Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes
This work addresses supply chain monitoring for companies, but it is incremental as it applies existing change-point detection methods to a new dataset.
The paper tackled the problem of detecting disruptions in supply chain networks, specifically change-points due to the Covid-19 pandemic, using sequential change-point detection methods on spatial-temporal order data from a furniture company, and demonstrated efficacy across varying data sparsity levels.
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.