Optimise
Option Evaluation
Most improvement programmes start with a shortlist of options someone has already decided to consider. Option Evaluation does not start there. It simulates every allowable combination of pathway, policy, and capacity adjustments to establish the complete set of configurations that make the performance target achievable, including ones nobody had proposed.
The output is not a verdict on a curated shortlist. It is a map of every viable configuration, the shape of the frontier between them, and three reference points drawn from the model, not selected by a committee.
What this delivers
A complete map of every configuration that makes the target sustainably achievable
Not a test of options already on the table. Every allowable combination of pathway, policy, and capacity parameters, simulated.
A feasibility frontier
Performance against cost: a chart of every viable configuration, each one explorable. The shape of the frontier shows how much performance is available and at what cost.
Three reference points from the frontier
Lowest-cost feasible configuration; highest-performing configuration to the point of diminishing returns; minimum-change configuration. Surfaced from the model, not selected by a committee.
A defensible programme selection
Every configuration not chosen has a calculable reason. The governance record holds the evidence, not only the conclusion.
How it works
The evaluation does not begin with a shortlist. The Pathway Engine exhaustively simulates every allowable parameter adjustment across pathway, policy, and capacity, generating the full solution space rather than testing a hand-curated set of candidates. The result is a chart where the x axis is performance against SLA and the y axis is annual pathway cost. Each point on that chart is a distinct configuration, explorable for its full parameter detail.
The shape of the frontier is itself informative. A steep curve means additional performance is expensive to buy. A flat curve means there is headroom. Clusters reveal where multiple configurations converge on similar outcomes. Gaps reveal where no configuration can bridge the distance without a step-change in input.
Three reference points are then derived algorithmically from the frontier: the lowest-cost configuration that meets the target; the highest-performing configuration before returns diminish sharply; and the minimum-change configuration that crosses the feasibility threshold. These replace the committee-preference shortlist with a model-derived solution space where every option not chosen has a calculable reason for its exclusion.