metadata: authors: 0: author: Arvis,Jean Francois
1: author: Ulybina,Daria
2: author: Wiederer,Christina Katharina
count: World
docna: 0: docna: From Survey to Big Data : The New Logistics
            Performance Index
docty: Policy Research Working Paper
theme: Trade Logistics,Trade Facilitation,Economic Policy,Trade Policy,Trade
abstracts: cdata!: The World Bank has published the
            Logistics Performance Index since 2007. The Logistics
            Performance Index used to be based exclusively on perception
            ratings from a global survey of logistics professionals. In
            2023, it was augmented with key performance indicators
            derived from massive global international shipment tracking
            data (data on container shipping, air cargo, and postal
            logistics). The new set of indicators measure the speed and
            connectivity of international supply chains. This paper
            presents the data sources, rationale, and production of the
            indicators. It does not discuss the findings from the new
            indicators, nor does it introduce additional empirical work.
            The paper complements the 2023 issue of Connecting to
            Compete, the companion report to the Logistics Performance Index.
display_title: From Survey to Big Data : The New
            Logistics Performance Index
listing_relative_url: /research/2024/05/34319158/survey-big-data-new-logistics-performance-index
topicv3: Big Data m1327333 661
publishtoextweb_dt: 2024-05-16T00:00:00Z
dois: 10.1596/1813-9450-10772
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 43
author_keys: 0: author_key: 452197
1: author_key: 1814225
2: author_key: 1207423
model_extractions: Policy Research Working Paper 10772 From Survey to Big Data The New Logistics Performance Index _Jean-François Arvis Daria Ulybina Christina Wiederer_ Macroeconomics, Trade and Investment Global Practice May 2024
Policy Research Working Paper 10772

# **Abstract**

The World Bank has published the Logistics Performance Index since 2007. The Logistics Performance Index used to be based exclusively on perception ratings from a global survey of logistics professionals. In 2023, it was augmented with key performance indicators derived from massive global international shipment tracking data (data on container shipping, air cargo, and postal logistics). The new set of indicators measure the speed and connectivity of international supply chains. This paper presents the data sources, rationale, and production of the indicators. It does not discuss the findings from the new indicators, nor does it introduce additional empirical work. The paper complements the 2023 issue of Connecting to Compete, the companion report to the Logistics Performance Index.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jarvis1@worldbank.org, cwiederer@worldbank.org, and dulybina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# From Survey to Big Data: The New Logistics Performance Index

Jean-François Arvis, Daria Ulybina, and Christina Wiederer<sup>1</sup> JEL: F10, R40

> 1 The authors are all in the World Bank’s <mark>Trade and Regional Integration Unit (</mark> **<mark>ETIRI</mark>** <mark>) and</mark> can be reached at <u>jarvis1@worldbank.org, dulybina@worldbank.org, cwiederer@worldbank.org.</u>
# **Table of Contents**

|**_Abstract ........................................................................................................................................ i_**|
|---|
|**_Abbreviations ............................................................................................................................. iv_**|
|**_1. Introduction ............................................................................................................................. 1_**|
|What is the World Bank Logistics Performance Index (LPI)? ........................................................................ 1|
|Moving towards Big Data.............................................................................................................................. 2|
|Digitization and new opportunities: massive tracking data .......................................................................... 3|
|**_2. Data sources ............................................................................................................................ 4_**|
|Universal Postal Union (Postal data) ............................................................................................................. 4|
|Cargo iQ (Air cargo data) ............................................................................................................................... 7|
|TradeLens (Container tracking data) ............................................................................................................. 9|
|MDST and Marine Traffic (Container ship tracking data)............................................................................ 11|
|**_3. Definition and processing of KPIs ............................................................................................ 12_**|
|From tracking data to country indicators.................................................................................................... 12|
|Definition of the KPI .................................................................................................................................... 13|
|Level of availability and country level aggregation ..................................................................................... 16|
|Data cleaning and consistency .................................................................................................................... 16|
|Potential biases with the data..................................................................................................................... 18|
|Potential for more indicators from the existing data.................................................................................. 19|
|**_4. Conclusions and future directions ........................................................................................... 19_**|
|**_References ................................................................................................................................. 20_**|
|**_Appendix 1: Container tracking data from TradeLens .................................................................. 22_**|
|**_Appendix 2: Postal data from the Universal Postal Union (UPU) .................................................. 30_**|
|**_Appendix 3: Air cargo tracking data from Cargo iQ ..................................................................... 34_**|
|**_Appendix 4: Container ship tracking datasets ............................................................................. 36_**<br>MDS Transmodal ......................................................................................................................................... 36|

ii
|Marine Traffic .............................................................................................................................................. 37|
|---|

# **List of tables**

|Table 1: Events in the UPU database ...................................................................................................... 5|
|---|
|Table 2: KPI derived from tracking data in the LPI 2023 ....................................................................... 14|
|Table 3: Sample container voyage ........................................................................................................ 16|
|Table 4: Rules that consistent events must follow (TradeLens dataset) .............................................. 17|
|Table 5: Number of observations by event type (TradeLens dataset) ................................................. 23|
|Table 6: Data provisioning requirements by role TradeLens event categories .................................... 25|
|Table 7: TradeLens event categories .................................................................................................... 25|
|Table 8: List of timestamps (TradeLens dataset) .................................................................................. 27|
|Table 9: EDI message names and definitions ........................................................................................ 30|
|**List of figures**|
|Figure 1: UPU coverage by region and income group ............................................................................ 6|
|Figure 2: Cargo iQ events sequence........................................................................................................ 7|
|Figure 3: Country coverage of Cargo iQ dataset by WB region and income group ................................ 8|
|Figure 4: TradeLens supply chain framework ....................................................................................... 11|
|Figure 5: TradeLens dataset: Number of observations by week .......................................................... 22|
|Figure 6: Count of consignments by number of events (TradeLens) .................................................... 24|
|Figure 7: High-level network structure of Tradelens dataset ............................................................... 29|
|Figure 8: Share of null values in 2019 parcel postal records of destination countries ......................... 32|
|Figure 9: Network representation of the UPU dataset ......................................................................... 33|
|Figure 10: Sequence of data events (Cargo iQ dataset) ....................................................................... 34|
|Figure 11: Maritime dataset country coverage by WB region and income group ............................... 37|

iii
# **Abbreviations**

|AIS|Automatic Identification System|
|---|---|
|B2B|Business to Business|
|e-AWB|Electronic AirwayBill|
|EMSEVT|Express Mail Service EVenTs|
|IATA|International Air Transport Association|
|IMO|International Maritime Organization|
|KPI|Key performance indicator|
|LPI|Logistics Performance Index|
|MOP|Master OperatingPlan|
|OE|Office of Exchange|
|TE|Transport equipment|
|TEU|Twenty-foot equivalent unit|
|ULCV|Ultra-large carrier vessel|
|UPU|Universal Postal Union|

iv
# **1. Introduction**

<mark>Logistics combines operations and services related to the movement and storage of goods. Logistics services include freight forwarding; third-party logistics; customs brokerage; road transportation; port, airport and rail operations; and warehousing and refrigeration. Logistics is a critical contribution to economic activity within and across borders. Efficient logistics help reduce trade costs and is essential to trade and regional integration. Low international connectivity, inadequate logistics infrastructure, low-quality logistics services, and heavy trade procedures at and behind the border raise logistics costs. Logistics costs amount to 8% of GDP in the United States but rise to 15%-20% in many middleincome countries and up to 30% in low-income countries, landlocked or island states.</mark><sup>2</sup> <mark>Although logistics is mainly a B2B activity, its efficiency depends on policy interventions. Logistics activities expand in space and time, require specific infrastructure (e.g., ports), involve government controls (e.g., customs clearance), and have a large environmental or spatial footprint (e.g., warehouses, multimodal terminals). Logistics performance, a concept introduced by the World Bank (Arvis et al. 2007), is a cross-cutting concern for the public sector and international organizations. It combines interventions in the following areas: infrastructure and provision of infrastructure services including ports (often in public-private partnership) or land corridors, regulations and market incentives, and border management.</mark><sup>3</sup> <mark>Logistics also require specific attention as a contribution to broader concerns such as spatial planning (e.g., logistics zones), skills and training, urban policies (distribution in urban environments), and especially decarbonization (“green logistics”).</mark><sup>4</sup> <mark>The World Bank introduced the Logistics Performance Index (LPI) in 2007 as a set of country indicators to inform policy makers and practitioners.</mark> What is the World Bank Logistics Performance Index (LPI)?

The World Bank’s Logistics Performance Index is a comprehensive index that has been covering the entire supply chain for between 139 and 160 countries in the 2007 to 2023 editions. It is based on a survey of nearly 1,000 logistics professionals worldwide and is useful for comparing performance across countries and identifying and prioritizing broad reform areas for interventions within countries. The index is based on numerical ratings of 1 (weakest) to 5 (strongest).

The Logistics Performance Index is a weighted average of six components:

1. Efficiency of the clearance process

2. Quality of trade- and transport-related infrastructure 3. Ease of arranging competitively priced international shipments 4. Competence and quality of logistics 5. Ability to track and trace consignments

> 2 Banomyong et al. 2022.

> 3 Arvis et al. 2007; World Bank 2010.

> 4 McKinnon 2015.

1
6. Frequency with which shipments reach the consignee within the scheduled or expected delivery time <mark>Respondents rate up to eight countries along those six dimensions. The countries are selected according to the country of operation of the respondent. The country selection engine combines randomness and trade patterns. The LPI tells how easy (perceived) it is to establish efficient and reliable connections between trading countries. Conceptually, the LPI is a revealed supply chain accessibility metric. The LPI expanded on the concept of logistic friendliness introduced by Daley and Murphy [1999]. These authors were measuring through surveys the attractiveness of cities in the United States, as regional hubs of logistics activities.</mark> <mark>The choice of a survey-based instrument was in part dictated by necessity. When the LPI was created, "hard" supply chain data was scarce, and hardly comparable across economies. Initially, the LPI survey covered lead time to trade or in customs for different modes, with focus on the respondent’s country of operation. Yet the dispersion of results for a given country was higher than the perception-based LPI, and global surveys were found inadequate to measure lead time or delays. The LPI survey was discontinued after the 2023 edition to focus on more general attributes of logistics.</mark> <mark>The Logistics Performance Index (LPI) is the primary supply chain benchmark for policymakers and has motivated reforms since 2007. The LPI has been used as a component indicator in several high-level dashboards including the Sustainable Development Goals or those of the World Economic Forum.</mark><sup>5</sup>

# <mark>Moving towards Big Data</mark>

<mark>The perception-based approach had shortcomings. L</mark> PI scores reflect a quantification of qualitative perceptions on an ordinal scale and are thus subject to noise. Sampling is nonrandom since respondents choose whether to participate, possibly leading to selection bias. This creates difficulties when comparing small changes between countries and within countries over time. An additional issue relates to year-on-year comparisons, which suffer from the limitation that respondents grade performance on a qualitative scale that could be affected by indexing issues, meaning that respondents’ standards of what constitutes “high” or “low” efficiency and quality of logistics services can differ. In <mark>each edition, several countries could not be featured in the LPI as too few assessments from logistics service providers based outside the country in question were obtained. Survey fatigue was a growing issue.</mark> <mark>In recent years, supply chain data has changed from scarce to abundant. Digitalization of supply chains expanded swiftly. Shipments are tracked from origin to destination in many industries and across borders. This has been driven by the rapid digitalization of logistics processes (e.g., Internet of Things) and the growing need for traceability, in parallel with the fast pace of automation in public agencies (e.g., port community system, customs automation). Consequently, global datasets are available, albeit mode-specific.</mark> The 2023 edition introduces quantitative key performance indicators (KPIs), based on shipment tracking datasets, measuring the speed of trade around the world. The KPIs are derived from millions of international movements of containers, aviation shipments, or postal parcels. KPIs measure delays at ports, airports or assess international connectivity (e.g., number of international connections). These

> 5 See <u>https://sdgs.un.org/goals</u> and <u>https://www.weforum.org/publications/the-global-enabling-trade-report2016/</u>

2
KPIs, measured in days or simple counts, are relatable to policy makers and practitioners concerned with the performance of key logistics hubs or gateways, such as ports or airports.

# Digitization and new opportunities: massive tracking data

Since the first release of the Logistics Performance Index in 2007, the data ecosystem of supply chains has changed radically. Tracking data is now abundant, and at least for some modes, from globally consistent sources. Much of this change is driven by digitization of supply chains where shipments and assets are located in real time. This knowledge allows the owner of the goods, or its agent, to make timely decisions on purchasing, inventory management, routing or rerouting. Digitization has transformed supply chain management techniques and improves operational efficiency and flexibility and reduces costs across supply chains.<sup>6</sup> Supply chain data interoperability across the supply chain participants is essential to supply chain digitization: Third-party logistics operators should exchange seamlessly with carriers (e.g., shipping lines). Industry initiatives to normalize supply chain data or create shared tracking repositories are critical to the fast adoption of digitization. For instance in the area of container shipping, the Digital Container Shipping Association<sup>7</sup> promotes common standards, while the now discontinued TradeLens was an attempt of a common digital platform.

Digitization of supply chain operations generates massive granular high-frequency datasets. The volume of information on cargo, vessel and vehicle movements in time and space offers new opportunities to analyze supply chains and logistics services provision at the national level and globally.

To construct the new set of indicators for the LPI 2023, the World Bank collaborated with external data providers in the logistics industry. The data comprised high-frequency, micro-level datasets, namely: (i) dwell time and connectivity of containerized trade based on consignment activities from TradeLens (ii) speed of international parcel movements from the Universal Postal Union (UPU); (iii) air cargo tracking from Cargo iQ (IATA); (iv) container lines shipping service deployment information from MDS Transmodal; and (v) turnaround time at port generated from worldwide containership port calls from Automatic Identification System (AIS) data provider Marine Traffic.

The data accessed is pure tracking data: a series of events for a given shipment with a time stamp and a localization (e.g., port or airport code). It does not include the price paid for freight and logistics services, nor the value or nature of the merchandise. The partners’ databases may aggregate waybill or manifest information where monetary information is available. As this information is commercially sensitive, it has been excluded from the partnerships with the World Bank.

The tracking data is exhaustive regarding long-distance international trade (the number of observations is in the double digit millions). It covers three major types of international trade: container trade, air cargo and parcels. Bulk shipping is not included. However, bulk logistics is much less representative of countries’ performance than container trade.

The available tracking data is less representative of cross-border trade between countries in the same tracking block than long-distance trade. Cross-border trade is carried by trucks (semi-trailers) or rail.

> 6 Supply chain digitalization has been reviewed many times, e.g., https://supplychaindigital.com/digital-supplychain/mckinsey-5-point-plan-on-supply-chain-digital-transformation.

> 7 https://dcsa.org/newsroom/streamlining-international-trade

3
Tracking systems for trucks and freight trains exist only at the country or regional level. Lacking a global repository, these modes (road and rail) are not yet included in the new KPIs. However, corridor performance information is available from container tracking data to/from inland destinations.

The rest of the paper is organized as follows. Section 2 summarizes the data sources and presents the indicators. Section 3 presents the production process of the indicators. Appendices describe the datasets, their cleaning methodology and descriptive statistics.

# **2. Data sources**

Universal Postal Union (Postal data) The volume of e-commerce has surged in the past decade. According to McKinsey projections,<sup>8</sup> by 2030, cross-border e-commerce in goods will grow anywhere between $1 trillion and $2 trillion of merchandize value from its current $300 billion, resulting in significant changes in supply chains. The volume of e-commerce activities was equivalent to 30% of global GDP in 2019,<sup>9</sup> so its role in economic development cannot be overlooked.

Information on postal services, including data on postal infrastructure, processing volumes, and speed allows to assess the performance of logistics, customs, and delivery services at the country level. In many countries, postal networks constitute an essential infrastructure and play a key role in guaranteeing access to universal communication services and global value chains. This is particularly valuable for the youth, women, and small businesses in vulnerable, marginalized, and rural areas. According to research conducted by UPU in collaboration with UN Global Pulse, various characteristics of the international postal network are closely related to countries’ economic and social advancement, playing an important role in their trade development.<sup>10</sup> The extent to which international e-commerce is interconnected with postal services was pointed out by a McKinsey report<sup>11</sup> published in March 2022, specifying that the UPU is responsible for handling about two-thirds of all letter-parcel (up to 2 kilograms) deliveries across borders. The Universal Postal Union (UPU) also manages international express shipments, which account for about 5%-10% of international e-commerce volumes, and the rest is delivered by commercial operators. Therefore, information collected by UPU is a source of comprehensive data for over 190 member countries, and probably the best source of information on e-commerce trade.

UPU develops and maintains technical standards and Electronic Data Interchange message specifications used in exchange of electronic information between postal services. UPU’s databases of EDI messages contain detailed information on volumes, frequencies, key cross-border activities and other tracking data on postal items. This information is available via the EMSEVT (Express Mail Service

> 8 https://www.mckinsey.com/industries/travel-logistics-and-infrastructure/our-insights/signed-sealed-and-delivered-unpacking-the-cross-border-parcel-markets-promise

> 9 https://unctad.org/news/global-e-commerce-jumps-267-trillion-covid-19-boosts-online-sales 10 Hristova et al. 2016

> 11 https://www.mckinsey.com/~/media/mckinsey/industries/travel%20logistics%20and%20infrastructure/our%20insights/the%20endgame%20for%20postal%20net-

> works%20how%20to%20win%20in%20the%20age%20of%20e%20commerce/the_endgame_for_postal_networks_how_to_win_in_the_age_of_e-commerce.pdf

4
EVenTs) messaging standard, which is used to track parcels (packages up to 30 kg), letters (letter-post items and packages up to 2 kg), and express mail flows in the UPU network.

_Table 1: Events in the UPU database_

|**Message ID**|**Event Description**|
|---|---|
|**Exporting event**<br>|**s**<br>|
|A|Posting/Collection|
|B|Arrival at outwards office of exchange|
|C|Departure from outward office of exchange|
|**Importing even**|**ts**|
|D|Arrival at inward office of exchange|
|E|Held byImport Customs|
|F|Departure from inward office of exchange|
|G|Arrival at deliveryoffice|
|H|Attempted/Unsuccessful delivery|
|I|Final delivery|
|**Transit events**||
|J|Arrival at transit office of exchange|
|K|Departure from transit office of exchange|

The processing and handling sequence of cross-border shipments is reported in Table 1. For an e- commerce item, after a shopper places an order, the shipper hands the item to the origin post (code A). The post inducts the item into its domestic network where it passes through several handling, sortation, and transport processes (code B). At the origin Office of Exchange (OE), the item is placed in a receptacle for international dispatch to the destination OE, in which it departs from the countryof-origin (code C). After two potential transiting events (coded J-K), the item arrives at the destination (code D), where it is unloaded and handed over to the destination post. The E event denotes the process of separating items from a bundle that they were shipped in, as well as item retrieval and clearance through customs. The destination post inducts it into their domestic network for processing and potential relocation to the delivery office, from which a final delivery to the customer happens (code I).

Alternatively, records of unsuccessful deliveries are recorded using event H. Each of the different handoffs described above is supported by data capture methods such as barcode scanning and computerized entries. The different supply chain partners use this data to generate and exchange the applicable EDI messages in compliance with agreed standards and business rules designed by UPU.

With about 1 billion records per year, EMSEVT provides data for building quality-of-service KPIs that are used by postal operators for assessments of their efficiency. The time difference between A and B means collection time performance, the difference between B and C denotes the processing time at origin, C-D informs about international transit, D-F contains information on the dwell time at destination office of exchange, including customs clearance. The difference between D and H/I informs about performance of postal activities at destination.

For the LPI, the focus was on the availability and performance at destination, making events D, H/I the primary events of interest for assessing the performance of postal services and last mile deliveries. This indicator has also the best country coverage. The KPI published in 2023 only retain parcel, not letter, data.

5
The dataset was constructed for the whole calendar year 2019 and aggregated to the country level by looking at parcels’ time difference between events using statistics such as mean, median and interquartile range for tracking the lead time of parcel movements. The sample comprised countries with more than a hundred inbound unique parcel shipments. The sample included 132 countries from all World Bank geographical regions and income groups (see Figure 1). Forty percent of low-income countries were represented in the postal dataset, while other income categories were represented at between 61 and 68 percent. The geographical coverage was more evenly distributed with all but one region having over half of the countries represented in postal statistics. Only Sub-Saharan Africa had less than 50 percent of its members represented as part of UPU’s information. A detailed overview of this dataset is available in Appendix 2.

_Figure 1: UPU coverage by region and income group_ <!-- Start of picture text --> 100.0%<br>79.3%<br>80.0% 71.4%<br>60.0% 52.6% 53.7%<br>40.0%<br>20.0%<br>0.0%<br>East Asia & Europe & Latin America & Middle East &<br>Pacific Central Asia Caribbean North Africa<br>Percentage of WB countries/region<br><!-- End of picture text --> <!-- Start of picture text --> 71.4%<br>66.7%<br>62.5%<br><!-- End of picture text --> 6
# Cargo iQ (air cargo data)

In addition to the postal flow dataset, the LPI 2023 includes indicators on air cargo logistics. The operational framework of the air cargo industry, including cargo handling by carriers and airports, is rooted in the same EDI protocol as the postal network: the main events of the supply chain follow a similar logical ordering (Figure 2). The dataset was provided by Cargo iQ – a nonprofit and affiliated with the International Air Transport Association (IATA) created in 1997 to develop a system of shipment planning and performance monitoring for air cargo based on common business process and milestone definitions.<sup>12</sup> Cargo iQ’s focuses on the digitalization and exchange of e-AWB (electronic airway bill).

Cargo iQ includes over 60 participants including forwarders, air carriers, ground handling companies, road carriers and airports to work together to define the standards for shared processes and planning to control and to evaluate performance of cargo shipments. Most major carriers are participating in the initiative. Annually, Cargo iQ collects over 110 million data lines, of which 12 million are airportto-airport shipments. These records, covering information for about 650 airports and 184 countries and accounting for 45 percent of global air freight volume, were used to construct the aviation pillar of the LPI 2023.

_Figure 2: Cargo iQ events sequence_ <!-- Start of picture text --> NFD<br>RC<br>DEP DL<br>AR Notification of<br>Cargo<br>Shipment Shipment Received in Readiness for Cargo<br>Departure from Arrival at Warehouse at Delivery of Delivered to<br>Origin / Last Transit / Transit / Cargo to Consignee/age<br>Departure Point Destination Destination) consignee/age nt<br>nt<br><!-- End of picture text --> Cargo iQ’s reporting system captures the path of air cargo shipments defined by a master operating plan (MOP) consisting of 19 milestones.<sup>**13**</sup> These events along the supply chain capture the OriginDestination movement of cargo between and at origin airports, destination airports and transit locations. Figure 2 demonstrates the milestones at the destination airport that were available for the LPI 2023. A shipment is tracked from the point of departure of the flight (DEP) to its arrival (ARR) and checking into a warehouse at a destination airport (RCF), followed by the advisory to the consignee of the freight’s arrival (NFD) and the consignee’s final collection of the freight from the carrier at the destination airport (DLV).

As with the other shipment tracking databases, it is the carrier’s responsibility to enter the data in the system. The time differences between the milestones provide information on the reliability and performance of individual carriers, freighters, operators and on the aggregated level, i.e., individual airports and countries. To avoid leakage of sensitive information for specific carriers operating on lanes with total market share exceeding 80 percent and on those lanes where the total number of carriers is below 3, 46 countries were excluded from the final dataset, bringing the total number of countries reported in the LPI 2023 aviation pillar to 141.

> 12 https://www.cargoiq.org/value-proposition

> 13 https://www.iata.org/en/programs/cargo/cargoiq/

7
The indicators that were extracted from Cargo iQ are based on a pair of milestones: NFD and DLV, i.e., timestamp of the notification of document’s readiness and final handing over of the physical shipment to the freight forwarder. These milestones have the highest compliance levels. The time elapsed between events NFD and DLV was computed for each e-AWB recorded in the system at destination country given the validity of the time difference (both timestamps have to exist and the time difference between them has to be positive). Destination country aggregates presented as part of the aviation pillar of natural unit indicators were constructed from these time differences derived for each e-AWB. Country-level aggregates computed were means, medians, and absolute square deviations.

Figure 3 shows the country coverage of the final subset of the Cargo iQ dataset. As was the case with the survey-based LPI, low-income countries have the least coverage (only a quarter of LICs are available as part of aviation statistics). Geographical coverage is lower overall for Cargo iQ in comparison with UPU. East Asia & Pacific, South Asia and Sub-Saharan Africa all have about 35-40 percent of countries covered. Overall, slightly less than a half of the World Bank member countries have records sourced from Cargo iQ information on aviation supply chains and logistics. For a more detailed overview of Cargo iQ data, refer to Appendix 3.

_Figure 3: Country coverage of Cargo iQ dataset by WB region and income group_ Figure 3 Cargo IQ country coverage <!-- Start of picture text --> 66.7%<br>58.6% 57.1%<br>60%<br>41.5%<br>39.5%<br>37.5%<br>40% 35.4%<br>20%<br>0%<br>East Asia & Pacific Europe & Central Latin America & Middle East & North America South Asia Sub-Saharan Africa<br>Asia Caribbean North Africa<br>80%<br>60.5%<br>60%<br>42.6%<br>38.9%<br>40%<br>25.0%<br>20%<br>0%<br>High income Low income Lower middle income Upper middle income<br><!-- End of picture text --> 8
TradeLens (container tracking data) TradeLens, started in 2018 and discontinued in 2023, was a data and document sharing data platform bringing together shipping lines and other participants in container logistics. The project was a collaboration between IBM and GTD Solution, a division of shipping conglomerate Maersk. TradeLens brought together over 1,000 entities involved in the global supply chain, including over 200 ports and terminals, over 15 custom authorities and, by mid-2022, was facilitating the information exchange of about 65% of containerized trade.

# <u>TradeLens data model</u>

TradeLens used a data model with three related classes: Consignments, Transport Equipment and Shipments. It tracked consignments, transport equipment (containers) and shipments, while managing the identifiers and relationships between them. The TradeLens data structure allowed a consignment to be sequentially in multiple transport equipment (ship, rail, barges, truck), along with other consignments. It also allowed transport equipment to be part of multiple consignments.

The LPI 2023 dataset retrieval for this report was defined by the data provider for a period of 6 months between May 1, 2022, and October 31, 2022. The sample contained timestamps for 11 events for four transport modes (Ocean, Road, Barge, and Rail) and two load statuses (Full or Empty), associated with over 3 million unique tracked consignments (container voyages) and over 30 million observations in total. The dataset covers over 11,000 UN/LOCODEs<sup>14</sup> (including destinations, origins, and live locations (locations of specific event’s timestamps).

# <u>Process framework and definitions of phases</u>

The conceptual framework for processing the data is presented in Figure 4 and with more detail in Appendix 1. The dataset contained a sequence of events and their timestamps for each consignment. On average, there were 9.8 events associated with each consignment. Additional static information on each consignment includes UN/LOCODEs for origin and for destination.

Compared to aviation and postal tracking data, the container data is more complex. Events do not happen in a strict sequential order, as for UPU or Cargo iQ. Shipping transshipments or land corridors at origin and destination increase the number of possible combinations.

The sequence of a container is as follows (possible but not compulsory legs are in italics):

1. An empty container is sent from a depot to the exporter’s premise where it is stuffed (sending from the depot is a registered event, not the stuffing)

_2. The full container arrives at an inland facility_ _3. The container leaves the inland facility (by rail, barge, or by default truck)_ _4. Processes 2-3 are repeated at intermediate facility_ 5. The full container arrives at the port of export 6. The container is loaded on vessel 7. The vessel departs

> 14 <u>https://unece.org/trade/cefact/unlocode-code-list-country-and-territory</u>

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_8. The vessel arrives at transshipment port_ _9. The container is unloaded from vessel_ _10. The container is loaded on vessel_ _11. The vessel departs the transshipment port_ _12. Processes 8-11 are repeated at other transshipment port_ 13. The vessels arrives at destination port 14. The container leaves the destination port _15. The container arrives at an inland facility_ _16. The container leaves the facility_ _17. Processes 15-16 are repeated at other inland facility_ 18. The empty container arrives back at a depot<sup>15</sup> For each consignment, the raw data was partitioned into phases of the supply chain processes: export, transshipment, and import. Hence the export phase included events numbered 1 through 7 in the sequence above, transshipment phase encompassed events 8 through 12 (and as many of the repeated transshipment port visits as was documented), and the import phase covered stages 13 until the last record existing for the specified consignment.

The export phase was identified as a collection of events on a consignment level that are preceding the first occurrence of loaded on vessel or vessel’s departure at location of origin (country level). Usually, these include Gate-out (Empty) and Gate-in (Full) events with the latter event’s location being close to the vessel’s departure and loading locations. This phase was the source of LPI KPIs such as export dwell time and export corridor time (defined in more detail in the next section).

With the same principle in mind, the import phase corresponds to all events occurring after the last occurrence of Vessels Arrival or Discharge from vessel at location of destination (country level). Events in this category include Gate out (Full), Gate in (Empty) (return of an empty container), contributing to indicators such as import dwell time and import corridor time.

The final phase is associated with transshipments and is defined as a collection of events occurring between import and export phases (between first occurrence of Loaded on Vessel/Departure and last occurrence of Arrival/Discharge from vessel) where the active UN/LOCODE is different from the UN/LOCODE of origin and destination. All three phases include timestamps for Rail and Barge legs of the voyage in some cases. Using the definitions above, a collection of natural unit indicators was constructed for each phase of the process using timestamps of the events to compute time differences for segments of the consignment voyage. Once the time differences for specific segments were derived, the aggregation for UN/LOCODEs and countries was used to produce the KPI. More details on

> 15 Different depot than the one from leg 1.

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<!-- Start of picture text --> Location # Origin or Destination C /<br>ALo'-—O °Gate*Gate outiin (Full) (Empty) *Arrival/DischargeTranshipmentphase °Arrival/Dischargeoe ou O O<br>eLoaded on eLoaded/Departure eA alot<br>vessel/Departure<br>——1") Export phase Import phase rom)<br>Gate in Full Gate out Full<br>Country/Location. of first. Full event Country/Locationv/ of last Full event<br>= Destination<br>= Origin<br><!-- End of picture text -->
and on ports and ship datasets from open-source datasets and a database made available through the International Maritime Organization (IMO). Fields available in the port calls dataset include:

- Type of the event: arrival or departure

- UN/LOCODE 7 Port Name

- <mark>Date / Time in UTC</mark>

- Vessel particulars:

- <mark>IMO number</mark>

- <mark>MMSI (Maritime Mobile service Identity) number</mark>

- <mark>Vessel name</mark>

- <mark>Vessel type</mark>

- <mark>Draught at the time of the recording</mark>

- Length, width, and max draught

The dataset was prepared using MarineTraffic port call data covering over 5,000 container ships calling at over 800 ports during the first two quarters of 2022. Based on estimated time differences between recorded arrivals and departures to port facilities, an indicator of turnaround time per port was constructed. In addition, ship data, including capacity in twenty-foot equivalent units (TEU) is available. Ship types range from small feeders with capacity of up to 1,000 TEU to ultra large container vessels with capacity starting at 14,501 TEU. Additional details regarding this data source are in Appendix 4.

# **3. Definition and processing of KPIs**

From tracking data to country indicators The micro-logistics datasets used for the LPI 2023 consist of large numbers of voyages of individual shipments (e.g., container or parcels). For shipping data, they contain the port call sequence. The raw tracking data consists of the following fields:

- ID of the voyage

- Origin, destination

- Coded location of the event: tracking data localize the event by port, airport, or more generally UN/LOCODE.

- Time of the event

- Type of the event (structured categories, specific to the dataset): e.g., container loaded on ship, parcels arriving at the bureau of destination, port arrival etc.

The individual voyage statistics are sliced to create lead time between two consecutive events either at the same location (e.g., container stay or dwell time at a port) or at two different locations. The data is then combined across voyages going through this location (if the consecutive events are at the same place), or through a pair of locations (if the consecutive locations are different). The data is processed according to a growing level of aggregation. First the information is aggregated by trade lane: Statistics of lead time of a shipment or cargo unit from origin to destination or key logistics steps, for instance dwell time at gateways (ports, airports, postal bureau) at origin or destination. The indicators in the LPI 2023 have been decided with the data partners depending on the ease of interpretation for final users.

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The survey-based LPI captures the ease of establishing reliable and effective supply chain connections to export target countries. The survey does this by pooling the information from the six LPI components, as assessed by logistics and freight forwarding professionals. LPI respondents rate countries according to their supply chain experience when providing their macro-level qualitative knowledge of them. Micro-level tracking data, however, informs about the performance outcomes along the LPI dimensions except for affordability, as cost information is not available in this data. Attributes of supply chain performance accessible from the data include two main categories.

The first category comprises indicators of timeliness and reliability. Domestic and international supply chains have a skewed lead time distribution with a long tail. Therefore, the significance of lead time at a port or along a corridor is not captured just but its average but by how likely much longer delays are. The shape of the right side of the distribution, i.e., the length of the “tail”, reflects reliability. Corresponding KPIs include:

- Mean/Median lead time for legs of the supply chain (between two subsequent events), with focus on time at and within border.

- Indicators of reliability as statistical dispersion such as interquartile range defined as the difference between the 75<sup>th</sup> and 25<sup>th</sup> percentile.

The second category includes indicators of connectivity. The structure of the network matters. More connections mean more options to access markets, also contributing to reliability and performance. This is like human mobility where more options to reach destinations increase accessibility of individuals to reach jobs or services. Connectivity indicators are provided by mode: container shipping, aviation, postal. They mostly consist of simple counts of connections in the network.

# Definition of the KPI

The set of indicators below focuses on (i) delays experienced at the same place or dwell time at ports or airports, (ii) delays in motion, and (iii) connectivity information. From a policy standpoint, dwell time is an important KPI. Time spent at hubs and gateways can be improved by productivity investments, fluidization of information, or simplification of processes. Furthermore, dwell time at these hubs and gateways has considerably more dispersion than in international freight transport. While planes and ships have consistent travel time (at least under normal circumstances), lead time at key facilities or to some extent land corridors is affected by a high degree of dispersion that impacts the logistics of the consignee at destination. The KPI produced for the report are in Table 2.

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_Table 2: KPI derived from tracking data in the LPI 2023_

|**Data provider**|**KPI name**|**Definition and significance**|**Period**|**Unit**|**Why it matters**|
|---|---|---|---|---|---|
|Cargo iQ|Number of part-<br>ners|Count of distinct origin country partners per destina-<br>tion country|Q1-2 2022|Number of<br>countries|Air cargo connectivity metric|
|Cargo iQ|Aviation dwell<br>time|Time difference between event NFD (Notification of<br>Readiness for Delivery of Cargo) and DLV (Cargo Deliv-<br>ered to Consignee) at destination country<br>Median and quartiles are provided|<br>Q1-2 2022|days|Efficiency of handling and clearance<br>and notification to consignee|
|UPU|Number of part-<br>ners|Count of distinct number of country partners|2019|Number of<br>countries|Postal connectivity|
|UPU|Postal delivery<br>time|Median time difference between event EMD (Arrival<br>at inwards office of exchange) and events EMH (Un-<br>successful delivery) or EMI (Final delivery) at destina-<br>tion country<br>Median andquartiles areprovided|2019|Days|Efficiency of clearance and postal lo-<br>gistics at destination|
|MDST|Number of ser-<br>vices|Total number of maritime services (operated through<br>liner shipping companies on a predefined rotation) be<br>tween the two countries|-<br>Q1-2 2022|Number of ser-<br>vices|Availability of services and fre-<br>quency of connections|
|MDST|Number of alli-<br>ances|Count of number of alliances per destination country|Q1-2 2022|Number of alli-<br>ances|Competition between services|
|MDST|Number of part-<br>ners|Count of distinct number of country partners per<br>country|Q1-2 2022|Number of<br>countries|Shipping connectivity metric|
|MarineTraffic|Turnaround<br>time|Time difference between first instance of arrival and<br>last instance of departure for consecutive repeated|Q1-2 2022|Days|Proxy of the performance of the<br>ship to shore interface (inc. handling<br>by terminal operator)|

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|||port visits (if any) calculated for each port call. Aggre-<br>gated directly from port call time differences to coun-<br>tries over a 6 months’ time period (2022-01 - 2022-06|)||
|---|---|---|---|---|
|TradeLens|Import dwell<br>time (port of<br>entry)|Time spent at the same location defined as UN/LO-<br>CODE before delivery to the client. Dwell time at port<br>of entry. Mean, median and quartiles are provided|May 1 to Octo-<br>ber 31, 2022<br>Days|Critical KPI resulting from several<br>factors including goods clearance,<br>removal and land services and to<br>some extent terminal and multi-<br>modalperformance|
|TradeLens|Import dwell<br>time (consoli-<br>dated)|Time spent at the same location defined as UN/LO-<br>CODE before delivery to the client. Dwell times at port<br>of entry and intermediate inland locations are com-<br>bined and statistically aggregated for the same coun-<br>try of destination. Mean, median and quartiles are<br>provided|<br>May 1 to Octo-<br>ber 31, 2022<br>Days|Critical KPI resulting from several<br>factors including goods clearance,<br>removal and land services and to<br>some extent terminal and multi-<br>modal performance|
|TradeLens|export dwell<br>time (port of<br>departure)|Time spent at the same location defined as UN/LO-<br>CODE since expedition and before ship loading. Dwell<br>time at port of departure. Mean, median and quartiles<br>areprovided|<br>May 1 to Octo-<br>ber 31, 2022<br>Days|Same as import dwell time but more<br>representative of domestic logistics|
|TradeLens|export dwell<br>time (consoli-<br>dated)|Time spent at the same location defined as UN/LO-<br>CODE since expedition and before ship loading. Dwell<br>times at port of departure and intermediate inland lo-<br>cations are combined and statistically aggregated for<br>the same country of origin. Mean, median and quar-<br>tiles areprovided|May 1 to Octo-<br>ber 31, 2022<br>Days|Same as import dwell time but more<br>representative of domestic logistics|
|TradeLens|Corridor import<br>and export lead<br>time|<br> <br>Time spent in motion (or idle or not) between loca-<br>tions. This time is combines across legs and aggre-<br>gated by country of export or country of import.<br>Mean,median andquartiles areprovided.|May 1 to Octo-<br>ber 31, 2022<br>Days|Representative of road or rail corri-<br>dor performance excluding multi-<br>modal transfers en route which are<br>included in dwell time.|

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# Level of availability and country level aggregation

The KPIs are available at the level of location or pair of locations (typically a UN/LOCODE). For the official report, the data was further aggregated at country level by taking average, medians and other statistics of the natural unit indicators generated for all locations in the country (or pair of countries for corridor data). Portspecific data is available for just under 500 ports and can be made available for academic purposes upon request. Consignment-specific data cannot be made publicly available due to confidentiality concerns and data sharing agreements with partnering organizations.

For landlocked countries, the observations serving to estimate the dwell time at port of entry are separate from the observations related to shipments going to the country of transit. Some countries classified as landlocked developing countries in our report are not considered to be such in a global, less strict definition. For example, Azerbaijan, a country that formally has access to the Caspian sea and operates its largest port (Baku), was classified as a landlocked country. This data feature stems from the nature of our datasets: all of the maritime-related data solely focused on container shipping. Our indicators are thus relevant to ports and terminals that are able to handle containerships and ignore dry bulk, liquid bulk, LNG/oil, and Ro/Ro terminals. In the future, we plan to include additional KPIs that include the performance of port facilities that handle dry bulk operations.

# Data cleaning and consistency

Tracking datasets are not perfect. Events may be missing or duplicated in the sequence of individual consignments. Sequences may be truncated. Table 3 includes the sequence for a consignment going from Odessa in Ukraine to India in transit to Nepal. In between, the container is transshipped in Istanbul, Salalah, and Colombo. However, the transshipment in Istanbul includes duplications (highlighted in grey). To account for this type of issue, cleaning procedures are implemented prior to slicing the data to produce indicators. Anomalous events are eliminated based on rules that “normal” events should logically follow (Table 4). This ensures that the slicing isolates consecutive event pairs.

_Table 3: Sample container voyage_

|event|location|date|FULL_ST<br>ATUS|
|---|---|---|---|
|Actualgate out|UAYUZ|2021-10-29-12.59.00.000000|Empty|
|Actualgate in|UAYUZ|2021-10-30-14.14.00.000000|Full|
|Actual loaded on vessel|UAYUZ|2021-10-31-21.59.00.000000|Full|
|Actual vessel departure|UAYUZ|2021-11-01-03.49.00.000000|Full|
|Actual vessel arrival|TRIST|2021-11-07-16.30.00.000000|Full|
|Actual discharge from vessel|TRIST|2021-11-07-23.42.00.000000|Full|
|Actual discharge from vessel|TRAMR|2021-11-07-23.42.07.000000|Full|
|Actual loaded on vessel|TRIST|2021-11-17-03.51.00.000000|Full|
|Actual loaded on vessel|TRAMR|2021-11-17-03.51.46.000000|Full|
|Actual vessel departure|TRIST|2021-11-17-08.15.00.000000|Full|
|Actual vessel arrival|OMSLL|2021-11-27-04.18.00.000000|Full|
|Actual discharge from vessel|OMSLL|2021-11-27-14.15.00.000000|Full|
|Actual loaded on vessel|OMSLL|2021-12-01-21.25.17.000000|Full|
|Actual vessel departure|OMSLL|2021-12-02-00.16.00.000000|Full|
|Actual vessel arrival|LKCMB|2021-12-05-07.38.00.000000|Full|

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|Actual discharge from vessel|LKCMB|2021-12-05-21.09.51.000000|Full|
|---|---|---|---|
|Actual loaded on vessel|LKCMB|2021-12-09-03.26.28.000000|Full|
|Actual vessel departure|LKCMB|2021-12-09-07.18.00.000000|Full|
|Actual vessel arrival|INVTZ|2021-12-15-12.09.00.000000|Full|
|Actual discharge from vessel|INVTZ|2021-12-16-05.28.00.000000|Full|
|Actualgate out|INVTZ|2021-12-22-06.11.00.000000|Full|
|Actualgate in|NPBRG|2022-02-03-09.15.00.000000|Full|
|Actualgate out|NPBRG|2022-02-17-18.16.00.000000|Full|
|Actualgate in|NPBRG|2022-02-18-09.52.00.000000|Empty|

_Table 4: Rules that consistent events must follow (TradeLens dataset)_

|**Prior event**|**Prior location**|**Event**|**Next location**|**Posterior event**|
|---|---|---|---|---|
|Vessel arrival|same|'Actual discharge from vessel'|same|Gate out<br>Rail departure<br>Loaded on vessels<br>Barge departure|
|Gate in<br>Rail arrival<br>Discharge from vessel<br>Barge arrival|same|'Actual loaded on vessel'|same|Vessel departure|
|Vessel departure|previous|'Actual vessel arrival'|same|Discharge from vessel|
|Loaded on vessel|same|'Actual vessel departure'|next|Vessel arrival|
|Gate out|previous|'Actual gate in'|same|Gate out<br>Rail departure<br>Loaded on vessel|
|Gate in<br>Discharge from vessel<br>Rail arrival|same|'Actual gate out'|next|Gate in|
|Rail departure|previous|'Actual rail arrival'|same|Gate out<br>Loaded on vessel<br>Barge departure|
|Gate in<br>Discharge from vessel|same|'Actual rail departure'|next|Rail arrival|

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<!-- Start of picture text --> Barge arrival<br>Gate in same 'Actual barge departure' next Barge arrival<br>Discharge from vessel<br>Rail arrival<br>Barge Departure previous 'Actual barge arrival' next Gate out<br>Loaded on vessel<br>Rail departure<br><!-- End of picture text -->

# Potential biases with the data

Despite the data being massive and provided by reputable industry sources, the data used in this analysis may imperfectly reflect the reality on the ground. Potential issues include:

- Although there are rigorous procedures to input the time stamps, the process is not fully automatized in some countries and may depend on practices by local operators, more so for aviation and postal data than for shipping. The very few countries for which there is a strong suspicion this is the case have not been included, as per the recommendation of the data partners (see appendixes by mode on the cleaning process). The published KPI only use data that the data partners consider reliable.

- The shipping data by TradeLens is a very large sample of movement of containers (in the double digit millions). There may be a selection bias in that more efficient operators tend to use advance digital tracking solutions. There is a also a possibility of bias due to some shipping line being more present in the sample than others. However, expert opinion is that it is very unlikely. Furthermore, expert reading of the dwell time data across ports suggests that the dwell time data is consistent with local knowledge when available.

- In some cases, the end of the import process registered in the data may not be the actual delivery to the importer. For instance, small developing economies including islands states may not move containers inland but rather unstuff them and store the goods in bonded facilities. The current system does not detect these old-fashioned logistics practices, which are progressively disappearing.

- Representativeness of transport corridor data for landlocked countries: For rich landlocked countries in Europe, moving containers inland is not representative of their international supply chain. In Europe, goods are cleared mostly at entry into the EU. Thus lead time data from container tracking is not representative of the cross-border processes of these countries and may exaggerate the actual delays. For instance, trucking of imports to landlocked countries in Europe is not directly measured given direct trucking and clearance at the port of entry. In developing landlocked countries, trucks (or rail) are going to inland ICDs,<sup>16</sup> thus the data is representative.

> 16 Except for some landlocked developing economies, such as the West Bank and Gaza, or SACU countries, where goods are cleared at the port of entry.

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- 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.

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