For years, the fashion industry’s sustainability gaze has been firmly fixed on the farm—treating raw material cultivation as the ultimate environmental baseline.
But a new, data-driven reality check from the Impetus Group and traceability platform TrusTrace reveals that what happens inside factory walls is a massive operational blind spot. By shifting the focus from smoothed-over industry averages to granular, process-level primary data, the recent study uncovers unexpected hotspots. For example, in a controlled manufacturing environment utilizing pre-colored recycled fibers, textile finishing accounted for 75 percent of water consumption.
With impending regulations like the Digital Product Passport (DPP) and CSRD rapidly shifting the landscape from vague, claims-based marketing to strict, evidence-based compliance, relying on secondary datasets is no longer a viable strategy.
Here, Tércio Pinto, board member at Impetus Group, and Shameek Ghosh, co-founder and chief executive officer of TrusTrace, discuss the power of facility-level data, the role of AI in managing global supply chains and how brands can leverage immediate manufacturing insights to drive real, scalable reductions.
Sourcing Journal: While global ESG conversations often fixate on raw material cultivation (like cotton farming) as the ultimate environmental villain, this new data tells a very different story about what happens inside the factory walls. Why has textile finishing emerged as such a massive blind spot, accounting for three-quarters of your water consumption, and how should this reshape where brands focus their reduction efforts?
Tércio Pinto: For years, most sustainability conversations have focused on raw materials, and for good reason. Cotton cultivation remains a major contributor to overall environmental impact. But our study shows that when you collect primary data across manufacturing, you uncover hotspots hidden behind industry averages.
Finishing is one of the clearest examples. Washing, softening, drying, and stabilizing all demand significant water, energy, heat, and chemicals. In our analysis, finishing drove roughly 75 percent of water consumption across the manufacturing stages we directly control.
An important caveat: the Revive set is made with pre-colored recycled fibers and skips conventional dyeing entirely. In a typical process, dyeing would be the most impactful finishing stage by far. Designing it out is what concentrated the remaining impact in finishing. So, the takeaway is not that finishing without dyeing is generally the biggest hotspot. The primary data shows you precisely where impact sits in your specific product.
Why has finishing been a blind spot? Most companies simply have not had access to process-level data. They know what materials they buy, but not how resources are consumed inside facilities.
That is exactly what this study set out to prove. Eco-design and circularity can significantly reduce a product’s footprint, and measuring the full chain reveals the biggest improvement opportunities. Here, beyond cultivation, those sit mainly in finishing.
None of this means brands should stop focusing on raw materials. They need to broaden the conversation. Finishing offers some of the most immediate reduction opportunities because these are operational decisions, improvable through technology, process optimization, chemistry selection, renewable energy, and product design.
S.J.: From a data architecture perspective, why do manufacturing stage impacts like finishing get so routinely underrepresented or smoothed over in typical ESG reporting? What changes when a brand shifts from using industry averages to the process-level primary data TrusTrace captures?
Shameek Ghosh: Most ESG reporting today is still built on secondary datasets, modeled assumptions, and industry averages. Those approaches are useful for establishing a baseline, but they often flatten the complexity of what is actually happening inside a supply chain.
Manufacturing processes like finishing become underrepresented because they’re frequently grouped into broad categories rather than measured directly. When that happens, you lose visibility into where resources are actually being consumed.
What changes with primary data is that you’re no longer estimating impact. You’re measuring it. Instead of assuming how much water, energy, or chemicals were used, you’re capturing what was actually consumed at the facility and process level.
That level of granularity fundamentally changes decision-making. It allows brands to connect verified production data directly to sourcing, supplier engagement, compliance, and risk decisions, turning sustainability data into a foundation for action rather than just reporting.
S.J.: You’ve pointed out a critical distinction between a brand’s overall footprint calculations and their actionable opportunities for reduction. For stages controlled by the Impetus Group, how does focusing on manufacturing impact give brands more immediate, direct leverage to drive down emissions compared to trying to overhaul agricultural supply chains?
T.P.: Agricultural transformation is extremely important, but it is also complex. It involves large ecosystems, multiple stakeholders, geography, weather conditions, and factors that are often outside a brand’s direct control.
Meanwhile, manufacturing is different. Within the production stages we manage, we can make decisions and see results much more quickly. We can optimize finishing processes, improve energy efficiency, increase renewable energy use, reduce unnecessary washing or dyeing, adopt alternative chemistries, and redesign products to eliminate high-impact steps altogether.
The Revive product demonstrates what is possible when sustainability is considered at the design stage. By using pre-colored recycled fibers, we avoided conventional dyeing processes and significantly reduced the additional water, energy, and chemical demand that would normally be required. That is why manufacturing data is so valuable. It helps brands identify areas where they can take action immediately rather than waiting years for broader system-level changes.
S.J.: How much of this blind spot is simply an issue of system boundaries—meaning, how we define what is being measured? How do environmental hotspots radically shift when a brand looks at its entire macro-value chain versus looking strictly at the operational production stages they can directly influence?
T.P.: System boundaries are central to this conversation. Across the entire value chain, raw material production remains a significant contributor, particularly for water consumption. But within the manufacturing stages we control, finishing becomes one of the dominant hotspots, keeping in mind that this product was not dyed. In a conventional process, dyeing would typically be the most impactful finishing stage. Both views are correct. They simply answer different questions.
Part of the blind spot is also structural. Most actors in the supply chain are not measuring consumption in a systematic or transparent way. And brands are not aligned on what they ask for—each has its own metrics, so a supplier needs the capacity to measure, control, and share data according to each individual request. That requires real investment in systems that capture order-level information and feed it into brands’ platforms.
Well-collected impact data, validated by a third party, is what makes correct measurement possible and lets you manage the blind spots.
Buying decisions also directly influence the final impact. The same product made in a different location, with different suppliers, will most likely have a different footprint.
S.G.: The challenge is that many sustainability discussions treat environmental impact as if there is only one answer. In reality, the answer depends on what you’re trying to understand.
If you’re measuring total product impact, you need an end-to-end perspective. If you’re looking for opportunities to improve operational performance, then manufacturing-stage data becomes critically important.
What the Impetus study demonstrates is that greater granularity reveals opportunities that would otherwise remain hidden. The more detailed the data becomes, the more accurately organizations can prioritize where action will have the greatest effect.
We see this across the platform. With over 112,000 suppliers active in more than 80 countries, the same product category can have completely different hotspots depending on how and where it is made. That is exactly why averages mislead.
And this is where AI starts to play a meaningful role. When you’re working across hundreds of suppliers and thousands of data points, no team can manually surface where the risk hotspots are. AI does that continuously, so brands aren’t waiting for a quarterly report to find out where attention is needed.
S.J.: With regulations such as the Digital Product Passport (DPP) and CSRD putting immense pressure on fashion brands, standardizing data is no longer optional. How does having granular, facility-level data on something like finishing impact help a brand stay compliant, rather than just relying on high-level sustainability claims?
S.G.: The regulatory landscape is moving rapidly from claims-based sustainability to evidence-based sustainability.
Frameworks like Digital Product Passports and broader product transparency requirements increasingly require companies to substantiate environmental information with verifiable data. High-level estimates and generic claims are becoming much harder to defend.
Facility-level data gives brands the evidence needed to demonstrate compliance, substantiate product claims, and respond to regulatory requirements without relying on generalized assumptions or supplier declarations.
Just as importantly, standardized primary data reduces duplication across compliance programs. Once verified, data is collected and structured correctly; it can support multiple regulatory requirements, supplier engagement programs, risk assessments and reporting obligations simultaneously.
It also lightens the load on suppliers. They submit data once, update it only when something materially changes, and that same data flows across every program and regulation—instead of answering the same questionnaire ten times.
That is ultimately where platforms like TrusTrace create value: transforming fragmented supplier information into a standardized, trusted foundation that brands can use across compliance, sustainability, sourcing, and risk management.
S.J.: Looking forward, if a brand wants to act on this data today, what does the ideal collaboration look like between a manufacturer like Impetus, a data platform like TrusTrace, and the brand’s own sustainability team to actually scale these reductions?
T.P.: The first step is the will to be transparent. Manufacturers like Impetus need to collect and share primary data from the production floor—measuring actual resource consumption, understanding process performance, and identifying where improvements can be made. Once that visibility exists, meaningful collaboration becomes possible.
From there, a platform like TrusTrace needs to link the brand’s data requests with the impact data of the final product. A third party may also be needed to certify the quality of the data.
To measure impact reductions across a brand’s supply chain, data is crucial—and the way it is managed, compared, and shared between systems is the key.
S.G.: From the platform side, the goal is to create a verified data foundation that powers every program a brand runs, from due diligence and compliance to sourcing, risk management, and product-level transparency.
The ideal model is one where manufacturers provide verified primary data, platforms structure and connect that information at scale, and brands use those insights to inform sourcing, product design, supplier engagement, and compliance programs.
In practice, that looks like a brand’s sustainability team being able to ask—”Show me the dyeing facilities in Bangladesh,” or “Which upstream suppliers in my chain pass through Italy”—and get a verified, structured answer in minutes. AI within the platform connects the data points, so the human conversation can focus on what to do about it rather than whether the data is right.
The brands that build this foundation now won’t be scrambling when new requirements, like DPP, arrive. They’ll already be operating on it.
