论文标题
在线检测供应链网络中断，使用霍克斯流程的顺序更改点检测
Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes
论文作者
论文摘要
在本文中，我们试图检测到来自大型家具公司收到的供应链数据的Covid-19引起的拐点或变化点。为此，我们在公司的时空订单数据以及基于GLR（广义的似然比）方法上采用了修改后的CUSUM（累积总和）程序。我们通过离散空间数据并将每个顺序视为具有相应的节点和时间的事件，使用Hawkes Process网络（一种多维自我和相互激动的点过程）对订单数据进行建模。我们在国家规模上应用了该方法的方法，并深入研究了一个州。由于该项目在该州与国家相比很少订购，因此这种方法使我们能够在不同程度的数据稀少度上表现出功效。此外，它展示了在不同水平的空间细节范围内使用潜力。
In this paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company. To accomplish this, we utilize a modified CUSUM (Cumulative Sum) procedure on the company's spatial-temporal order data as well as a GLR (Generalized Likelihood Ratio) based method. We model the order data using the Hawkes Process Network, a multi-dimensional self and mutually exciting point process, by discretizing the spatial data and treating each order as an event that has a corresponding node and time. We apply the methodologies on the company's most ordered item on a national scale and perform a deep dive into a single state. Because the item was ordered infrequently in the state compared to the nation, this approach allows us to show efficacy upon different degrees of data sparsity. Furthermore, it showcases use potential across differing levels of spatial detail.
