From one-off calculations to repeatable footprint workflows
Reducing manual work is not only about saving time. It is about making product footprint data easier to maintain, reuse and trust. When calculations are locked in separate spreadsheets or consultant files, every update becomes a small project. Pickler helps create a more repeatable workflow around product impact data.
Import and structure product data
Teams can start by importing product data through spreadsheets or connecting product systems through the API. This makes it easier to work with many products at once instead of handling every footprint as a separate file.
- Bulk product data import for many SKUs.
- Structured fields for materials, weights, dimensions and product details.
- Reusable product records that can be updated over time.
- Cleaner handover between sustainability, product, data and commercial teams.
Map inputs to impact data
Pickler helps connect product inputs to environmental impact data using consistent mappings, calculation rules and secondary lifecycle data. This helps teams avoid rebuilding the same logic each time a product, customer request or reporting need appears.
- Material and product input mapping.
- IDEMAT lifecycle data as a consistent secondary data source.
- Transparent assumptions when perfect data is not available yet.
- Calculation logic that can be reused across products and scenarios.
Keep outputs ready for the next request
Once the product impact data is structured, teams can reuse it for reports, exports, comparisons, passports and customer answers. That means the same data foundation can support multiple workflows without starting from scratch every time.
- Export data for internal reporting or BI tools.
- Use product impact results in customer-facing outputs.
- Update calculations when product data, assumptions or data sources change.
- Improve product data quality over time as better supplier or primary data becomes available.
For sustainability teams, this creates a more practical way to scale footprint work: less time spent managing files, and more time spent improving data quality, explaining results and acting on product impact.
