- Finally, the concept used in some countries to measure delays may differ from the definitions used here to ensure global comparability. For instance, in many places, containers may be moved for processing at terminal to satellite facilities in the same general location. Our definition consolidates the time spent at all facilities, not just at the port terminal.

## Potential for more indicators from the existing data

The current data can support more indicators than in Table 2. The following indicators could be considered in the future:

1. **How time at destination depends on origin** . Trade procedures and transfer of information or payment at destination may be influenced by the country of origin. An indicator could be a weighted variance of dwell time depending on origin.

2. **Efficiency of domestic logistics** (KPI based on the logistics of empty containers). The tracking data covers the responsibility of the international logistics operators, not logistics done by shippers upstream or consignees downstream the supply chain. Supply chain practices by the latter may vary. However, container data include information on the movement of empty containers that proxies the time taken to stuff export containers or deliver full import containers at destination. The lead time for an empty container to come back full is an indication of the efficiency of domestic logistics.

3. **Cold chain logistics** . Refrigerated containers (reefers) are important for food trade. Differences in lead time for reefers compared to ordinary containers could be informative, especially on the export side.

4. **Accessibility indicators** as weighted average over origin countries of the lead time to destination country.

# **4. Conclusions and future directions**

The experience of the 2023 analytics of the large tracking datasets is encouraging. KPI derived from Big Data provide new insights, more actionable than the traditional LPI (e.g., dwell time). The following directions could reinforce the value of the KPIs. The first is making the production sustainable by strengthening the data partnerships with industry. The discontinuation of TradeLens means that other data partnerships are needed to access comparable data, either direct partnerships with the shipping lines or purchasing the data from commercial providers.

The second direction is to explore the potential of new indicators from the same dataset (see section 3). A third area is producing the established overall LPI on a 1-5 scale from Big Data. It is possible that the full set of KPIs can accurately help predict the survey-based LPI. A respondent to the classical LPI survey is influenced by his/her actual experience of timeliness, speed and delays of supply chains when rating a partner country. This information is objectively captured by the new KPIs. In statistical terms, the tracking data may constitute a “sufficient statistic” of the LPI, and the survey-based LPI will hence be discontinued.

Finally, establishing more data partnerships and obtaining more data are desirable. Having global or largescale sources on international rail or road movements or truck movements would enrich the set of indicators. Since shipment tracking databases compile waybills or manifests, they include information on the nature and value of goods and freight costs, adding new insights to the current ones focused on speed of trade and connectivity.

19