Product carbon footprint — PCF built on your plant's data, not on a sector factor.

The problem

The B2B customer asks for PCF per SKU — and the sector factor we have been delivering for years no longer works.

When the commercial manager of an industrial plant receives the latest specification from their best customer (automotive OEM, retail brand, large construction group), the shift is clear:

1. Before — PCF was a nice-to-have delivered with generic sector factors. The customer accepted it because it was what the market offered.
2. Now — the customer asks for PCF per SKU, with traceable primary data, and uses it to segment suppliers (price + emissions + verifiable data). Some OEMs have gone as far as offering green premium contracts to the supplier that can prove consistent primary data.
3. And the gap — the plant is still calculating PCF from factors. Raw material is reported with a sector factor. Electricity consumption is prorated across every SKU. Furnace gas is split by minutes of use. The figure is defensible — until the customer asks for third-party verification, and then the sector figure does not hold.

The sentence the commercial director has had to say more than once to the plant manager: "if we don't give the customer a PCF built on primary data before the next RFQ, we lose position against the competitor who already has it". And the honest answer is that instrumenting for PCF per SKU was never designed into the plant — it has to be added.

How it fits into the IRIS system

iLEAN does not replace your LCA engine — it seals the gap between the plant and primary data per SKU.

PCF calculation does not fail for lack of an LCA engine — it fails because there is a gap between what the plant actually consumes to make each SKU and what the LCA engine receives. iLEAN acts as the filler that closes that gap. It does not ask you to change your LCA engine or your ERP. It sits on top and guarantees that every input figure to the calculation starts from traceable primary data, per batch and per SKU.

Tracer captures consumption per SKU. Writer builds the cradle-to-gate PCF dossier. The external audit finds every figure traced back to its source.

The iLEAN pieces applied to Product Carbon Footprint:

• iLEAN Tracer — captures consumption where it happens, linked to the manufacturing order and the SKU. Electrical submetering per line/process, furnace gas consumption logged by the minute, real raw material weight per batch, supplier certificates with their PCF per component captured by email/EDI, real kilometres and transport mode upstream. Allocation is made to the SKU produced, not prorated.
• iLEAN Writer — builds the PCF dossier with the structure ISO 14067 / GHG Protocol Product Standard require: scope, functional unit, system boundaries, primary sources (readings, certificates) and secondary sources (inventory factors) clearly differentiated, emission factors used with their version, and estimated uncertainty. Every SKU has a defensible PCF.
• PCF agent — tracks the consistency of the data over time. If the same SKU starts coming out with a PCF significantly different from its history, the agent alerts the team (change of raw material supplier, change of energy mix, change of line efficiency). Before the B2B customer spots the deviation or the verifier flags it as an inconsistency.

See the full IRIS architecture →

Before and after

PCF with a sector factor vs. cradle-to-gate PCF with iLEAN

Aspect	Classic system (sector factor)	With iLEAN Tracer + Writer
Electricity consumption per SKU	Prorated across all products	Real submetering per line/process
Furnace gas per SKU	Split by minutes of use	Direct log per manufacturing order
Raw material PCF	Average sector factor	Supplier certificate captured by EDI/email
Upstream transport	Estimate with average km and mode	Real km and mode from the freight forwarder
PCF per specific batch/SKU	Same figure for the whole SKU	Traceable to the batch — reflects real variation
Third-party verification	Hard — the figure cannot be defended	ISO 14067 dossier with full traceability

Impact estimate

Impact estimate for your plant — to be validated with your numbers.

The following block is an estimate to be validated with your plant's data and your SKU portfolio. We set it out so the committee has an order of magnitude; we refine it during the diagnostic.

• Industrial B2B supplier (automotive tier 1/2, retail private label, construction) with a customer already asking for PCF per SKU, 1-3 plants, an existing LCA engine or an outsourced one.
• First value expected within a few weeks: a cradle-to-gate PCF pilot built on primary data for 3-5 strategic SKUs (the Pareto 80% of revenue with the demanding customer).
• Indicative payback between 4 and 9 months, depending on the weight of the demanding B2B customer in revenue and the margin difference that customer pays (or demands) for verified vs estimated data.
• The hard lever is not only the margin difference — it is the risk of exclusion from the OEM's supplier panel once it starts requiring verified primary data. The pilot pays for itself if it avoids a single lost position in a strategic RFQ.

And the sustainability manager's reasonable doubt

"What if the AI gets it wrong when allocating consumption to an SKU or reading a supplier certificate?" — hallucination is a problem of free generation, not of anchored tasks. In tasks where the AI is limited to recontextualising an existing figure (reading a supplier certificate in PDF and extracting the PCF per component, assigning a meter log to the correct manufacturing order), the best models brought the error below 1.5% ^[1]. And even then, the PCF is not signed off on its own: iLEAN proposes, the sustainability manager validates, and the traceability of every figure is left ready for the third-party verifier. The three safety rings are there precisely for this.

^[1] OpenAI paper "Why Language Models Hallucinate", 2025 — on the reliability of AI in anchored tasks.