Detecting the spatiotemporal characteristics of the supply-chain disruption and estimating its short term effects

Ships are often considered as the backbone of the global economy. A fundamental unresolved problem is how to best operate fleets, given a sudden increase in demand, such as that reported following the first months of the COVID19 pandemic. Advancing our knowledge of the supply chain's delicate equilibrium between demand and supply, requires analyzing huge amounts of ship-related positional data, thus revealing which areas should be avoided due to potential congestion buildup and where cargoes should be rerouted to. Herein, we analyze a large-scale high-resolution mobility data set of more than 7,000 container ships, collected over an extended period of 36 months, covering the entire globe, so as to measure quantitatively the effects of the pandemic post-hoc on the supply chain. To further understand these fine-grained mobility patterns, we introduce a mobility model for calculating ship presence times (or waiting times) at a global scale. We then reveal the congestion points, which strongly correlate with port waiting areas and anchorages. We analyse the data to reveal how times at port areas were affected by the rising number of ships waiting to load or unload cargo. Following this, we transform this data into a ‘port to port’ graph mapping the international flow of  containerised trade. We apply methods of graph theory, complex networks, and multivariate statistics to unravel the hidden relationships between global maritime structure and ship time distribution.
This analysis is novel in respect to the size of the data analyzed, the algorithmic approach and the impact of the results which reveal some affinities between pre-COVID and post-COVID shipping patterns.