Map the basket
Six essential UK food products are mapped through supply chain nodes and risk channels.
Build Your Basket
Build a basket. See how pressure moved.
Choose the foods in a basket, compare the best available observed series over time, and see which supply chain pressure channels may explain the movement. This is an evidence based pressure view, not an exact supermarket receipt.
This is not a live supermarket bill. Eggs and milk use upstream producer or farmgate prices.
Bread, chicken, potatoes and pasta may use CPI category indices or retail observations where
available. Pressure channels are directional explanations, not causal proof.
Basket products
litres
loaves
kg
kg
kg
Quantities scale the upstream value for eggs and milk. Category index proxy products contribute directional movement only.
Estimated basket movement
—
The default basket loads automatically using the best available observed series.
What moved in this basket
Estimating basket movement…
Pressure channels across the basket
Directional explanations from the supply chain model and shock registry. Channel coverage shows how many basket products each channel touches.
Build a basket to read a text summary of the movement, coverage, and pressure channels.
How the basket estimate works
Each product uses its best available observed series over a window anchored on the latest observation. Eggs (pence per dozen, Defra producer price) and milk (pence per litre, Defra farmgate price) produce quantity-scaled upstream GBP subtotals. Bread, chicken, potatoes, and pasta use ONS CPI category indices as directional proxy movement; no GBP value is invented for them. A pack of eggs is treated as 12 eggs. When the basket mixes price-level and index products, the headline movement is the unweighted average of each product's own movement and no basket GBP total is shown.
Calculation modes: price_level, mixed_price_and_index, index_only, synthetic_fixture_only (explicitly labelled demonstration data), and insufficient_data. Pressure channels map each product to shocks in the platform registry (for example grain_export_disruption, energy_price_spike, avian_flu, fertiliser_price_shock) through the supply chain graphs.
Read-only endpoints: /basket_cost/products,
/basket_cost/default, /basket_cost/estimate (POST),
and /basket_cost/summary. To light up real data, import the ONS
MM23 dataset with python -m gcmrp.jobs.import_ons_cpi path/to/mm23.csv
and Defra files with python -m gcmrp.jobs.import_product_prices.
Risk constellation
Six essential UK food products. Verified rings show official evidence. Select a product to inspect.
Select a product in the constellation to see its exposure channels and evidence status.
Loading basket overview…
Product selector
Click a node to understand how it may connect to basket movement.
Select a product to trace its supply chain pressure.
Pick a product on the left, or start from a basket card in the Food Basket Overview tab.
Choose a scenario below, or use the dropdown to model a custom shock.
Shock Comparison
Estimated price pressure across basket products from this shock.
Select this tab to load shared upstream exposure data.
Shock event timeline
Select an event to replay the model signal and compare with official evidence.
2022
Ukraine grain disruption
Model signal
Observed movement
Match found
ONS food CPI matched
2022–23
UK avian flu (HPAI)
Model signal
Observed movement
Match found
2021–22
UK energy price spike
Model signal
Lag observed
Match found
Checking local driver coverage…
Select this tab to load data source status.
Product Graph Specification
Food Chain Lens can now describe products through bounded product graph specs. This makes it easier to add new products consistently. Each spec must declare dependencies, evidence status, limitations, and confidence. Brand level graphs are possible later, but the current system does not claim exact retailer traceability, and it does not predict exact shelf prices.
Bundled example specs load with this tab.
Schema fields and bounded vocabularies
Specs are pure data (YAML or JSON), strictly validated, and compiled into the existing supply chain graph structure. The format is bounded, not a programming language: fixed vocabularies and hard size and depth limits.
Spec fields
product_slug, product_name, product_type, category, archetype, version,
description, brand, retailer, country_scope, graph_layers, nodes, edges,
risk_drivers, commodities, evidence_sources, observed_series_mappings,
limitations, confidence_notes
Node types
product, brand, retailer, business_activity, asset, production_input,
commodity, risk_event, market_price, observed_series, origin_country
Relationship types
contains, requires, processed_by, produced_by, depends_on, priced_by,
affected_by, validated_by, sourced_from, transported_by
Evidence statuses
verified, partial, planned, expert_estimate, missing
Hard limits
weights and confidence 0–1; max 80 nodes, 160 edges, 9 graph layers;
graph depth bounded by declared layers; limitations and at least one
evidence source are mandatory
Validate specs locally:
python -m gcmrp.jobs.import_product_specs examples/product_specs.
See docs/product_graph_spec.md for the full format guide.
Retail Price Evidence Hub
Food Chain Lens can now connect product identity, open price observations, local research datasets, official index evidence, and future licensed sources through one retail evidence layer. Open Food Facts helps identify products. Open Prices and local datasets can provide reported price observations. Generated product graph candidates stay unreviewed until a human promotes them.
Reported observations are evidence, not live supermarket truth. Community sources are a community open dataset and not official retailer verified pricing unless the source says so. Brand and country labels are not supply chain proof. Generated candidates require human review. Licence and coverage limitations apply to every source.
Bundled example coverage loads with this tab.
Open Food Facts
Read only client
Product identity: barcode, name, brand, quantity, categories, labels. A community open dataset — identity, not price, and not supply chain proof.
Open Prices
Read only client
Community price observations linked to barcodes, with shop and date metadata where available. A community open dataset — not official retailer verified pricing.
Manual CSV and research datasets
Local import
Controlled local imports of historical retail price observations, including Kaggle style research datasets where the licence permits. Missing licence metadata adds an explicit limitation.
Official index evidence
Validation layer
ONS and similar official indices stay validation benchmarks: category index evidence, not product level shelf price truth unless the data is genuinely granular.
Commercial grocery API
Future adapter
A placeholder slot for future licensed data sources. Nothing is implemented until credentials, documentation, and licence terms exist.
Retailer web observation
Future source type
Represented as a future source type only. No supermarket scraping is implemented in this version.
Community price reports
Future source type
Represented as a future source type only. No accounts, no receipt photos, and no personal data collection.
Technical evidence fields and endpoints
Every source normalises into the same internal objects with a declared source type, evidence status, confidence, and limitations. Unit prices are normalised per kg, per litre, per egg, or per loaf where the quantity can be parsed; unknown quantities keep the original price and gain a limitation.
Source types
open_food_facts, open_prices, manual_csv, kaggle_dataset, official_index,
commercial_api, retailer_web_observation, community_report, synthetic_fixture,
unknown
Evidence statuses
verified_official, community_open_dataset, retailer_reported,
commercial_dataset, manual_observation, research_dataset, modelled, missing,
unknown
Read only endpoints
/retail-prices/sources, /retail-prices/coverage,
/retail-prices/aggregate, /retail-prices/examples,
/product-specs/candidates
External lookups
disabled by default; ENABLE_EXTERNAL_RETAIL_PRICE_LOOKUPS=true opts in
on deployments you control
Import local files:
python -m gcmrp.jobs.import_retail_prices path/to/file.csv.
Generate unreviewed candidates from a local product export:
python -m gcmrp.jobs.import_open_food_facts_products examples/open_food_facts/sample_products.json.
See docs/retail_price_evidence_hub.md.
Global Origin Map
Where upstream food basket commodities enter the UK supply chain.
Top supplier
—
Select a commodity
Top 3 concentration
—
Combined import share
Diversification
—
Active suppliers
Commodity selected
Wheat
UN Comtrade 2023
This layer shows UK import exposure by origin country based on UN Comtrade trade statistics. It does not claim exact retailer traceability. Import partner country does not always equal production origin.
UN Comtrade
Import value share
Not exact retailer traceability. Import partner country does not always equal production origin.
Select the Global Origin Map tab to load origin data.
Schematic origin map based on import country weights. Data source: UN Comtrade.
Exposure assessment
Select a product and commodity to view import origin exposure and concentration analysis.
Evidence and Validation
Food Chain Lens compares model pressure signals against official observed datasets. These checks confirm direction and timing, not exact shelf prices.
Pressure index, not retail price prediction. Static graph weights are expert estimates. Origin data shows UK import exposure, not exact retailer traceability.
Observed evidence timelines
Historical Product Prices
Track how the best available observed series has moved over time for each basket product. Some series are producer or farmgate prices, while others are CPI category indices. These are evidence signals, not exact supermarket shelf prices.
Eggs and milk use producer and farmgate prices, which are not supermarket shelf prices. Bread, chicken, potatoes, and pasta use CPI category indices, which are not exact product shelf prices.
Producer price, not supermarket shelf price.
Latest observed value
—
Awaiting series
10 year movement
—
Across the visible window
Source type
—
Official series where available
Coverage status
—
Local imported rows
Click a point or event window to inspect movement.
Historical series loads with the Evidence and Validation tab.
This chart uses observed upstream or CPI proxy evidence, not exact shelf prices.
Select a product to read a text summary of the observed series, its source type, coverage, date range, latest value, and main caveat.
Where each series comes from
Each product maps to its best available observed series. Producer and farmgate prices are upstream prices. CPI category indices track a whole category against 2015=100. Synthetic retail fixtures are bundled demonstration data and never real market evidence. None of these are supermarket shelf prices.
| Product | Series | Frequency | Unit |
|---|---|---|---|
| Eggs | Defra egg producer prices | Quarterly | pence per dozen |
| Milk | Defra farmgate milk prices | Monthly | pence per litre |
| Bread | ONS bread and cereals CPI (D7D5) | Monthly | index, 2015=100 |
| Chicken | ONS meat CPI (D7D6) | Monthly | index, 2015=100 |
| Potatoes | ONS vegetables CPI (D7DB) | Monthly | index, 2015=100 |
| Pasta | ONS bread and cereals CPI as a labelled proxy (D7D5) | Monthly | index, 2015=100 |
To load the CPI series: download the
ONS MM23 dataset
and run
python -m gcmrp.jobs.import_ons_cpi path/to/mm23.csv.
To load Defra egg or milk data, run
python -m gcmrp.jobs.import_product_prices path/to/file.ods eggs_producer_price eggs
(or milk_farmgate_price milk).
Read-only endpoints for this explorer: /price_history/products,
/price_history/summary, /price_history/{product_slug},
and /price_history/{product_slug}/sources. Retail evidence fixtures
from the Retail Price Evidence Hub can be selected as a clearly labelled
synthetic secondary source.
Evidence trust centre
Layer
Official evidence
What it supports
What it does not prove
Basket
Official ONS food CPI evidence
Directional pressure during Ukraine grain shock 2022
Exact retail shelf prices or product-level causation
Eggs
Defra egg producer prices (quarterly)
Upward pressure during avian flu outbreak 2022–23
Supermarket shelf egg prices or exact timing
Milk
Defra farmgate milk prices (monthly)
Direction matched with 14-month lag during energy shock 2021–22
Retail milk prices or causal proof
Origin exposure
UN Comtrade import value weights
Import origin concentration by commodity and year
Exact retailer traceability or production origin
Basket level
Official ONS food CPI
Evidence loaded
Direction matched for Ukraine grain disruption
Eggs
Defra egg producer prices
Evidence loaded
Signal matches observed price movement
Milk
Defra farmgate milk prices
Evidence loaded
Signal matches observed price movement
Model Reliability
Food Chain Lens checks whether model pressure signals move in the same direction as official observed series during known historical shocks. This tests directional reliability and timing. It does not prove causality or predict exact shelf prices.
Reliability results load with the Evidence and Validation tab.
How timing lags are tested
Food prices often respond months after an upstream shock. Each reliability check re-tests directional agreement after shifting the observed series by 0, 1, 3, 6, 9, 12, 14, and 18 months, then reports the best-matching lag. The best lag is descriptive, not a fitted parameter.
Why this is not price prediction
Observed sources are producer prices, farmgate prices, and CPI sub-indices — partial proxies, not supermarket shelf prices. A directional match shows the model pointed the right way at a plausible lag. It does not prove the shock caused the price movement and it does not forecast exact prices.
Technical reliability metrics
Reliability metrics load with the Evidence tab.
What evidence supports the movement?
Official ONS and Defra series used to validate model direction and timing.
Technical series details
Observed CPI series validate direction and timing, not exact product shelf prices. This is directional validation only — not causal proof or quantitative prediction.
D7BT is CPI All Items (benchmark only) — not a food category series.
D7BU is the correct COICOP 01 food series. All six food sub-index series IDs
are verified against ONS MM23 (2026-06-07).
To import observed data: download the
ONS MM23 dataset
and run
python -m gcmrp.jobs.import_ons_cpi path/to/mm23.csv.
Product level validation
Developer import notes
Product level data may be farmgate or producer price data, not supermarket shelf price. This is partial product evidence only — not causal proof or exact retail price prediction.
To import Defra or AHDB product data: prepare a CSV with
date and value columns,
then run python -m gcmrp.jobs.import_product_prices path/to/file.csv eggs_producer_price eggs.
Select the Validation tab to load the validation report.