Optimise
Resource Planning
Theatre lists not clearing despite available capacity. Rotas that should be delivering throughput but aren't. Clinic scheduling that creates bottlenecks nobody can see. Organisations know they have a resource problem. The question is whether the plan will actually solve it before it is implemented.
Resource Planning tests candidate configurations across rotas, beds, theatre scheduling, and clinic scheduling against the validated pathway logic. The output is not a range of options to deliberate over. It is a specific, tested configuration, with a clear answer to whether resource is the right lever to pull.
What this delivers
An optimised configuration across every relevant resource type
Rotas, physical space (beds), theatre scheduling, clinic scheduling. The output is not a range of options to deliberate over. It is a specific, tested configuration validated against the pathway's actual constraints.
Confirmation of whether resource is the binding constraint
Resource planning applied to a policy problem cannot resolve it. Establishing whether capacity is the binding constraint first prevents investment being absorbed without effect.
A resource plan tested against the pathway logic before implementation
The configuration is validated against the pathway's policy environment before it goes live. What the model produces is a plan the pathway logic can actually support, not a plan that looks right on paper but fails in operation.
Evidence base for the investment or operational change decision
A resource commitment backed by model evidence differs from one backed by extrapolation. Boards and finance committees can distinguish the two. This is the evidence base that makes the distinction clear.
How it works
Candidate resource configurations are tested via the Pathway Engine against the validated pathway logic. Each resource type is modelled directly: rota patterns are mapped to available session capacity and tested against demand profiles; bed allocation is modelled against admission, discharge, and escalation logic; theatre scheduling is tested against list composition, turnaround constraints, and booking rules; clinic scheduling is modelled against appointment slot structure, DNA rates, and follow-up ratios.
The engine returns a System Reliability Rating for each configuration — the percentage probability of meeting the target every month — identifies the threshold at which the pathway becomes feasible, and produces the specific configuration that crosses it. Where resource is not the binding constraint, the model confirms this and routes toward policy resolution instead. The resource planning question has a useful answer only when capacity is the constraint, and that determination is made before the configuration work begins.
The output is a structured resource specification: the configuration tested, the rating produced, the feasibility threshold, and the configuration that crosses it. Every number is model-traceable. The investment or operational change case is built from the model's output, not from historical throughput trends and projected demand.