OPS.10:4 - Solution
OPS.10:4.1 - State the demand and service question
Name the accepted result, population, demand profile, horizon and service criterion. Distinguish a service-visit queue from the operation’s final accepted-result population. State whether the question concerns a hard deadline for known jobs, a mean delay, a probability of completion, a protected response class or a bounded overload scenario.
Recover the initial unfinished work and its remaining service requirements. Preserve the customer’s waiting origin when local release times differ. If a target is an existing commitment, keep its parties and authority; a calculated service scenario does not create a promise.
OPS.10:4.2 - Recover usable time and required load
For each necessary resource, identify capability, access, calendar, location, configuration and skill conditions. Deduct known unusable intervals only where they actually remove the needed service. Keep uncertainty about outages or absence separate from known deductions.
Translate each demand class into resource-time requirements under the supplied Method. Include normal setup, joint-resource occupancy, expected or scenario-specific rework, recovery and other consequential load. State which effects are already included in a service-time estimate so they are not counted again as lost capacity.
Use compatible units. Two hours of a specialist and two hours of a general resource are not four hours of interchangeable service. A test attempt and an accepted release are not two interchangeable outputs.
The first calculation is a necessary bound: required load cannot exceed usable time for a resource within the required window. Passing that bound is not enough when precedence, arrival timing, simultaneous resources or non-interchangeable classes can prevent a schedule.
OPS.10:4.3 - Choose the smallest adequate service analysis
| Receiving question | Appropriate first analysis | What it can establish |
|---|---|---|
| Can these known jobs fit the actual windows? | A finite schedule using arrival, readiness, service, calendar and precedence data, or a necessary time/resource bound. | A feasible assignment; impossibility only when a necessary bound or complete valid search excludes the deadline. |
| What is a useful mean steady-state screen? | A queueing approximation whose population, service, routing, stability and dependence assumptions fit. | A qualified mean estimate, not a tail guarantee. |
| What happens under a specified disruption or burst? | A trace replay or explicit stress schedule, with actual policy and resources. | A conditional scenario result, not its probability of occurrence. |
| Does a changing or coupled operation meet a probabilistic service target? | A stochastic model or simulation of the relevant inputs and policy, retaining parameter and model uncertainty. | A conditional service probability; reliance on actual service additionally needs the corresponding input and model basis. |
For a single continuously available compatible server operating first-come, first-served, a finite calculation can be very small. For each job in arrival order, start is the later of its arrival and the previous finish; finish is start plus service time; wait is start minus arrival. This assumes no other setup, interruption, precedence or resource requirement. Add those conditions to the schedule when they apply.
Do not use a steady-state approximation for a finite overload or changing regime merely because it yields a number. Correlated bursts, feedback, blocking, priorities and skill classes may require a different model. A specialist analysis is a named input with a receiving use, not a substitute for stating the operating question.
OPS.10:4.4 - Test variability and the proposed change
Compare the current arrangement with the smallest material alternatives: demand/admission changes, timing, usable access, skill coverage, batch/setup changes, pooling or segregation, reserve, recovery and capacity acquisition.
Keep the result and service criterion fixed. Inspect arrival patterns as well as total demand, service and recovery variation as well as average duration, and common-cause losses as well as independent failures. Test the plausible adverse conditions that can reverse the choice.
A batching change may reduce setup time while delaying the first result or defect feedback. A pooled resource may improve a mean while changing a tail or protected class. A reserve must name the disturbance and capability it covers. A proposed schedule must not claim improvement by moving waiting outside the measured start boundary.
Use C.11.CRC for the finite comparison of consequences, resource cost, transition, reversibility and affected options. Keep a recommendation, provision of the resource, authority to use it and the resulting performance separate.
OPS.10:4.5 - State exactly what the result supports
Name each result as a hard bound, a feasible schedule under stated inputs, a stress scenario, an estimated mean, a percentile/probability forecast or an unresolved question. Attach the relevant uncertainty, data window and model limitations. Historical coverage of a target is not automatically its future probability.
For a consistently defined population with stable compatible averages, Little’s relation L = λW connects mean in-system count, effective flow rate and mean time. Returns, rework, abandonment and finite-window boundary terms need correct population accounting. The arrival/exit rate of service visits is not necessarily the rate of accepted final results. The relation is useful for consistency; it neither supplies a delay distribution nor makes a protective buffer sufficient.
If the available basis supports only a load bound, return that bound and the exact missing service information. A missing distribution does not prevent a known infeasibility result or a useful conditional stress comparison.
OPS.10:4.6 - Return the operating decision and refresh basis
Return demand and result units, horizon, usable capacity and load assumptions, the selected analysis, alternative consequences, chosen option or exact blocker, provision/decision authority, residual demand and reopening conditions.
OPS.5 can use this basis for admission. OPS.8 can use it for release and protection. OPS.7 can use changed feasibility evidence for an existing commitment. Use OPS.13, or an appropriate method from the responsible service practice, to establish a wider credible service offer; the local capacity result alone does not supply it.
Reopen when mix, arrivals, access, capability, absence, failure/rework, resource coupling or the service criterion changes. Retain observations of actual performance so the next decision can test, rather than silently inherit, the previous model.