A note on this case study
What follows is an illustrative, composite scenario, not a real company. We built it to walk through a pattern that shows up repeatedly in Category 1 (Purchased Goods & Services) work: a small number of suppliers usually account for a disproportionate share of a company's purchased-goods emissions, and the path to reducing that footprint runs through data quality improvement first, engagement second, and switching only as a last resort. Any figures below are for illustration only.
The starting point
Our illustrative company is a mid-sized electronics manufacturer that assembles consumer devices from several hundred component suppliers, ranging from large printed-circuit-board fabricators to small connector and casing shops. Like most manufacturers in this position, Category 1 was by far its largest Scope 3 category, dwarfing business travel, commuting, and even upstream transportation combined.
Its first-year inventory used spend-based estimation, the weakest tier in the GHG Protocol's data quality hierarchy: emissions were estimated by multiplying procurement spend by industry-average emission factors. That approach produced a defensible total but told the company almost nothing about which suppliers actually drove the number. Spend and emissions intensity don't move together — a supplier receiving a small fraction of total spend can still contribute a large share of embedded emissions if its manufacturing process is carbon-intensive or its grid is coal-heavy.
Finding the concentration
In year two, the company moved a subset of its supply base to hybrid data — combining supplier-reported activity data with average emission factors where primary data wasn't yet available — for its top spend categories. This is a standard progression: hybrid data is more reliable than spend-based estimates but doesn't yet require full supplier-specific primary data, which is the strongest tier but also the hardest to collect at scale.
The re-estimation revealed a familiar pattern. A small number of suppliers, concentrated in board fabrication and metal casing, accounted for a large majority of Category 1 emissions, while the long tail of smaller component suppliers contributed comparatively little despite representing most of the supplier count. This is the kind of concentration that makes prioritization possible: instead of trying to engage several hundred suppliers at once, the sustainability and procurement teams focused effort on the handful driving the bulk of the footprint.
The engagement choice
For each of these priority suppliers, the company presented two paths rather than issuing an ultimatum. Suppliers could either commit to a joint improvement plan — covering things like renewable electricity procurement, process efficiency upgrades, and better production-route data disclosure — or they could accept that continued business would be evaluated against emissions performance alongside cost and quality, opening the door to switching if performance didn't improve within an agreed period.
This structure matters. Framing switching as a contingency rather than an immediate action reflects how supplier engagement programs typically work in practice: outright switching carries real costs, including requalification time, quality risk, and loss of institutional knowledge, and it doesn't guarantee a lower-emissions replacement is even available at the required volume. Engagement first, exit as leverage, is the more common and more defensible sequence.
What happened over the following years
Over the next several years in our illustrative scenario, most of the priority suppliers stayed in the program and made measurable progress, largely through electricity source changes and process efficiency work, supported by better data reporting that let the manufacturer move those suppliers toward supplier-specific primary data rather than averages. A minority of suppliers didn't engage meaningfully with the plan, and for those the company followed through: it phased in alternative suppliers for the affected component lines, prioritizing candidates that could provide credible primary emissions data rather than simply the lowest spend-based estimate.
The combined effect — efficiency gains among suppliers who stayed, plus replacement of a few laggards — produced a steady, multi-year decline in Category 1 emissions per unit produced, even as production volumes grew. Importantly, the improvement in data quality itself explains part of the story: as more suppliers moved from spend-based to hybrid to supplier-specific data, the inventory became a more accurate reflection of actual emissions rather than industry averages, which is a necessary precondition for knowing whether real reductions are happening at all.
What this illustrates
A few patterns generalize beyond this composite example. First, spend-based data is a starting point, not a decision-making tool — concentration analysis requires at least hybrid data to be credible. Second, supplier emissions concentration tends to follow a Pareto-like distribution, which makes prioritization both possible and necessary; treating all suppliers identically wastes engagement capacity. Third, switching works best as a backstop for non-responsive suppliers rather than a first move, since collaborative improvement usually reduces emissions faster and cheaper than requalifying a new supply base. Finally, none of this is possible without first investing in the underlying measurement and inventory work — you can't prioritize what you haven't measured with enough granularity to see.
For companies building this kind of program on real supplier data, the sequence is the same regardless of sector: establish a defensible Category 1 baseline, upgrade data quality for the highest-spend or highest-intensity suppliers, then design engagement structures that give suppliers a genuine path to stay in the program before switching becomes the default response.