Eggs

The French egg supply chain relies on one of the largest laying-hen flocks in Europe (France is the EU's leading producer). The eggs laid are split between hatching eggs (reproduction) and table eggs, the latter being either packed for in-shell sale or broken in egg-processing plants to make egg products (liquid, dried egg, etc.). The chain stands out through a growing share of processing into egg products (~40% of production) and the generation of egg-breaking co-products (shells, technical egg white, production rejects).

The flow diagram presenting the results for this supply chain, produced as part of the SOCLE project, offers a complete view of how material circulates, from supplies to final outlets. It is a Sankey-type representation: the thickness of the arrows is proportional to the quantities (in kilotonnes, kt), making it possible to grasp at a glance the relative weight of each segment.

Complete view of the results

This diagram aims to represent the supply chain as a whole at the level of detail allowed by the available public data. The chain is modelled without distinguishing farming systems (cage / barn / free-range / organic), as this distinction cannot be reliably reconstructed all along the chain from public sources.

The diagram represents all the material flows within the egg supply chain in France, for the 3 years studied in the SOCLE project: 2015, 2019 and 2023 (select the desired year in the timeline below the diagram).

Supplies include national production (laying), split into hatching eggs and table eggs, supplemented by imports of fresh eggs. The intermediate market highlights the two primary processing routes: the packing of shell eggs and the egg-breaking industry, which turns eggs (downgraded + processed + imported) into egg products (split into "non-white" and "egg white / ovalbumin", following the PRODCOM nomenclature), while generating co-products (shells, technical egg white, production rejects) and evaporating water during drying. The outlets include hatching (hatching eggs), human food (self-consumption, at-home purchases, out-of-home catering, agri-food industries), the recovery of co-products (fertilisation, animal feed, various uses) as well as exports.

The thickness of the flows makes it possible to identify the main recovery routes, the growing weight of processing into egg products, and the diversity of the final uses of the material produced by laying.

AF-Filières methodology: general principle and illustration through the diagrams

The AF-Filières (Supply Chain Flow Analysis) methodology, implemented in the SOCLE project, aims to reconstruct all the flows of a supply chain by aggregating, structuring and analysing all the available information. It consists in gathering all the relevant data about a given sector into a single, hierarchical model that links every piece of information together. This systematic cross-checking makes two essential operations possible: 1. checking the internal consistency of the data and, where necessary, reconciling it; 2. determining missing values, either precisely or as intervals when some indeterminacy remains.

The Sankey diagrams presented in the "Method and sources" section illustrate each step of this process, relying directly on the data actually used in the SOCLE model.

Part 1 — Data collection, qualification and structural completion of the model

(Associated diagram: Sources)

This first phase starts with an inventory of all the relevant public and sector-specific sources describing the supply chain (available years, nature of the data: direct values, min/max, coefficients, etc.). The associated diagram shows, for each documented flow, the sources used and their characteristics, giving an immediate reading of the well-documented areas and of those where information is scarcer. The diagram also makes it possible to display the type of data collected in the legend: direct value, or allocation and yield coefficients, which express a relationship between two flows rather than an absolute value. The raw data from the source can also be consulted in the corresponding sheet of the model's Excel file (the sheet name is in the tooltip of each flow).

Because the collected data generally does not cover the whole supply chain, this step also includes structural completion: additional flows, not yet documented, are added to the model to ensure the continuity of the processing chains.

At the end of this phase, the whole knowledge perimeter is mapped: the flows for which data exists and those that will have to be determined later.

Part 2 — Validation, reconciliation and determination of the flows

(Associated diagram: Method)

Based on this complete structure, the flows for which reliable data exists are consolidated. When several sources converge, the collected values are retained as they are. When inconsistencies are detected (contradictory material balances, incompatible yields, etc.), a statistical discrepancy flow may be added to the model.

Once the model has been made consistent, the missing flows are determined using material balances, structural constraints and relationships between flows. Some flows thus obtain a precise value ("determined"); others can only be defined through a min–max range when a lack of information remains. In the egg supply chain, the water evaporated during the drying of egg products is thus determined by material balance on the egg-breaking node.

Part 3 — Overall synthesis and confidence level

(Associated diagram: Confidence level)

The last diagram offers an overall view of the results, enriched with a global reliability indicator for each flow. This reliability is based on several criteria: quality and precision of the sources, closeness of the collected data to the project's perimeter, magnitude of the statistical discrepancies, width of the indeterminacy intervals, etc.

How to read the reliability levels:

Reliable: data with a very low level of uncertainty, drawn from validated and consistent sources.
Robust: solid data, confirmed by several elements or methods, usable with confidence.
Probable: plausible data, based on serious elements but carrying notable uncertainty.
Approximate: data giving the right order of magnitude, but without any guarantee of precision.
Indicative: data provided for information only, with a high level of uncertainty.

This diagram makes it possible to identify at a glance the segments of the supply chain that are well documented (production, trade, egg products) and those that remain more exploratory (downgrades, drying water, fine breakdown of the outlets).