The efficiency case for Scope 3 data in 2026, and how to turn it into a business case

More than three-quarters of supply chain sustainability leaders say they always or often use efficiency gains as a key argument when building the business case for supply chain and product sustainability work, according to Verdantix. For Scope 3 data, the argument is easy to make and often made without numbers. This post sets out where the efficiency sits, then walks through turning it into a business case that finance can review line by line.

What does Verdantix say about efficiency in the business case?

In its blog From Supplier Data To Product Footprints: Three Innovation Trends Reshaping Supply Chain And Product Carbon Management, Verdantix draws on its benchmark report Smart Innovators: Supply Chain And Product Carbon Management. It reports that more than three-quarters of supply chain sustainability leaders use potential efficiency gains as a key argument for the business case, and that drivers of implementation go beyond compliance.

"...always or often use potential efficiency gains as a key argument..." Alessandra Leggieri, Senior Analyst, Verdantix

The same blog notes that carbon data are increasingly used to inform procurement decisions and to identify lower-carbon materials and suppliers. It also notes that managing product carbon footprints remains resource-intensive, particularly for manufacturers with large product portfolios and complex supply chains.

Efficiency is the argument leaders already reach for. The work is to show which hours it frees, and what else the data changes.

Where does the effort in Scope 3 data go?

Four activities take most of the time, and each one has a different fix:

ActivityWhere the effort goesWhat to measureFinding supplier dataSearching reports, portals and past surveys, supplier by supplierHours per cycle, and suppliers with usable data before any requestAsking suppliersBlank forms, chasing, answering follow-up questionsRequests sent, and requests returned usableReworking the baselineSwapping sector averages for supplier figures and re-running the numbersLines still resting on a generic factorPreparing for reviewReconstructing where each number came fromTime to trace one figure to its source

What changes when coverage exists before the first request?

Supplier data is the largest block of effort, and part of it is avoidable. Many organisations on a typical Category 1 list have already published a GHG inventory or product-level footprint, or shared activity data with other clients. DitchCarbon starts from that: verified emissions data for over 2 million organisations, built on primary emissions data wherever it exists. Sharing is always the supplier's decision.

Two effects follow. Suppliers who have already published are covered before anyone sends a form. And where a figure does need improving, the request arrives prepopulated with what the supplier has already published, so the supplier confirms and corrects rather than starting from a blank page. In a recent deployment, about 60% of a large supplier base was reached within 2 weeks.

Every figure also carries its source and change history, so reviewing a baseline starts from a trail rather than a search.

How do you turn the table into a business case?

The table is a measurement plan. A business case needs your own numbers in it, in four steps.

1. Baseline the last cycle. For each of the four activities, record the hours spent, the number of people involved and their loaded hourly cost. Add the two quality counts that sit beside effort: how many suppliers had usable data before the first request went out, and how many lines still rest on a generic factor. Put them in a table like this and fill it from timesheets, calendars or a short team survey:

ActivityHours last cyclePeople involvedLoaded hourly costTotal effortFinding supplier dataHours multiplied by costAsking suppliersHours multiplied by costReworking the baselineHours multiplied by costPreparing for reviewHours multiplied by cost

2. Name the mechanism, one row at a time. A claim that effort will fall needs a reason that finance can test. Finding data falls where coverage exists before the first request. Asking falls where requests arrive prepopulated and are limited to the suppliers worth asking first. Reworking falls as fewer lines rest on a generic factor. Review falls where each figure carries its source and change history. If you cannot name the mechanism for a row, leave that row out of the case.

3. Test on a slice before you commit. Pick one spend category, or the suppliers carrying the most emissions, and run it through the new process with the same four counts. Compare against the baseline for the same slice. A measured range from a pilot is easier to approve than an estimate for the whole programme, and the target at the end is numbers you can defend within 2 weeks.

4. Add what efficiency does not capture. Hours saved is the floor of the case. Verdantix notes that carbon data are increasingly used to inform procurement decisions, so add the decisions the data now supports: which suppliers to engage first, where a lower-carbon alternative exists, and how much of the baseline rests on supplier data rather than an average. These are harder to put a figure on, so state them as outcomes with a named owner rather than as savings.

What should the one-page case contain?

Finance reads a case in a familiar order, so use it:

1. The problem, in your baseline numbers: hours, people and the share of lines on a generic factor.
2. The proposal, with the mechanism for each activity.
3. The expected change per activity, as a range taken from the pilot.
4. The cost, including internal time to onboard.
5. The risks, such as suppliers who do not respond, and how the gap is shown rather than hidden.
6. The measure of success and the date you will report it, normally after the next reporting cycle.

Presented this way, the efficiency claim stops being an assertion and becomes a forecast with a method behind it. If the next cycle comes in outside the range, you will know which row to revisit.

Hours back for the sustainability team, fewer forms in front of suppliers, and a baseline in 2 weeks: that is the efficiency case for Scope 3 data, and the table above is how you prove it.