Glossary

Unmet demand that carries forward across periods. When backlog is enabled, the solver creates backlog variables instead of declaring infeasibility when demand exceeds capacity. A penalty rate controls how aggressively the solver avoids backlogs.

The current reference scenario. When you compare scenarios, changes are measured relative to the baseline. You can promote any scenario to become the new baseline.

An edge that is either fully on or fully off — the solver treats it as a 0/1 integer variable. Use binary decisions for on/off choices like "open this line" or "use this supplier."

A constraint that is fully used up in the optimal solution. It has zero slack. Relaxing it can improve the objective; marginal value indicates the local effect. A non-binding constraint usually has no immediate effect.

A binding constraint with high impact on the objective. SSPLAX surfaces bottlenecks through the binding-constraint callout, most-sensitive-drivers ranking, marginal values, and pressure tests.

A constraint limiting total CO₂ emissions across the network. Each edge can carry a carbon rate (tCO₂ per unit of flow).

The tonnes of CO₂ emitted per unit of flow on an edge. Feeds into carbon cap constraints and the carbon minimization objective.

A plain-language warning about input documentation quality. Caveats flag missing sources, low input confidence, placeholder values, or missing review bounds.

What flows through edges. Usually "throughput" but can be any named quantity. The objective and constraints reference commodities.

A rating (High, Medium, Low, Unspecified) indicating how reliable an input value is. It is not solver confidence in the answer.

A limit on the system: a budget cap, capacity ceiling, minimum delivery, or resource limit.

A time pattern applied to a constraint across periods. Choose Flat (constant), Growth (compound percent change), Step (jumps at a period), Ramp (linear interpolation to an end value), or Custom (manual per-period values). The schedule generates period-by-period values automatically.

USD per unit of flow on an edge. When minimizing cost, the solver routes flow through the cheapest paths. Cost rates also feed into budget constraints.

Piecewise-linear cost curve on an edge. Each segment defines a breakpoint and cost rate. The solver picks the optimal volume on each segment.

A structured summary of the baseline answer, next move, active limits, scenario evidence, robustness evidence, input documentation, and caveats. Designed for sharing with stakeholders.

A 2D sweep over two constraints. Each point is colored by its active-limit region, with transitions and infeasible points called out. A selected region can be compared in Scenario B.

A narrative explanation of why the solver chose one plan over another, including which constraints and flows drove the choice.

A flow path between two nodes. Carries a commodity with min/max bounds, cost rate, carbon rate, yield ratio, and optional resource consumption.

A model is feasible when at least one plan satisfies all constraints simultaneously. Infeasible means the constraints contradict each other.

The quantity moving through an edge in the optimal solution. The solver decides flow values to meet the objective while respecting constraints.

The solved network visualization in the Answer tab. It shows path volume, capacity saturation, direction, yield, and resource totals for the baseline answer.

Synonym for slack — how much room is left before a non-binding constraint becomes binding.

Cost per unit per period for keeping inventory in a queue node. When inventory economics is enabled, the solver adds holding costs to the objective, discouraging excessive stockpiling.

A natural-language what-if question that SSPLAX translates into proposed constraint and edge changes, previews for review, then stages in Scenario B.

A 0-100 sensitivity score showing how strongly the result depends on a constraint. Input documentation fields such as input confidence, source, and value type are shown separately and used to flag validation priorities.

No plan can satisfy all constraints. SSPLAX computes the minimum relaxation: ranked single-limit relaxations that could restore feasibility.

Holding costs and storage caps on queue node inventories. When enabled, the solver penalizes stockpiling and respects storage limits, producing plans that balance early production against carrying cost.

A staged Scenario B change from the intervention library. Interventions adjust constraints or edge settings so you can compare the outcome against the baseline.

Synonym for shadow price — how much the objective improves per unit of constraint relaxation.

When a model is infeasible, ranked single-limit relaxations that could restore feasibility. Each option shows one constraint that could be changed, by how much, and in which direction.

The Drivers tab ranking of constraints by leverage. It uses pressure-test results when available, or marginal value as a fallback.

Solving the same network across multiple time periods with constraint schedules, demand growth, ramp rates, inventory carry-over, backlog / unmet demand, and inventory economics between periods.

A stage in your system — a source, service, queue, or sink. Nodes are connected by edges.

What you want to optimize: maximize throughput, minimize cost, or minimize resource usage. The solver finds an optimal feasible plan for the chosen objective.

The set of efficient tradeoff points between two competing objectives. Each point on the curve represents a plan where you can't improve one objective without worsening the other.

An input value type indicating the number is a rough guess that needs validation before the model can support a real decision.

A normalized sensitivity metric (0-100). Measures how much the objective drops when a constraint is tightened under stress. Higher means more sensitive.

A robustness check that tightens one limit at a time by the selected stress level and re-solves. It shows whether the answer stays stable, loses objective value, or becomes infeasible.

Optional low and high values recorded for input review. They document a plausible validation range but do not change the baseline solve or stochastic sampling.

A region in parameter space where the same set of constraints is binding and the solver recommends the same structural plan. Regime boundaries are where small changes flip the strategy.

A custom consumable attached to edges (FTEs, GPU hours, water, energy). Total consumption can be capped with a resource_limit constraint.

The staged comparison scenario in the Compare tab. It can come from presets, manual changes, interventions, a selected decision-map region, or a plain-language hypothesis.

How much the objective changes per one-unit relaxation of a constraint. A high value can indicate a limit worth reviewing. It is only valid within a range.

A node where flow exits the system. Represents delivered output, final destination, or completed work.

How much room is left before a non-binding constraint becomes binding. Zero slack means the constraint is active.

A node where flow enters the system. Represents raw material, inbound demand, or the starting point of a process.

Maximum inventory a queue node can hold in any period. Set as "Max storage" in the model editor or overridden per scenario on the Constraints screen.

Monte Carlo simulation that draws constraint values from uncertainty ranges and checks how often the sampled scenarios remain feasible. Reports a feasibility rate and which constraints cause failures.

The structural definition of a model: nodes, edges, constraints, objectives, and resources. Templates can be starter templates (pre-built) or custom.

A chart showing how the objective changes as you sweep a single constraint from tight to loose. Shows the marginal return of relaxation.

The range of constraint values over which a shadow price holds. Beyond this range, a different constraint becomes binding and the shadow price changes.

The fraction of input that becomes output on an edge. A yield ratio of 0.72 means 28% is lost. Output = input × yield.