Illustrative example, not a client result. This service business, its records, interviews and findings are fictional. Every figure below is invented. No improvement has been achieved or measured. This shows the broader diagnostic, starting at $1,500; the shorter $499 review has its own example.
The decision: add capacity or change how jobs enter the schedule?
A service business has a full calendar, missed customer dates and repeated rescheduling. Its manager is considering another field employee. Dispatch says jobs arrive without confirmed parts or customer access. The field team says last-minute schedule changes leave gaps that are difficult to fill.
The example examines one process, from an approved job entering scheduling through completion. It uses existing job records, the scheduling procedure and three stakeholder interviews. Emergency work is recorded separately. Hiring decisions, technical service quality and a company-wide redesign are outside this diagnostic.
The working map is: job approved → parts and access confirmed → slot assigned → work completed. In the fictional records, a slot is often assigned before parts and access are confirmed. The diagnostic asks how that sequence affects the schedule and what else could explain the delays.
Completed work and unfinished work tell different parts of the story.
The invented sample covers 40 routine jobs completed during six weeks. An additional 11 jobs are still open at the cutoff, including four beyond their original promised date. Open jobs are reported separately; they are not counted as on time or dropped from follow-up.
- Missed original promised date
- 18 of 40 completed jobs, or 45%. Rescheduling does not reset the date used for this measure.
- Parts or access not confirmed when the slot was assigned
- 12 of these 16 jobs finished late.
- Parts and access confirmed when the slot was assigned
- 6 of these 24 jobs finished late.
- Time from approval to completion
- Median of 8.2 business days for completed jobs. This excludes the 11 open jobs and cannot describe their eventual turnaround.
- What the records cannot establish
- Reliable active-work time and job-complexity categories are missing. These records cannot show whether the team has enough capacity or whether scheduling order caused the late work.
These are made-up observations, not industry benchmarks. The difference between the groups is a reason to investigate, not a causal estimate.
What supports each explanation?
The calendar includes work that is not ready to start.
Evidence: in the fictional records, 16 jobs received a slot before parts or access were confirmed. Dated notes show that nine of those jobs were later moved while a prerequisite remained unresolved.
Confidence: high that this sequence occurred in those records. Moderate that it contributes to disruption, based on the notes and interviews. The time lost has not been measured reliably.
Keeping the calendar full may hide usable capacity.
Evidence: dispatch is asked to fill every available slot. Dispatch and field interviews describe short gaps after jobs are moved, but the records do not show whether another ready job could have used them.
Confidence: low. Whether ready work could fill those gaps is still untested. Empty calendar time is not proof that an employee could have completed more work.
Jobs also ran late when prerequisites were confirmed.
Evidence: six jobs with confirmed parts and access still finished late. Interviewees describe emergency interruptions and jobs taking longer than expected.
Alternative explanations: job complexity, supplier reliability, travel, absence and demand beyond available capacity. A scheduling change would not necessarily resolve these.
The evidence does not settle the hiring question.
Evidence needed: comparable job categories, available hours, active-work time, demand arriving each week and the age of unfinished work. The current records do not justify a claim that another employee is unnecessary.
Test a schedule that distinguishes ready work from blocked work.
For one team, keep jobs awaiting parts or access in a visible holding queue with a named owner and next action. Confirm those prerequisites before releasing a job into a committed slot. Agree on a cutoff for reviewing the next day's work and one person who can approve an urgent exception.
The proposed mechanism is fewer avoidable slot changes and more notice to use a released slot. The tradeoff is that holding work outside the calendar could delay customer commitments or hide a growing backlog. The test therefore follows all approved jobs, including those waiting to enter the schedule.
The process owner agrees the rules with dispatch and the field team. Record why a slot changes, how much notice is available, whether the slot is reused, and the additional dispatch time. Implementation support would be scoped separately.
Define what would count as a useful change.
In this fictional example, the sponsor chooses a four-week pilot. For routine jobs due in that period, report the number and percentage that missed their original promised date, including jobs still unfinished after that date. Recalculate the earlier period using the same inclusion rule before comparing rates; the 45% above describes completed jobs only. Also track avoidable slot changes and time from approval to completion, alongside open-job age, the size of the holding queue, emergency response, overtime and dispatch effort.
Compare similar jobs and record changes in volume, staffing and supplier delays. Follow the pilot's unfinished jobs through to completion. A short pilot with a different work mix may be inconclusive, even if the headline percentage improves.
If slot changes fall and delivery improves without a growing queue or unacceptable extra work, the team has a reason to continue testing. If the queue grows despite ready work moving reliably, examine demand and capacity. If urgent work suffers, revise the exception rule. None of those decisions can be made from the invented baseline alone.
A real Performance & Process Diagnostic defines the question, access and deliverables in writing. This example shows how evidence can narrow a decision while leaving uncertainty visible.