Table 1. Indicators Quantifying Supply Chain Disruptions

|Surveys of supply chain<br>professionals|Purchasing Managers’ Index and components (IHS Markit, S&P), see section 5|
|---|---|
|Tracking, Schedule Data|**Global Supply Chain Stress Index (GSCSI)**<br>Schedule reliability (Sea Intelligence), see section 5<br>Ocean TimelinessIndicator<sup>1</sup> (Flexport)|
|Meta indicators|Global Supply Chain Pressure Index (FED New York), see section 5<br>SupplyChain StabilityIndex,<sup>2</sup>KPMG|

Source: Authors.

The current working paper informs the underlying methodology and use cases for the GSCSI, which belongs to the second category of indicators. The rest of the working paper is organized as follows: The next section (2) expands the stress index's conceptual framework. Section 3 explains how the index is derived from AIS tracking data, while Section 4 describes real use cases. Section 5 compares the proposed stress index with other indicators developed by the private sector or governmental institutions. Section 6 proposes a rate model in accordance with observed patterns of rate hikes in times of disruptions, and Section 7 concludes.

# **2. Conceptual Framework and Data Sources**

A stress index attempts to quantify deviations from a norm, which is the typical steady state in which a system operates. The greater the deviation, the more likely the system is to experience stress. However, some level of deviation is expected due to unforeseen events such as a storm or a break in equipment, so only large deviations are considered indicative of stress. Others are a variability in regular operations. A specific threshold for the standard deviation needs to be defined to determine when a deviation is significant enough to indicate stress. Global demand for container shipping has seasonal patterns, with a marked low in February. While disruptions typically have no seasonal patterns, the industry may have more capacity to absorb them in the first quarter of the year.

In this conceptual framework, global container shipping is schematized as a directed network, where the nodes represent ports, and the edges represent the shipping connections between ports used by the shipping lines' loop, or inter-range, services (Figure 1). Under normal circumstances, these loop services are highly predictable, with consistency in:

- (i) Lead times: The transit time between the departure port (d) and the subsequent arrival port (a).

- (ii) Turnaround times: The time spent at each port, especially for loading and unloading operations.

Network data at a given time is therefore characterized by the moving trade capacity (expressed in TEUs) between ports, as well as the consistent transit times, which tend to be relatively stable within the same ship class. Therefore, the mobilized trade capacity (flowing capacity) on a given connection can be calculated as: Mobilized trade capacity = Flowing capacity per unit of time × Transit time However, operational disruptions such as congestion, around the destination port can lead to:

> 1 <u>https://www.flexport.com/research/ocean-timeliness-indicator/</u>

> 2 <u>https://kpmg.com/us/en/articles/2023/supply-chain-stability-index.html</u>

3