GCRI · Trade and Strategic Resources
Logistics and cold chain
Logistics move goods from producers to users through containers, warehouses, trucks, rail and air freight; the cold chain does the same for food, vaccines and medicines that must stay within a temperature range. Both depend on energy, equipment and labour, and both fail quietly before shortages show. This page sets out how logistics and cold chains work and break, how capacity and integrity are measured, and what evidence serves the operators and agencies responsible.
How the work runs.
Corridor and chokepoint monitor
Logistics and cold chain · Corridor A · Week 28
- Transit time25days6Alert
- Freight index14538Watch
- Vessels rerouted21this weekWatch
- Inventory cover30daysNormal
Corridors
red: disrupted
Time to recover vs time to survive
by critical input
| Fertiliser | Strait | 7 | 6 | Short |
| Fuel | Strait | 4 | 6 | Tight |
| Semiconductors | Port hub | 15 | 8 | Short |
| Grain | Corridor A | 5 | 11 | Covered |
Fig.Corridor and chokepoint monitor: example names and illustrative values, not a record or a live system.
Logistics and cold chain: how the risk develops.
Transport time and disruption. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Capacity is tightly balanced
Containers, vessels, trucks and warehouse space are run near full use to keep costs low. When demand jumps or capacity is lost, delays and costs rise sharply.
Cold chains need constant power
Refrigerated stores and transport depend on electricity and fuel. Outages and equipment failures expose products to temperatures that reduce quality or make them unsafe.
Labour is a critical link
Drivers, port workers and warehouse staff are often in short supply. Illness, strikes and border rules can stop flows as surely as damaged infrastructure.
Losses show up as shortages
Spoiled food, lost vaccine doses and expired medicines are counted late, if at all. The first visible sign is often a shortage at the point of use.
Activity and hazards in the Strait of Malacca.
Night lights, population density and crop greenness over the Strait of Malacca, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Activity and hazards, for this page
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra NDVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite greenness index over the 8 days to the date shown; an indicator of crop and pasture condition, not a yield estimate or a food security warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Activity and hazards, framed on the Strait of Malacca.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (7 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
Logistics and cold chains keep food and medicine moving.
- FoodCouplingPerishables need unbroken cold chains.What to watchTemperature excursions in transit
- HealthCouplingVaccines and biologics share cold logistics.What to watchCold chain capacity for health products
- EnergyCouplingRefrigerated warehouses depend on reliable power.What to watchBackup at cold stores
Logistics findings are designed for the Global Sector Risk and Resilience series. UNEP and FAO’s report on sustainable food cold chains (2022) sets out capacity, energy and refrigerant issues.
- Cold chain performance data.
- Disruption records by node.
A shock through an input-output economy.
A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Strait of Malacca; the model itself is not tied to a place.
Controls
- Sector hit first (position in the list)
- 0
- Size of the shock
- 30% of its output
- Duration of the shock
- 30 days
- Recovery rate
- 0.08 per day
- Days simulated
- 180 days
Readouts
- Output lost over the run
- 0.52% of a year’s output
- Total over direct (multiplier)
- 1.59 times
- Most affected other sector
- Trade
- Its peak inoperability
- 4.1%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Agriculture | 30.28 | 11.7 |
| Energy | 0.27 | 9.8 |
| Manufacturing | 0.68 | 23.5 |
| Transport and ports | 0.38 | 24.3 |
| Trade | 4.08 | 18.4 |
| Services | 2.38 | 12.2 |
Methods
- Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
- Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
- Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Seaports Natural Earth
- Maritime chokepoints Nexus (named straits and canals at their published coordinates)
- Railways Natural Earth
- Major roads Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Trade and Strategic Resources in Foundry
- Tested at Trade and Strategic Resources in Universe
- Codified in Industry, Trade and Supply Chains in Standards
- Published in Trade and Strategic Resources in Reports
- Taught in Faculty of Finance and Economics in Academy
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Container dwell time at port | days | weekly | Port and terminal data | OBSERVED |
| Warehouse vacancy | percent of space | quarterly | Property market reports | REPORTED |
| Refrigerated storage capacity | cubic metres per thousand population | per assessment | Industry and government surveys | ESTIMATED |
| Temperature excursions in vaccine cold chain | count per period | monthly | Temperature monitoring devices | OBSERVED |
| Food loss in supply chain | percent of production lost before retail | per assessment | SDG indicator 12.3.1(a) Food Loss Index | ESTIMATED |
| Truck driver vacancies | count | annual | Labour market statistics | REPORTED |
Model logistics and cold chain as a system. Run the cascade.
Assembled from the Nexus Standards area profile Supply chain and trade risk: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
Systems and couplings
- Supply chain and trade risk
- Food security and nutrition risk Cascade
- Oceans and coasts risk standards Exposure
- Geopolitical and security risk Common driver
- Cyber and digital risk standards Cascade
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-X4Trade, transport and supply chains riskNRS-H89Hazard family: Transport network disruption (NHS-1 infra.mobility)NRS-K38Domain pack profile: Supply chain trade (DOMAIN-038)NRS-H43Hazard family: Livestock and aquaculture epizootics (NHS-1 health.animal-health)NRS-M10Method fabric: Markets, trade and supply chains
Observation
- IMF PortWatch port activity and chokepoint disruptions (listed for reference)
- Global Supply Chain Pressure Index (listed for reference)
- OECD Data Explorer (SDMX API) (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Developing countries’ and least developed countries’ share of global exports
- Passenger and freight volumes, by mode of transport
Example geography
- The Strait of Malacca: 227 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| FAO Food Loss and Waste database | Food loss estimates by commodity and stage | Open, FAO terms |
| WHO and UNICEF Effective Vaccine Management assessments | Vaccine supply chain performance in countries | Public summaries, terms per agency |
| IMF PortWatch and UNCTADstat | Port activity and maritime statistics | Public, terms to check |
| International Institute of Refrigeration publications | Cold chain capacity and practice | Licence per publication |
| National transport and logistics statistics | Freight volumes, capacity and labour | Public records, licence per government |
How logistics and cold chain is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in logistics and cold chain: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Trade and Strategic Resources prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Ministries of health and immunisation programmes | Where cold chain capacity and monitoring must improve | Capacity maps; temperature excursion data |
| Ministries of agriculture and trade | Where storage and logistics investment reduces food loss | Food loss and capacity evidence |
| Logistics operators | Where to add buffer capacity and backup power | Bottleneck analyses; power dependency reviews |
Outputs this topic produces
- Cold chain capacity maps against food and health needs
- Logistics bottleneck analyses from port and freight data
- Power dependency reviews for cold stores
- Evidence notes for supply continuity planning
What experts ask about logistics.
- How much spare capacity exists in ports, fleets, warehouses and labour?
- How long can goods wait before they lose value or become unsafe?
- What happens to cold storage and transport when power fails?
- How dependent is the system on digital platforms and data exchange?
- Where do last-mile gaps leave communities without reliable supply?
Why time and temperature matter.
Logistics networks are designed for efficiency, which often means high utilisation and little slack. When demand surges or capacity is lost, queues build quickly: ships wait at anchor, containers pile up and delivery times stretch. Recovery takes time because equipment and crews must be repositioned.
Perishable goods add a hard constraint. Fresh food, vaccines and many medicines must be kept within temperature ranges throughout the journey. A break in the cold chain, from a power outage, a delayed truck or a failed refrigeration unit, can spoil the product. Losses in the cold chain are a significant source of food waste in many regions.
Logistics is increasingly digital. Booking, customs, tracking and payment systems are interconnected, so a cyber incident or system outage at a major operator can halt physical flows.
Methods and their limits.
- 01
Operations research models
Queueing, routing and inventory models using optimisation and operations research. They rely on assumptions about behaviour under stress.
- 02
Transport engineering
Assessment of terminal, road and rail capacity using transport engineering.
- 03
Temperature monitoring data
Sensor records from cold-chain shipments. Data are proprietary and not standardised.
- 04
Disruption case studies
Structured analysis of past events. Each event is different, which limits generalisation.
Real operating data are mostly held by private operators, so public analysis often relies on proxies and case studies.
How it is studied, and what the methods cannot say.
- 01Optimisation and operations researchRerouting under chokepoint closure · Strategic stock sizing · Sourcing portfolio designApplied here
- 02Urban and transport engineeringPort capacity and congestion · Freight corridor resilience · Hinterland connectionsApplied here
- 03Geospatial information systemsFacility hazard overlays · Sourcing region deforestation · Corridor mappingApplied here
- 04Network science and contagionTrade network centrality · Multi-tier dependency mapping · Disruption propagationApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is a cold chain?
What is a temperature excursion?
Why do logistics problems appear suddenly?
Does the platform cover humanitarian logistics?
Why is the cold chain singled out?
Can operators contribute data?
Contribute evidence from this page.
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