Monitor and Assure
Target Feasibility Monitor
A pathway that was feasible when it was assessed is not guaranteed to stay feasible. Demand patterns shift. Variation profiles change. Unofficial rules accumulate in practice. The Target Feasibility Monitor tracks the pathway's structural health on an ongoing basis. When a change moves the system toward infeasibility, the organisation knows before performance fails.
Standard monitoring watches lagging indicators: breaches, DNA rates, utilisation. These record what has already happened. Structural health monitoring tracks the conditions that determine what is still possible. That is a different signal, and an earlier one.
What this delivers
Ongoing System Reliability Rating tracking as system conditions change
Demand long-run averages, variation profiles, and operational practice, not only policy additions. The monitor tracks structural health against all the ways a complex system drifts.
Early warning before infeasibility becomes performance failure
A falling System Reliability Rating precedes waiting time breaches. The monitor provides the lead indicator that makes emergency responses avoidable.
Attribution of rating changes to their specific cause
When the rating changes, the model identifies whether the cause is a demand shift, a variation change, or rule drift. Named, not inferred.
A continuous governance record of structural health
Every score, threshold status, and attributed cause recorded over time. Not a snapshot at the point of concern. A history that shows when the trajectory changed and why.
How it works
The client completes a periodic template capturing current pathway conditions: demand volumes, variation parameters, and any changes to operational rules or practice since the last return. Boole analyses each return and produces a structured report covering the current System Reliability Rating, trajectory against the established feasibility threshold, threshold status, and any attributed cause for material movement in the rating.
Where the rating moves below threshold, the report identifies whether the cause is a demand shift, a change in variation profile, or accumulated rule drift. The cause is named from the model, not inferred from performance data. The remediation is specific, not speculative.
Monitoring capability is evolving. The current model operates on periodic client returns; the roadmap includes API-connected tracking with threshold-triggered alerts, which would shorten the interval between a structural change and its detection. That capability is in development. What is available now is a rigorous, structured monitoring process that provides substantially more lead time than waiting for performance indicators to move.