Guide
Decision Workflow
The first solve gives you a baseline. The real work is exploring alternatives, testing robustness, and building confidence in the recommendation before you commit.
Scenario comparison
The Compare tab lets you run a second scenario alongside your baseline. Change one or more constraints, re-solve, and see both results side by side.
SSPLAX includes built-in scenario presets that adjust constraints in realistic ways:
Relaxes capacity constraints and tightens delivery requirements. "What if we push harder?"
Tightens budget constraints by a realistic percentage. "What happens if funding shrinks?"
Reduces capacity constraints and slightly raises budget. "What if a key supplier underdelivers?"
Manually adjust any constraint values for the comparison scenario.
The comparison shows the objective delta, flow diffs, and which constraints flipped between binding and non-binding. The decision trace explains why the recommendation changed.
Interventions
When the solver identifies bottlenecks or infeasibility, SSPLAX suggests interventions — specific one-click changes that would improve the outcome. Each intervention adjusts a constraint or flow path capacity and re-solves instantly so you can see the impact.
Available interventions depend on the constraint types in your model:
Raise a constrained operating capacity by 10%. Targets the top bottleneck or the first capacity constraint.
Increase available budget by 10%. Shows how much throughput the extra funding unlocks.
Reduce the minimum delivery requirement by 10%. Useful when the current target is infeasible.
Open 10% more capacity on a flow path. Tests whether a specific stage is the bottleneck.
Domain-specific templates may include additional interventions tailored to the scenario — for example, adding a purification shift, outsourcing a process step, or increasing bioreactor runs.
Click any intervention to apply it, then check the Compare tab to see whether the change is worth pursuing.
Promote a scenario to baseline
When you find a scenario that performs better, you can promote it to become the new baseline. This resets the comparison point so future scenarios are measured against your latest best plan.
Promoting is useful when you've iterated through several what-if changes and want to lock in the best configuration before exploring further.
Sensitivity summary
The sensitivity analysis gives you a ranked view of what matters most in your model:
Ranks all constraints by impact. Each bar shows how much the objective moves when that constraint is stressed. Longer bars = higher leverage. Based on both shadow prices and stress test results.
A 2D sweep over two constraints. Each point shows the optimal regime — which constraints bind and what plan the solver recommends. Regime boundaries show where small parameter changes flip the entire strategy.
When two objectives compete (e.g., throughput vs. cost), the Pareto frontier shows every efficient tradeoff point. Each point on the curve represents a plan where you can't improve one objective without worsening the other.
Monte Carlo sampling over your constraint uncertainty ranges. Shows the probability that the current plan stays feasible under real-world variation, and which constraints cause the most failures.
What to validate next
SSPLAX uses sensitivity results alongside assumption quality to tell you where to focus validation effort. The highest-priority items are constraints that:
- -Have high pressure scores (the model is sensitive to them)
- -Have low confidence or missing sources in the assumption ledger
- -Are the first to cause infeasibility under stress
See Assumptions & Validation for how to fill in sources, confidence, and ranges.
Hypothesis testing
Type a natural-language "what if" question and SSPLAX translates it into constraint and edge changes, re-solves, and shows you the result.
"What if yield improves to 80%?"
→ Interprets as: increase the yield assumption from 72% to 80%. Re-solves and shows throughput jumps from 18,400 to 24,100 units/mo, with capacity becoming the new bottleneck.
The system shows you its interpretation and any assumptions it made, so you can verify before acting on the result.
Build a decision brief
When you're ready to share findings, SSPLAX generates a structured decision packet that captures:
- -The recommended plan and objective value
- -Binding constraints and key bottlenecks
- -Scenario comparison results
- -Sensitivity rankings and assumption caveats
- -Narrative explanation of the decision logic
Export it or copy the narrative to share with stakeholders who don't need to see the model.