Eco-design contextualization for decision support

In many industrial organizations, sustainability starts with a pragmatic tool: a spreadsheet. It can capture rules, constants, and calculations well enough for a first assessment or a reporting need.

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The limitations appear when teams try to use sustainability during design decisions: inputs change, variants multiply, assumptions drift, and comparisons become harder to trust. Results exist, but the workflow becomes difficult to maintain and scale.

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This is where Dessia’s approach matters. Dessia focuses on contextualization: turning sustainability knowledge (rules, parameters, constants, scoring logic) into an explicit, structured model that can be reused consistently across alternatives and iterations.

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What “contextualization” means in this sustainability use case

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In practical terms, contextualization is a simple idea:

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It is the act of making environmental assessment logic explicit and structured, so it can be executed consistently and understood clearly—even when exploring many design alternatives.

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Concretely, contextualization ensures that four things are always clear:

• What defines a design alternative (the inputs)
• What stays fixed (constants, reference values)
• How the score is computed (rules, indicators, phases)
• What is assumed, unknown, or out of scope (limits and usage boundaries)

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When those elements are explicit, sustainability stops being “a spreadsheet someone owns” and becomes a reusable workflow.

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Why spreadsheets struggle when eco-design becomes iterative

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Spreadsheets fail not because they are “bad,” but because eco-design becomes complex very quickly.

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When teams iterate, they need to answer questions like:

• What changed between option A and option B?
• Which parameters drive the score the most?
• Which levers are actually controllable in real life?
• How do we explore alternatives without exploding into thousands of combinations?
• Can we explain why a solution is “better” beyond one final number?

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A spreadsheet can compute a number.

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Decision support requires more: structure, traceability, comparison, and repeatability.

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The backbone: alternatives built from parameters and constants

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Dessia structures each eco-design alternative around two elements: parameters (what defines the option) and constants (the governed reference values used by the rules).

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1) Parameters define the alternative

An alternative is described by inputs of two types:

• Discrete parameters: choices selected from a controlled list, ensuring consistent vocabulary.
• Continuous parameters: numeric values entered by users (with clear units/meaning), treated as variable or fixed depending on the analysis.

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2) Constants stabilize the rules

Constants are the reference values used to compute indicators (e.g., conversion factors). They are managed separately from user inputs—organized by category and can be updated without breaking the model.

This clear separation keeps the eco-design model maintainable and comparable across alternatives.

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Environmental scoring that is consistent and explainable

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The environmental evaluation is structured around two ideas: a defined set of global indicators (measurable outputs) and a breakdown across life phases, so results can be interpreted by stage—not only as a single aggregate score.

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This structure matters because decision-making requires explainability. A strong eco-design workflow should make it clear:

• which life phase contributes most to the result
• which indicators drive the outcome
• which input parameters are responsible for differences between alternatives

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With contextualization, this scoring logic remains consistent across all evaluated alternatives, which makes comparisons reliable and trade-offs easier to justify.

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Making eco-design scalable: from one scenario to many alternatives

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Eco-design becomes actionable when teams can compare multiple options—without turning the exercise into an endless spreadsheet of combinations. Dessia keeps exploration usable by adding structure and boundaries to the way alternatives are created and evaluated.

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• A controlled exploration scope

Instead of “try everything,” the exploration is framed so teams work with a realistic number of alternatives—enough to learn and compare, not so many that the process becomes unmanageable.

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• Focus on what drives the result

When many inputs could vary, the workflow prioritizes the variables that actually influence outcomes. This keeps the analysis centered on meaningful levers rather than noise.

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• Alternatives designed for comparison

The resulting set of alternatives is built to be comparable: each option is defined consistently, evaluated using the same logic, and presented in a way that makes trade-offs visible.

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This is what transforms eco-design from a one-off calculation into a repeatable decision workflow.

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What this approach changes for sustainability teams

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This contextualized workflow shifts sustainability from a late reporting activity to an earlier decision process by enabling:

• consistent evaluation across alternatives
• faster exploration without manual rebuild
• explicit assumptions and limits
• explainable trade-offs rather than opaque scores
• repeatability when the model evolves

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It does not rely on “one expert spreadsheet owner.”

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It becomes a structured model.

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What this approach changes for engineering teams

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For design teams, the value is also operational:

• inputs are structured and controlled
• exploration is bounded and configurable
• the outcome is interpretable (what changed, why it matters)
• the workflow is reusable across iterations

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This is how sustainability becomes compatible with real engineering cadence.

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Conclusion

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Eco-design becomes scalable when sustainability logic is contextualized as a model—not scattered across formulas and hidden assumptions.

• Clear alternatives: options are defined consistently, so comparisons stay fair.
• Governed references: constants and reference values remain controlled and maintainable.
• Consistent scoring: the same evaluation logic applies across iterations and variants.
• Decision-ready use: teams can compare options, explain differences, and iterate without rebuilding the model each time.

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This is how Dessia helps sustainability move from reporting to repeatable decision support during design.

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