Guide
Modeling Playbooks
Step-by-step recipes for common decision tasks. Each playbook starts from a model in SSPLAX and walks through the moves to reach an answer.
Find a bottleneck
When to use: You want to know which constraint is preventing more throughput.
Open your model and solve with the default constraints.
Go to the Bottlenecks tab in Results. The tornado chart ranks all constraints by pressure score.
Click the top constraint. The tradeoff curve shows how much throughput you gain per unit of relaxation.
Check the shadow price validity range — beyond this range, a different constraint becomes the bottleneck.
If you want to confirm, run the stress test: tighten the top constraint by 5% and see the objective drop.
Look for: The constraint with the longest bar in the tornado chart and the highest shadow price. That's your bottleneck.
Compare two routes
When to use: You have two options (build vs. buy, supplier A vs. B, route X vs. Y) and want to know which is better under your constraints.
Model both routes as parallel paths from the same source to the same sink. Each path has its own cost, capacity, and yield.
Solve and check the flow split — the solver automatically routes flow through the better path.
To test whether the other route wins under different conditions, tighten the winning path's capacity or raise its cost.
Use the Pareto frontier (if one route is cheaper and the other has more throughput) to see the efficient tradeoff curve.
For a clean comparison, force one route on using binary overrides and compare the two objective values.
Look for: The flow split between parallel paths, and the Pareto curve if objectives conflict.
Allocate limited capacity
When to use: Multiple uses compete for the same resource (budget, headcount, machine time) and you need to decide how to split it.
Model each use as a separate edge consuming the shared resource. Add a resource limit constraint for the total pool.
Set minimum throughput requirements on any edges that must receive at least some allocation.
Solve. The optimal allocation maximizes total output given the shared limit.
Check shadow prices on the resource constraint — this tells you how much more output you'd get from one more unit of resource.
Run scenarios: what happens if the pool shrinks by 10%? Which uses get cut first?
Look for: The shadow price on the shared resource constraint and which edges are at their minimum vs. receiving more.
Test demand upside
When to use: You want to know whether your system can handle a surge in demand.
Start with current constraints and solve to get the baseline throughput.
Enable multi-period planning and set demand growth (e.g., 10% per period).
Solve and check which period first becomes infeasible or hits capacity.
Alternatively, for a quick check: increase the minimum throughput requirement and re-solve. The solver will tell you whether it's feasible and what the binding constraints are.
If infeasible, check the minimum relaxation — it shows which single constraint could be relaxed, and by how much, to meet the higher demand.
Look for: The period or demand level where the first constraint breaks, and the minimum relaxation to handle the upside.
Recover from a disruption
When to use: A constraint has shifted (a supplier dropped, a machine broke, yield crashed) and you need a recovery plan.
Update the affected constraint or edge to reflect the disruption (lower capacity, worse yield, lost supplier).
Solve. If the result is infeasible, check the minimum relaxation for what else could change.
If feasible but worse, check the Compare tab to see the flow shifts — which paths absorbed the impact.
Review suggested interventions — SSPLAX may suggest temporary measures (add a shift, switch to a backup supplier).
Run stochastic feasibility with the disrupted model to check whether the recovery plan is robust to further variation.
Look for: Feasibility status, the minimum relaxation if infeasible, and the intervention suggestions.
Decide what to expand
When to use: You have capital for one expansion (add a line, hire a team, buy a tool) and need to pick the highest-value investment.
Solve the model as-is and note the objective value.
For each candidate expansion, relax the corresponding constraint or increase the edge capacity by the expansion amount.
Re-solve for each expansion and record the new objective value.
The expansion with the largest objective improvement per dollar is the best investment.
Cross-check with the shadow prices — they should agree unless the expansion crosses a validity range boundary.
Look for: The objective improvement per expansion option. The shadow price gives you a first approximation; the full re-solve confirms it.
These playbooks cover the most common patterns. For model-building fundamentals, see Build a Model. For understanding results, see Read the Answer.