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.

中文翻译：

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通过监督多元聚类改进间歇性需求供应链中缺少下游数据的客户的需求预测

在供应商管理库存等协作供应链安排中，销售点的产品需求信息预计将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，并且一些成员可能不愿意或无法提供对数据的必要访问权限。因此，具有多个成员的大型协作供应链可能在混合信息场景下运行，其中并非所有客户都知道销售点需求信息。其他需求信息来源也存在，并且在使用工业 4.0 技术的供应链中变得越来越可用，并且可以作为替代品，但数据可能存在噪音、扭曲和部分缺失。混合信息下，利用现有客户的销售点需求来改善信息缺失客户的间歇性需求预测还有待探索。我们提出了一种监督需求预测方法，该方法使用多元时间序列聚类来映射需求数据的多个来源。缺少下游需求数据的成员通过对具有相似交付模式的客户进行平均来改进其最终需求预测。我们的结果显示，与缺少信息的传统间歇性需求预测方法相比，准确度提高了 10%。