GCRI · Trade and Strategic Resources
Supplier concentration
An economy or firm that depends on one or two suppliers for an essential input is exposed to any shock those suppliers face. Concentration can be at the level of countries, firms or facilities, and it is often hidden several tiers up a supply chain. This page sets out how concentration arises, how it is measured from trade and firm data, and what evidence serves the governments and firms working to diversify.
Supplier concentration on the record.
Supplier concentration: how the risk develops.
Single points of failure in production. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Efficiency favours concentration
Scale economies, specialised know-how and low prices draw demand to the most efficient suppliers. Concentration grows from individually rational sourcing choices.
Dependence hides in deeper tiers
A buyer may have many direct suppliers who all depend on the same subcomponent maker. Firms often lack visibility beyond their first tier.
Few substitutes raise exposure
Concentration matters most when an input cannot be replaced quickly by another product or supplier, because of qualification, certification or technical lock-in.
Shocks propagate along dependence
When a concentrated supplier stops, buyers across industries and countries are hit at once, and competition for remaining supply raises prices.
Activity and hazards in the Strait of Malacca.
Night lights, population density and land cover 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
- Source
- GHRSST MUR25 (NASA JPL PO.DAAC)
- Provider
- NASA JPL PO.DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite analysis
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite analysis of how far sea temperature departs from its usual value for the date shown; a marine heat indicator, not a coral bleaching alert.
- 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 EVI (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 vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- 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
- GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- 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
- Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 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
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
Concentration is the hidden common mode across domains.
- HealthCouplingActive pharmaceutical ingredients come from few sites.What to watchSites per essential ingredient
- EnergyCouplingSolar and battery supply chains are concentrated.What to watchConcentration index by stage
- FoodCouplingSeeds, agrochemicals and fertiliser come from few suppliers.What to watchSupplier shares
Concentration measures are designed for the Strategic Systems series. Indicators such as the Herfindahl-Hirschman index are computed from UN Comtrade and OECD trade in value added data, with mirror-data differences reported; series would carry identifiers under the proposed Nexus Standards.
- Supplier mapping below the first tier, under terms.
- Concentration indicators with methods.
Supplier tier map.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Supplier tier map
Supplier concentration · Assembly cluster · Bill of materials BOM-089
Tier mapping
- Tier 118 suppliers
- Tier 264
- Tier 3163
- Raw materials9
Concentration
| Rare earth magnet | 76 | 5617 | None qualified | High |
| Gallium | 77 | 6123 | Recycling pilot | High |
| Copper wire | 25 | 1066 | Two qualified | Low |
| Lithium cells | 57 | 2908 | One qualified | Medium |
Mitigation
- Dual source
- qualification 13 months
- Buffer stock
- 8 weeks
- Design change
- substitution study
- Owner
- Procurement lead
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 |
|---|---|---|---|---|
| Import concentration by product | Herfindahl-Hirschman index of supplier countries | annual | Bilateral trade data | ESTIMATED |
| Products with a dominant supplier | count of products with one supplier above a stated share | annual | Trade data analysis | ESTIMATED |
| Upstream dependence through value chains | share of foreign value added by source | per release | Inter-country input-output tables | ESTIMATED |
| Firms with mapped tier-two suppliers | percent of surveyed firms | per survey | Business surveys | REPORTED |
| Substitutability of input | elasticity or qualitative class | per assessment | Engineering and economic studies | ESTIMATED |
| Strategic stocks held | days of consumption | per assessment | Government and firm records | DECLARED |
Model supplier concentration 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 |
|---|---|---|
| BACI international trade database (CEPII) | Harmonised bilateral trade flows at product level | Open, Etalab open licence |
| OECD Trade in Value Added (TiVA) and ICIO tables | Value added and dependencies along global value chains | Public, OECD terms |
| UN Comtrade | Bilateral trade by product | Free access with limits, UN terms |
| European Commission strategic dependencies analyses | Methods and results for identifying dependencies | Public, EU reuse policy |
| Business supply chain surveys | Firm visibility and diversification practice | Licence per survey |
How supplier concentration 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 supplier concentration: 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 economy and industry | Which dependencies are strategic and how to reduce them | Dependency screens; substitutability evidence |
| Procurement leads in critical sectors | Which inputs to dual-source or stockpile | Concentration and stock evidence |
| Trade agencies | Which partnerships could diversify supply | Diversification option studies |
Outputs this topic produces
- Product-level dependency screens from trade data
- Value chain dependency analyses using input-output tables
- Diversification option studies
- Methods notes on measuring concentration
What experts ask about concentration.
- How concentrated is supply at each stage of production, not only at the final stage?
- Are apparently diverse suppliers in fact dependent on a common upstream source?
- How substitutable is the input, and how long does qualifying a new supplier take?
- What buffers, such as inventory or spare capacity, exist downstream?
- How does concentration change over time with consolidation and policy?
Why concentration creates hidden risk.
Economies of scale, specialised know-how and network effects push many industries towards concentration. A few suppliers can serve the market at lower cost than many small ones. The risk lies in the tails: a fire, flood, cyber incident, insolvency or policy decision at one supplier can affect a whole industry.
Concentration is often hidden. A manufacturer may buy from several suppliers who all depend on the same upstream producer of a chemical, wafer or part. Network science describes this as a common-cause failure, and it is invisible without mapping beyond the first tier.
Concentration interacts with lean practices. Low inventories and just-in-time delivery reduce cost but leave little time to find alternatives when a key supplier fails.
Methods and their limits.
- 01
Concentration indices
Measures such as market-share indices by product and country. They depend on product definitions and may miss ownership links.
- 02
Multi-tier mapping
Tracing suppliers of suppliers. Firms often do not know or will not share their deeper tiers.
- 03
Network centrality
Identifying nodes whose removal fragments the network, using network science.
- 04
Optimisation of buffers
Modelling the trade-off between inventory cost and disruption risk with operations research.
Public data mostly show concentration by country, not by firm or site, so the real degree of concentration is often understated.
How it is studied, and what the methods cannot say.
- 01Network science and contagionTrade network centrality · Multi-tier dependency mapping · Disruption propagationApplied here
- 02EconometricsPrice transmission · Effects of trade measures · Disruption impacts on outputApplied here
- 03Optimisation and operations researchRerouting under chokepoint closure · Strategic stock sizing · Sourcing portfolio designApplied here
- 04Geospatial information systemsFacility hazard overlays · Sourcing region deforestation · Corridor mappingApplied 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 the Herfindahl-Hirschman index?
Why are deeper tiers important?
Is concentration always bad?
Is concentration always a problem?
Does the platform name concentrated suppliers?
Why look beyond the first tier?
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