Glossary

A scenario analysis that solves each scenario independently, letting anything marked adaptive change freely. It answers what the best feasible response would be if replanning were allowed.

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.

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 limit on total CO₂ emissions across the network. Each flow can carry a carbon rate (tCO₂ per unit of flow).

The tonnes of CO₂ emitted per unit moved along a flow. Feeds into carbon caps 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.

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.

An explicit transformation at a node that turns input products into output products, such as one bulk batch producing 900 vials, or multiple feedstocks producing one product. Activity can be continuous, whole-number, or yes/no, and can consume resources per activity unit. Ordinary flows preserve product identity; yield only changes the quantity of the same product. (Called a recipe in exported model files.)

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 moved along a flow. When minimizing cost, the solver routes flow through the cheapest paths. Cost rates also feed into budget limits.

A cost curve on a flow. Each segment defines an "up to" volume and a cost rate. The solver picks the optimal volume on each segment.

For an infeasible model, a single set of changes across several limits that works as a package. SSPLAX applies the proposed values to a hard model and re-solves before offering it, so the package is verified rather than estimated.

A scenario analysis that freezes committed values at their baseline and lets only adaptive values move. It answers whether the plan you have already committed to still works, and what that commitment costs in each scenario.

A structured summary of the baseline answer, next move, active limits, scenario results, risk checks, 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 set of competing options with Min selected and Max selected bounds: exactly one supplier, at most one upgrade, any two of five sites. The bounds are enforced exactly.

One alternative inside a decision group. Carries its own type (yes/no, integer, continuous), quantity range, fixed and variable cost, intervention cost, resource rates, and effects on flows, conversions, capacity, and output.

Whether a choice is Committed (locked before scenarios resolve, and shared across all of them) or Adaptive (decided after the scenario is known, so it may differ between them). Applies to decision options, flows, and conversions.

A scenario-aware optimization rule that optimizes the probability-weighted outcome across the scenario set, in whichever direction the objective runs. Use it when your weights represent real likelihoods.

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

A path between two nodes that carries exactly one product, with min/max bounds, cost rate, carbon rate, yield, and optional resource consumption. Also the quantity the solver decides to move along that path in the answer. (Called an edge in exported model files.)

The solved visualization for a Network model. It shows route volume, capacity saturation, direction, yield, and resource totals for the baseline answer. Other Forms keep their own solved visual grammar.

The structural shape of a decision: Network, Composition, Portfolio, or Assignment. Form controls authoring and Results language while every model continues to use the same validated solver contract.

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 limit and flow 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 each queue-and-product inventory. 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 limits or flow settings so you can compare the outcome against the baseline.

What it costs to change the plan by selecting a decision option, as opposed to what that option costs to operate. Target seeking minimizes intervention cost, which is why it is kept separate from fixed and variable cost.

An optional stable name for a spreadsheet feed. Reusing it lets a later import safely replace its own rows and restore anything it previously overlaid. Without one, the file and sheet name identify the source.

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 Analysis ranking of constraints by impact. It uses pressure-test results when available, or marginal value as a fallback.

Solving the same model across multiple time periods with constraint schedules, demand growth, ramp limits, inventory carry-over, backlog, and holding costs between periods.

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

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. Higher means more sensitive.

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

A named product or material with its own quantity unit. Every flow carries exactly one product, and conservation, inventory, backlog, output commitments, and objectives are tracked separately for each product. (Called a commodity in exported model files.)

A constraint classification meaning the limit stays visible in binding analysis but is never proposed for relaxation and cannot be softened. For commitments that are not yours to trade away.

A constraint classification permitting relaxation, but requiring any result that does so to be labeled as policy relief, so a recommendation depending on a relaxed commitment is not presented as routine.

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

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 flows (FTEs, GPU hours, water, energy). Its definition stores a quantity unit; flow rates are quantity per unit moved, and limits are quantity per planning period.

A named alternative future saved with the model, carrying its own constraint values, flow and conversion bounds, and cost rates, plus an optional probability or weight. Distinct from Scenario B, which is a one-off comparison.

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 a saved scenario set is weighted: Equal weighting, Probabilities, or Relative weights. Probabilities and weights are all-or-none across the set, and cloning a scenario splits its weight rather than duplicating it.

Optimizing one plan across the whole scenario set at once, on an expected-value or worst-case rule. Committed decisions are shared across scenarios; adaptive ones stay scenario-specific.

A constraint the solver may exceed while paying a penalty per unit of violation, up to an optional maximum. Violations are reported separately and never folded into the headline KPI.

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.

Fixing a required outcome and asking for the cheapest set of interventions that reaches it. SSPLAX adds the target as a hard requirement, minimizes only the interventions you allow, then re-solves and verifies the operating plan.

The structural definition of a model: nodes, flows, limits, 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.

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

A scenario-aware optimization rule that optimizes the weakest scenario outcome: the lowest value when maximizing, the highest when minimizing. Use it when the downside matters more than the average.

A choice that is either active or inactive, such as "open this line" or "use this supplier." The solver represents it as 0 or 1. (Called a binary decision in exported model files.)

The amount delivered per unit of flow input. A yield of 0.72 means 28% is lost. Output = input × yield; flow bounds and resource rates apply to the input side.