IN SUMMARY
Why supplier data collection breaks down, and how to fix it.
Why supplier data collection breaks down, and how to fix it.
Most LCA programmes stall at the same point: the supplier data request. Teams either ask for too much and get nothing back, or collect data with no governance and can’t defend it under review.
Most LCA programmes stall at the same point: the supplier data request. Teams either ask for too much and get nothing back, or collect data with no governance and can’t defend it under review.
Most LCA programmes stall at the same point: the supplier data request. Teams either ask for too much and get nothing back, or collect data with no governance and can’t defend it under review.
The most common failure is scope creep, asking suppliers for every data point upfront rather than starting with a minimum viable request and targeting quality improvements only where they change the result.
The most common failure is scope creep, asking suppliers for every data point upfront rather than starting with a minimum viable request and targeting quality improvements only where they change the result.
The most common failure is scope creep, asking suppliers for every data point upfront rather than starting with a minimum viable request and targeting quality improvements only where they change the result.
Data collected without a submission and approval trail cannot be defended under third-party critical review, regardless of its quality. Governance needs to be defined before collection begins, not after.
Data collected without a submission and approval trail cannot be defended under third-party critical review, regardless of its quality. Governance needs to be defined before collection begins, not after.
Data collected without a submission and approval trail cannot be defended under third-party critical review, regardless of its quality. Governance needs to be defined before collection begins, not after.
Supplier data collected for one reporting cycle is routinely rebuilt from scratch for the next. Naming conventions, storage structure, and mapping logic determine whether data is reusable or disposable.
Supplier data collected for one reporting cycle is routinely rebuilt from scratch for the next. Naming conventions, storage structure, and mapping logic determine whether data is reusable or disposable.
Supplier data collected for one reporting cycle is routinely rebuilt from scratch for the next. Naming conventions, storage structure, and mapping logic determine whether data is reusable or disposable.
Why supplier data is the real bottleneck in LCA
After helping teams build supplier data programmes across battery and critical mineral supply chains, one pattern keeps appearing: the model is rarely the constraint. The data is. A life cycle assessment is only as credible as the primary data behind it. For the stages that matter most — battery manufacturing, cathode and anode precursor production, metal refining — secondary database values aren’t enough to satisfy an OEM customer, a third-party critical reviewer, or the EU Battery Regulation. You need site-specific data from the companies actually running those processes. And that is where most programmes stall.
After helping teams build supplier data programmes across battery and critical mineral supply chains, one pattern keeps appearing: the model is rarely the constraint. The data is. A life cycle assessment is only as credible as the primary data behind it. For the stages that matter most — battery manufacturing, cathode and anode precursor production, metal refining — secondary database values aren’t enough to satisfy an OEM customer, a third-party critical reviewer, or the EU Battery Regulation. You need site-specific data from the companies actually running those processes. And that is where most programmes stall.
The three failures that stall most programmes
Asking for too much, too early. The instinct is to send suppliers a comprehensive data request covering every input and output. Response rates collapse. Suppliers see a forty-field spreadsheet and deprioritise it. A focused, staged request gets answered; an exhaustive one gets ignored.
Collecting data with no governance. Data that arrives by email, gets pasted into a model, and has no record of who submitted it or when cannot be defended. When a reviewer asks where a number came from, “a supplier sent it last year” is not an answer that survives critical review.
Building for one cycle instead of many. Most teams treat supplier data collection as a one-off exercise tied to a single report. The next reporting cycle arrives and the same data is requested, reformatted, and re-modelled from scratch — because nothing was structured for reuse.
Asking for too much, too early. The instinct is to send suppliers a comprehensive data request covering every input and output. Response rates collapse. Suppliers see a forty-field spreadsheet and deprioritise it. A focused, staged request gets answered; an exhaustive one gets ignored.
Collecting data with no governance. Data that arrives by email, gets pasted into a model, and has no record of who submitted it or when cannot be defended. When a reviewer asks where a number came from, “a supplier sent it last year” is not an answer that survives critical review.
Building for one cycle instead of many. Most teams treat supplier data collection as a one-off exercise tied to a single report. The next reporting cycle arrives and the same data is requested, reformatted, and re-modelled from scratch — because nothing was structured for reuse.
What a defensible programme actually looks like
The teams that get this right share a common structure. They assign clear internal ownership before contacting any supplier. They map what they already hold internally so they only request what they genuinely need. They start with a minimum viable data request and improve quality only where hotspot analysis shows it changes the result. They define submission, approval, and traceability rules upfront. And they structure naming and storage so the data collected today can be refreshed rather than rebuilt. None of this requires more resource than the ad hoc approach. It requires sequencing the same work in a way that produces data you can actually stand behind.
The teams that get this right share a common structure. They assign clear internal ownership before contacting any supplier. They map what they already hold internally so they only request what they genuinely need. They start with a minimum viable data request and improve quality only where hotspot analysis shows it changes the result. They define submission, approval, and traceability rules upfront. And they structure naming and storage so the data collected today can be refreshed rather than rebuilt. None of this requires more resource than the ad hoc approach. It requires sequencing the same work in a way that produces data you can actually stand behind.
Why this matters more under the EU Battery Regulation
From 2025, EV battery carbon footprint declarations became mandatory on the EU market, with Carbon Performance Classes following from August 2026. These declarations require primary data for the highest-impact supply chain stages, and they must survive third-party verification. A supplier data programme that was “good enough” for a voluntary sustainability report is not good enough for a regulated declaration scrutinised by an auditor. The bar has moved.
From 2025, EV battery carbon footprint declarations became mandatory on the EU market, with Carbon Performance Classes following from August 2026. These declarations require primary data for the highest-impact supply chain stages, and they must survive third-party verification. A supplier data programme that was “good enough” for a voluntary sustainability report is not good enough for a regulated declaration scrutinised by an auditor. The bar has moved.