Explore early biocomposite economics
Choose a product mission, adjust practical assumptions, and see how early scenario economics respond.
Look for boundary conditions—not a universal verdict. A promising case needs market value, industrial yield and realistic utilisation to align with delivered cost.
- ValueAchievable price and demand
- RobustnessYield, utilisation and throughput
- EconomicsMaterials, processing and CAPEX
1 · Choose
Choose a product mission
Start with the product route closest to your commercial question.
2 · Discover
Find the conditions that could make this mission work.
Compound pellet supply
Change mission
3 · Explore
Mission assumptions
Move sliders to test the economic boundary.
Product boundary explanation appears here.
Mission relationship map
Current mission
Follow the main economic logic from inputs to screening outputs.
Process route
Application gates
Select a calculated result
Its displayed formula and model boundary will appear here.
Evidence check
Compare the live setting, a discovery-derived feasible setting, and available sourced anchors. Proximity is not validation.
Discovery sensitivity
The strongest modeled directions from the latest all-factor run.
Run discovery first to identify the most influential assumptions.
Model conventions and excluded cash-flow items
Every economically active assumption is shown as a slider above. Comparison-only references are marked and excluded from sensitivity sampling.
TaxesNot modeled
Inflation and escalation0%; constant nominal annual values
Working capitalNot modeled
Debt and financing structureNot modeled
Residual or salvage value0
Production rampRepresented only by utilization
Cash-flow timingAnnual, end of year
Minimum-output safeguard1 kg/year to prevent division by zero
kg-based missionsFunctional unit mass fixed at 1 kg by definition
Non-take-back missionsReturn rate and collection cost fixed at zero as non-applicable
4 · Validate
What still needs evidence?
Annual operating cost
Which assumptions matter most?
One-at-a-time movement around the current sandbox setting.
Show the screening formulas
5 · Save, share or respond
Could your company use this?
Save the scenario with a key, print a short report, or tell us which evidence would make the next pilot useful.
2 · Discover
Discover feasible conditions
Explore the mission’s full assumption space before editing individual sliders. The run varies every unlocked economic input together and summarizes where positive economics appear.
Preparing the current mission scan
Current simulation mission: Preparing mission
Framework coverage: Preparing factor list
Ready to calculate.
This is deterministic stratified sensitivity sampling—not an exhaustive enumeration, probability forecast, optimizer, or investment model. Every sampled case varies all unlocked economically active sliders together; locked sliders remain fixed at the displayed company-known value. Comparison-only references are visibly marked and excluded because they do not enter NPV. For each varied factor, 0% and 100% are the allowed endpoints and 50% is the selected mission’s starting value. Unlocked composition shares are normalized around any locked shares so the total remains 100%. Calculations run in short batches so the page remains usable; the run can be cancelled at any time. “Financially positive” means positive NPV and positive gross margin only inside this simplified model and does not clear technical, regulatory, market, environmental, or warranty gates.
Variation scan results
Conditions around the current case
Full-framework economic landscape
Rows are the unlocked factors, ranked by modeled influence. Start with the positive-case view, then select up to three range boxes as A, B and C to find where feasible cases overlap.
Economic outcomes across sampled scenarios
The distribution shows how many sampled discovery cases fall into each NPV range. Red bars are below zero; green bars are above zero.
Which factor directions help?
Difference between mean NPV in the upper 20% and lower 20% of each slider range while every other factor also varies.
Higher values tend to helpLower values tend to helpWeak direction
How to read these views: the landscape shows where better and worse economics concentrate across every factor range; the direction chart summarizes whether higher or lower values tend to help; the distribution shows how common positive and negative modeled outcomes were. These are associations within the sampled design space, not causal proof or real-world likelihood.
Scenario comparison
Four distinct views of the sampled design space. “Best sampled” may depend on extreme settings and is not a recommendation.
| Scenario | NPV | Margin | Evidence proximity | Use |
|---|
What would make this work?
The strongest positive-case concentration across all active factors. Association inside this model does not establish causation.