A note on this post: what follows is an illustrative, composite scenario built to show how a Category 1 primary-data project typically unfolds. It is not a real company and should not be read as a case study of one. The mechanics, sequencing, and setbacks described are representative of what we'd expect from this kind of engagement, based on how the GHG Protocol's data quality hierarchy and supplier engagement generally play out in practice.
The starting point
Picture a mid-sized industrial components manufacturer with a few hundred active suppliers across metals, plastics, electronics, and packaging. Its Category 1 (Purchased Goods & Services) footprint, like most companies at this stage, was built entirely on spend-based estimates: procurement spend by category multiplied by an emissions factor per dollar. This is the weakest rung of the GHG Protocol's data quality hierarchy, useful for an initial inventory but incapable of showing whether a supplier switch, a material substitution, or a supplier's own decarbonization actually moves the number.
The company's goal was not to convert its entire supplier base. It picked a defined slice — its top twenty suppliers by spend within the metals and electronics categories, representing a meaningful share of Category 1 mass — and set out to move that slice from spend-based to supplier-specific (primary) data over eighteen months.
Months 1-3: scoping and the first wall
The project started with supplier segmentation: ranking suppliers by spend, then by estimated emissions intensity, to find where primary data would actually change the inventory rather than just add administrative overhead. This is a step worth taking seriously — chasing primary data from a low-spend, low-intensity supplier is a poor use of a limited engagement budget.
The first setback showed up immediately. Several suppliers had no emissions data at all, not even a corporate sustainability report. Others had data but at the wrong boundary — facility-level totals with no way to allocate emissions to the specific components being purchased. The team had budgeted for a straightforward data request; instead it needed a supplier-by-supplier capability assessment first.
Months 4-8: the request goes out, and mostly doesn't come back
A structured data request template went to all twenty suppliers, asking for product-level or process-level emissions data aligned with what the GHG Protocol calls hybrid or supplier-specific methods. Response rates were uneven. Larger suppliers with their own reporting obligations were more forthcoming, though even they sometimes provided data at a level of aggregation that couldn't be cleanly mapped to the purchased items. Smaller suppliers frequently had no internal capacity to respond at all — no one on staff tracking emissions by product line, no clear owner for the request.
By month eight, only a handful of suppliers had returned usable data. This is a common and predictable stall point: primary data collection depends on supplier capability, not just supplier willingness, and capability gaps don't close on a customer's timeline.
Months 9-13: narrowing scope, building the bridge
Rather than treating the slow responses as a failure, the project pivoted to a hybrid approach for the suppliers who couldn't yet provide full primary data — combining whatever supplier-specific inputs did exist (energy mix, process type, material composition) with average-data emissions factors to fill the gaps. This is explicitly a middle tier on the data quality hierarchy, and treating it as a stepping stone rather than an endpoint mattered for how the results were later communicated internally.
For the suppliers who were unresponsive, the team ran a small number of direct site engagements — video calls and, for two suppliers, in-person visits — walking through exactly what was needed and why. This closed some gaps but also surfaced a second setback: two suppliers, once they understood the request would recur annually and feed into the buyer's own regulatory disclosures, treated it as a contractual matter and looped in legal review, adding weeks of delay.
Months 14-18: consolidation and honest accounting
By the end of the eighteen months, the illustrative outcome looked like this: a portion of the twenty targeted suppliers had delivered genuine supplier-specific data, a larger portion had landed on hybrid data, and a small number remained on spend-based estimates because engagement simply hadn't produced anything better in the window available. The inventory for that slice of Category 1 spend was recalculated using the improved data, and the change in reported emissions was disclosed alongside a note on methodology — not presented as if the whole category had been upgraded.
What the setbacks actually teach
The recurring lesson across a project like this is that data quality improvement is a supplier-capability-building exercise disguised as a data request. Segmentation to find where it's worth the effort, templates that suppliers can actually complete without specialist staff, and patience with hybrid data as a legitimate interim state all matter more than the initial data request itself. Companies that expect a linear conversion from spend-based to primary data across an entire supplier base in eighteen months are usually setting themselves up to either miss the timeline or quietly lower the bar on what counts as "supplier-specific." A narrower, honestly reported slice of real improvement is a more defensible foundation than a broader claim that doesn't hold up to scrutiny.