Designing for cost and PCF isn’t about better formulas—it’s about making manufacturing assumptions explicit, structured, and reusable. Dessia contextualizes assemblies so teams can compare variants consistently and turn carbon into an early, decision-grade engineering signal.
Cost and carbon are increasingly managed together in industrial programs. Yet in many organizations, both are still treated as late checkpoints: cost is validated once designs are mature, and PCF is assembled when reporting or customer requests make it unavoidable.
The problem is rarely a lack of tools or formulas. The real bottleneck is that design and production assumptions are often implicit, scattered, or inconsistent across teams. Without a clear and structured description of “how this product is made,” cost and PCF become fragile: hard to reproduce, hard to compare, and difficult to use for decision-making early in the lifecycle.
This is where Dessia fits: not as an isolated carbon calculator, but as a structuring and contextualization layer for design information, enabling PCF evaluation to be performed consistently afterwards.
A product is not just geometry or a BOM. To connect design decisions to cost and environmental impact, each part and sub-assembly needs context that describes how it is intended to be produced.
The goal is to explicitly capture, for each component:
When this context is missing or inconsistent, PCF work becomes a series of ad hoc assumptions. Results can vary depending on who runs the evaluation, and comparisons between variants lose credibility.
In this framing, you are not doing “a one-off carbon calculation.”
You define a way of designing — rules, choices, assumptions, and modeling conventions — that allows a PCF score (or result) to be derived in a repeatable and consistent way.
That distinction matters. A single number is not enough if you can’t reproduce it, explain what assumptions created it, or compare options on a stable basis. By formalizing the underlying logic, carbon assessment becomes part of the engineering method — not a separate reporting exercise.
The same principle applies to cost: consistent evaluation depends on making the underlying assumptions explicit.
“Generative PCF” means the workflow goes beyond documenting an existing design. It enables teams to explore design variants while keeping the information structure compatible with PCF assessment.
For example, teams can evaluate scenarios such as:
The key is not just exploring alternatives — it’s doing so with a consistent structure, so evaluations remain comparable across variants and across programs.
Dessia acts as the layer that:
As a result, PCF is no longer an isolated task performed “on the side.” It becomes a logical outcome of a structured design approach.
Once this layer is in place, teams gain a much more operational way to handle cost and carbon:
This also supports lifecycle decisions beyond initial design—reuse, variant updates, material/process changes, and obsolescence responses—because the logic remains explicit and reusable.
The future of cost and PCF management is not “more calculations.” It’s more structure.
When “how it’s made” remains implicit, both cost and PCF arrive late, vary across teams, and are difficult to compare. By making production logic explicit at the part and sub-assembly level — processes, materials, rules, and assumptions — Dessia enables a workflow where PCF evaluation becomes repeatable, comparable, and compatible with design exploration.
Published on
12.01.2026
These articles may be of interest to you
Design optimization
Designing for cost and PCF isn’t about better formulas—it’s about making manufacturing assumptions explicit, structured, and reusable. Dessia contextualizes assemblies so teams can compare variants consistently and turn carbon into an early, decision-grade engineering signal.
6 min reading
Design optimization
Spreadsheets can score impact—but they can’t keep up with real design iteration. Dessia contextualizes sustainability logic into a model you can compare, explain, and reuse.
7 min reading
Design optimization
AI empowers engineering teams to automate design tasks, validate complex rules, and explore better architectures—helping manufacturers move faster and smarter.
7 min reading