Summary (TL;DR) of what this post covers:
- This post covers the 11 most common product footprint mistakes manufacturers make on LCA, PCF, and EPD projects – and how to avoid each one, drawn from real project experience working with construction products and industrial manufacturers.
- When something goes wrong on an LCA, PCF, EPD or any other product footprint project, it’s rarely the methodology. More often, it’s the setup – unclear goals, data collection scoped too tight, timelines that didn’t account for dependencies.
- If you’re wondering what to consider before starting a product life cycle assessment (LCA), it’s planning and structure. Most mistakes when calculating your product’s footprint are about how the project is scoped, resourced, and run.
What know one tells you before your first product footprint project
Running a product footprinting project for the first time, whether the goal is an LCA, a PCF, or a verified EPD, usually starts with a request and a deadline. A customer asks for environmental data. A tender requires an EPD. A new regulation comes into force. Suddenly, someone on the sustainability team is responsible for delivering.
It actually isn’t the LCA methodology that makes the first project so challenging – there’s enough documentation and direction out there. It’s everything around it. It’s the unclear scope, the scattered data, the stakeholders who don’t yet know they’re stakeholders, and a timeline set before anyone understands what the project entails.
As Emma Thunnissen, our Sustainability Expert, describes it: “When companies are doing this for the first time, they don’t really know where to start. They have a tight deadline that is probably not realistic, and they also don’t really know what data to collect or what deliverable they need. And it’s kind of difficult to start. And this can also cause a lot of stress.”
The good news: most of that stress is avoidable by knowing what tends to go wrong before it does (and what to do instead). That’s what this post covers.
This list is operational. It’s about how projects get planned, resourced, and run. We share insights from a recent episode of our Behind the Product Impact video series, with Emma Thunnissen, Sustainability Expert at Ecochain, and Artjom, Sustainability Business Expert at Ecochain, where they walk through what it takes to run a product footprinting initiative from start to finish.
| Note: This post isn’t about LCA methodological mistakes. You can read another article to learn about those: 10 common LCA mistakes and how to avoid them. |
The 11 most common product footprinting mistakes
Mistake 1: Starting without a clear business goal
The most expensive mistake in any product footprint project isn’t a data error or a methodology choice. It’s starting without a clear answer to why my company needs this and what the output will actually be used for.
This sounds obvious. In practice, companies skip this often. There’s a pressure to start, and clarifying the goal feels like it’ll slow progress.
Artjom, our LCA expert, sees this repeatedly: “It’s really important to nail down the fundamental question. Why does your company need it? Are you doing it because a customer is demanding it? Because you’re applying for B Corp? Are you doing it to reduce your impact? These are different use cases, and each one will have its own specifics.”
How to avoid this mistake:
- Write down the business goal in one sentence and confirm it with whoever owns the request before scoping anything.
- Ask: what decision or requirement will this output serve, and who will use it?
- If you feel like the ask is uncertain and goals might shift, build checkpoints into the project. During these checkpoints, ask: does this still address the original goal?
Mistake 2: Picking the wrong output for the request
This mistake is closely related to mistake 1, but worth separating because it does have distinct ramifications. Teams that know why they need environmental data still sometimes produce the wrong format for the actual request.
In another episode of our Behind the Product Impact series, Dr. Pratik Gholkar, our Sustainability Strategist, shared something we see often:“A sustainability manager phoned and said: ‘I need to have this carbon footprint ready, published on an EPD program operator platform tomorrow.’ And I said: ‘Wait. What is your business requirement?’ And he said: ‘We are going for tendering.’ And I said: ‘You might be needing EPDs, not just the carbon footprint.'”
How to avoid this mistake:
- Connect the request to the audience. Ask: is this going externally (customer, tender, regulator) or staying internal (R&D, eco-design, benchmarking)?
- A verified EPD aligned with EN 15804+A2 (not just a carbon footprint) is typically required for construction tenders and CPR compliance.
- For full guidance on matching requests to outputs, see: EPD, PCF, LCA or DPP: how to choose the right product footprint output for your business needs
Mistake 3: Underestimating data collection – in scope, time, and complexity
Data collection is where most product footprint projects lose time. This isn’t because teams lack initiative, but because the disparity between “we have data” and “we have data structured for LCA” is actually more relevant than expected.
Emma says: “What we see a lot of the time in practice is that people start collecting data without a clear plan. They’re not entirely sure what they need. And then they need to figure it out as they go. And that costs them a lot of time – because things are not complete and you don’t know where to find them.”
Teams mostly have data on their Bill of Materials (BOM). However, data on energy consumption per production stage, waste fractions in manufacturing, and transport data from specific suppliers is hard-to-find or incomplete. It can be scattered across systems, people, and sometimes paper records.
Even more so, data that is good enough for operational purposes often has errors that only become visible when used for LCA. As Emma describes: “Usually, for example, with the Bill of Materials for most customers, when we start a project, we check the data, and then there are issues in that data. It just usually happens.”
How to avoid this mistake:
- Run a data audit before the project starts. Understand what you have, where it lives, and what’s missing.
- Build a data collection checklist specific to the output you need. Don’t be generic.
- Watch: Before you start product footprinting: 5 steps to prepare your data foundation
| Continue reading: How to build a scalable product footprint data foundation – a step-by-step guide for sustainability teams in manufacturing |
Mistake 4: Treating data collection as a one-person task
When one person tries to collect all the product footprint data alone, two things tend to happen. The project slows down. And assumptions are made when actual data exists (but is living with someone who was never asked).
Even when one person owns the project, product footprinting data often covers many individuals and departments. It’s spread across product teams, procurement, operations, and sometimes individual factories.
Emma adds: “The product footprint projects are usually not a project that you do on your own. They’re a project that you do, hopefully, with a lot of people. Because it’s about the whole product and the whole life cycle of the product. So it will take other people that know about certain parts of those life cycles.”
How to avoid this mistake:
- Map your stakeholder list early. Ask: “Who owns the data? Who knows the production process? Who manages the supplier relationships?”
- Connect with relevant stakeholders before data collection starts and make sure they are properly briefed. Try not to wait for the last minute when you need something from them.
- Frame it as a cross-functional project from day one so they know its relevance beyond a sustainability team project.
Mistake 5: Waiting for perfectly complete data before starting
The other common mistake with collecting footprinting data is waiting until everything is complete before starting modeling. In reality, perfect data is hardly ever achieved. If you wait for it, your project will never start.
Artjom, as Sustainability Business Expert, speaks with sustainability teams every day: “What I know from experience is that this can take a couple of weeks. What we often do in projects is we work in iterations. When a first batch of data comes in, we can already kind of start modeling while collecting the other data that is also important, but not fundamental to getting started.”
This iterative approach doesn’t mean you’re compromising on quality. It creates natural checkpoints where data quality can be improved progressively. Plus, it’s common practice among professionals in this space.
How to avoid this mistake:
- Begin modeling with the data you have, document where assumptions are being made, and improve them as better data arrives.
- Apply the 95/5 principle. 95% of the data should be reliable and product-specific; 5% can be covered by documented assumptions where gaps exist.
- See: How to build a scalable product footprint data foundation.
Mistake 6: Not checking the PCR status before starting (if your output follows one)
Product Category Rules (PCRs) apply to most EPDs and some PCFs. They define how you model your product for that output (e.g., system boundaries, allocation methods, and mandatory modules). If your project is one of these, it’s worth knowing PCRs aren’t static. They get revised.
Artjom elaborates: “Something like a PCR can be updated frequently, sometimes multiple times a year if you’re unlucky. But luckily, a lot of the program operators that publish these PCRs will transparently say on their website: Hey, this one is being currently revised. We’re expecting a new publication by this and this time.”
Starting a project under a PCR that’s about to be revised can mean significant rework. Checking the current status is quick and saves a lot of time.
How to avoid this mistake:
- Identify the exact product category and program operator.
- Check the relevant EPD program operator’s website, such as IBU, EPD International, EPD Global, or MRPI, for the current PCR version and status before starting. Ask: is it under revision? What’s the expected publication date?
- If a revision is imminent, factor that into the project timeline and scope decisions.
- Lock the PCR version into the project plan before modeling begins.
- When in doubt, get advice from an LCA expert who works regularly with the relevant program operator.
Mistake 7: Setting an unrealistic timeline, or no timeline at all
Setting accurate product footprint timelines can be a real challenge for many project teams. Data collection is often delayed (Mistake 4 and 5). It may contain errors. Verifiers have capacity constraints. PCRs get revised (Mistake 6). These challenges frequently come up, but are challenging to predict until you’ve been through the process.
Artjom suggests asking: “What is the final deadline where your management expects a result?” He continues: “Do some form of backward engineering – okay, that’s by when I need the results. How and when do I need to do the different milestones to get to that end result? And if you already see issues and contingencies, make sure to communicate about those with the relevant people.”
Emma adds something key: “Definitely also build in some slack. Because expect the unexpected. There will also be things that you can’t check upfront and that you just won’t know.”
How to avoid this mistake:
- Work backward from the deadline. Ask: what are the milestones, and what does each one depend on?
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Build slack into the schedule for data clean-up, LCA modelling, verifier feedback, and PCR changes.
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Include time for one or more review rounds with internal stakeholders and external verifiers.
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Set an early decision gate for scope changes, so you can still adjust before the final modeling phase.
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Revisit the timeline at each milestone and update it when data, PCRs, or verifier availability shift.
Mistake 8: Waiting until the end to find a verifier
For outputs that require third-party verification, finding and engaging a verifier should never be a final step, especially for EPDs. It should happen early in the project, before modeling is complete.
Artjom explains: “[Customers] tend to reach out to a verifier quite early on in the project for two main reasons. One is to find a verifier that has experience with your product and knows what they’re doing. Two, figure out whether this verifier even has capacity and time to do your verification. Because if you go to the verifier on the day where you need the verification, the odds are they’re not going to be able to do it – because the demand is increasing but the verifiers are quite limited in how many there are.”
How to avoid this mistake:
- Identify relevant verifiers for your product category and program operator before the project starts.
- Make initial contact early. Try to reach out during the data collection or early modeling phase.
- Factor verifier availability into the timeline (Mistake 7).
- Learn more about environmental data verification.
| Continue reading: See how Ecochain helped Van Wijhe Verf scale their product sustainability program and turn product footprint data into innovation |
Mistake 9: Not documenting assumptions as you go
A product footprint project can run for several months, sometimes six or more. The decisions made in week two about system boundaries, allocation methods, or which dataset to use must be explained once verification starts. By then, that early reasoning can feel like a long time ago.
“Documentation and noting down assumptions – any potential changes – is an ongoing thing throughout the whole project. A project can last sometimes a few months. And towards the end when you’re writing that report, you may not remember everything that happened at the start of the project. If you document and write things down throughout the process, your future self will thank you for it.” says Artjom.
If you skip documenting assumptions, you’ll need to do some serious detective work to understand decisions you made months earlier. This will likely cause unnecessary delays and stress during the reporting and verification phases.
How to avoid this mistake:
- Keep a living document throughout the project. Document every assumption, dataset choice, other changes, and why you made them.
- Documentation also makes onboarding new team members or handing the project off easier.
Mistake 10: Treating the first project as a one-off
A project built with reuse in mind produces a foundation that makes the next EPD, the next PCF, the next regulatory requirement faster, cheaper, and less stressful.
The most common framing for a first product footprint project: “we have this request, we’ll do it once, and that’s done.” With this mindset, you’ll produce a dataset and a model, and that’s it. In reality, you won’t get a footprinting request just once.
Emma describes what actually happens: “Sometimes companies are like, okay, we have this request now, we’re gonna do this once. But you’re going to do this many more times. This is coming, and we also see this with our customers. It’s almost always not a one-off.” And, regulations are here to stay.
How to avoid this mistake:
- Structure data collection, modeling, and documentation so it can be updated and extended.
- Choose LCA automation software and approaches that support reuse across products, variants, and sites.
Mistake 11: Expecting the first result to be the final answer
The first iteration of a product footprint model is a starting point. The learning embedded in the first project (e.g., where the data lives, what the gaps are, what the modeling decisions mean) is what makes subsequent projects faster.
Artjom puts this well: “That’s one way I like to look at LCA in general – it’s an iterative process. It’s like when making a painting. The first sketch you make is not the final end result. Over time, it becomes the final piece.”
Emma adds the practical upside of embracing this: “The first time you calculate the impact of your products doesn’t necessarily need to be perfect. I think the goal of doing such a project for the first time should also be that you understand how to do it next time. To learn. Because I think that sometimes companies are like, okay, we have this request – but you’re going to do this many more times.”
How to avoid this mistake:
- Set expectations internally. The first result is just the first version.
- Use the first project to learn the data landscape, understand the gaps, and build the foundation for iteration.
- Resist the shortcut of AI-powered or fully automated tools that skip the learning entirely. The understanding you build in the first project is an asset.
How Ecochain helps you avoid these common LCA traps from day one
Most of the mistakes in this list share a root cause. They all stem from doing product footprinting without the right structure in place.
Reading this list before kicking off your next product footprint project is a great start. The teams that move fastest through their product footprinting spend the time to create structure. They set the goal clearly, map the dependencies properly, and build for reuse from day one.
Ecochain’s LCA automation software is built around the principle that the foundation should be set up once, done well, and reused. You don’t have to rebuild every time a new request arrives. This means:
- A structured data foundation: Data collection has a clear target, and gaps are visible before they slow the project down.
- Iterative LCA modeling: Start modeling with available data and improve progressively.
- Documentation built into the workflow: Assumptions are captured during each step of the modeling process.
- Reusable models: The first model becomes the foundation for the next one.
- Expert support alongside the software: Sustainability and product teams don’t have to navigate LCA methodology, PCR interpretation, or verification preparation alone.
The combination of software and LCA expertise on-demand is what makes the difference between a delayed, lengthy project and one that delivers.
If you want to talk through what the right starting point looks like for your specific products and markets, get in touch with us.
Frequently Asked Questions
What are the most common mistakes in a first product footprint project?
The most common mistakes in a first LCA, EPD or any other product footprinting project are operational, not technical. Starting without a clear business goal, underestimating how long data collection takes, treating it as a solo task when it requires cross-functional input, and building for a one-off request rather than a reusable foundation – these patterns appear across most first-time projects, regardless of industry or company size. LCA automation software like Ecochain helps you navigate this entire process and avoid the common traps.
Do I need perfect data before starting a product footprint project?
No, you don’t need perfect data before starting a product footprint project. A practical approach is to start modeling with the data you have, document where assumptions are made, and improve data quality iteratively. A useful rule of thumb: 95% of data should be reliable and product-specific; the remaining 5% can be covered by documented assumptions where genuine gaps exist.
When should I start looking for a verifier for an EPD project?
When planning a verified EPD, start looking for a verifier early – ideally during the data collection or early modeling phase. Verifier availability is a real constraint. Demand for third-party verification is growing, but the pool of qualified verifiers is limited. Leaving it until the end is one of the most common reasons product footprint projects miss their deadline.
Is a product footprint project something one person can run alone?
A product footprint project is rarely something one person can run alone in practice. The data required spans multiple functions – procurement, product, operations, and sometimes individual suppliers or factories. One person can own and drive the project, but identifying the relevant internal stakeholders and briefing them before data collection starts makes a significant difference to how smoothly it runs.
What should I check before starting a product footprint project that requires a PCR?
Before starting a product footprint project that follows a Product Category Rule (PCR) – as most EPDs do – check the current status of the relevant PCR on the program operator’s website (IBU, EPD International, MRPI, etc.). PCRs can be revised, sometimes more than once a year. Starting under a PCR that’s about to change can mean significant rework. If a revision is imminent, factor that into your timeline and scope decisions before modeling begins.
How long does a product footprint project take?
How long a product footprint project takes depends on several factors: how complex your product is, how accessible your data is, whether your output requires third-party verification, and how well the project is structured from the start. Data collection alone can take weeks. Verifier availability adds further lead time.
What’s the difference between LCA methodology mistakes and footprinting project management mistakes?
LCA methodology mistakes are almost always technical (e.g., wrong system boundaries, incorrect dataset use, misapplied standards). Footprinting project management mistakes are most often operational (e.g., unclear goals, underestimated timelines, missing documentation, treating the project as a one-off). Both matter, but they need different fixes. For LCA methodology mistakes, see: 10 common LCA mistakes and how to avoid them.
How do I make sure my first product footprint project sets me up for future ones?
The best way to make sure your first product footprint project sets you up for future ones is to treat it as a foundation, not a one-off. Structure data collection so it can be updated rather than rebuilt. Document assumptions and decisions throughout. Choose LCA automation software like Ecochain that supports reuse across products, variants, and sites. The understanding you build in the first project – where the data lives, what the gaps are, what the modeling decisions mean – is what makes every subsequent project faster and less stressful.
