulting demand forecast improved by averaging over customers with similar delivery patterns for their final demand forecast. Our results show up to a 10% accuracy improvement over traditional intermittent demand forecasting methods with missing information.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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