The day, side by side
Your schedule
- Crew hours / day
- 48 hrs6 crews × 8 hr benchmark day
- Jobs completed
- 29
- Windshield time
- 15.3 hrs
- Idle / gap time
- 7 hrs
- Time to build this schedule
- ~30 mindispatcher, by hand and by phone
Crisphive rebuild
- Crew hours / day
- 48 hrssame crew, same shift
- Jobs completed
- 41+12 jobsYour 29 booked stops stay on the schedule — the 12 extra jobs fit into recovered hours only.
- Windshield time
- 6.5 hrs−57%
- Idle / gap time
- 1 hrs−86%
- Time to build this schedule
- 3 secvs ~30 min by phone
Disruptions your dispatcher juggled by phone that day: 1
One vehicle's route, before and after (Vehicle 1)
5 stops · 112 km · 3 hrs driving
7 stops · 45 km · 1.2 hrs driving
Same day, same vehicle — jobs re-sequenced into a tighter loop. your original jobs · the 2 extra jobs that now fit. Layout is illustrative. The stop counts, distance and driving time below each panel are read from the schedule you sent.
Recovered hours → dollars
| Drive time recovered across the crew 15.3 hrs windshield time as dispatched − 6.5 hrs after the rebuild | 8.8 hrs |
| Idle / gap time recovered 7 hrs of gaps in your day − 1 hr remaining after gaps are back-filled | 6 hrs |
| Total time recovered 8.8 hrs driving + 6 hrs idle | 14.8 hrs |
| Additional jobs that fit 14.8 recovered hrs ÷ ≈1.2 hr average visit length in your schedule — on top of the 29 already booked | 12 |
| Average ticket used Conservative benchmark — your own export shows $7,706 across 29 completed jobs, ≈$266/ticket. We price below it so every dollar figure errs low; send a different number and it all recalculates. | $250 · based on industry benchmarks |
| Unscheduled revenue in this one day 12 additional jobs × $250/ticket | $3,000 |
| Annualized across the fleet (~250 working days) $3,000 × 250 working days | ≈ $750,000 |
Where the hours were hiding
- Cross-town scheduling and geographic overlap across crew zones45%
Jobs were booked across town from one another, so crews drove through zones where another crew was already working.
- Unfilled schedule gaps from cancellations and wide arrival windows30%
A cancellation or a wide arrival window left a hole in the day, and no nearby job was pulled in to fill it.
- Mid-day supplier runs due to missing inventory on vehicles15%
Trucks left mid-route for parts that could have been staged on the vehicle before the day started.
- Manual dispatch adjustments for emergency calls10%
Emergency calls were squeezed in by phone. The rebuild keeps buffer for them at industry emergency-call rates — the 12 added jobs never assume a disruption-free day.
What we assume about your customers
Customer availability may vary: this rebuild assumes every job could move anywhere within the same working day. Your real recovered hours will land between the day you ran and the day shown here — the live scheduler does match customer availability, this on-paper rebuild deliberately does not.
Assumptions used
- Average ticket: $250/ticket — conservative; your own export averages ≈$266 across 29 completed jobs
- Working days per year: 250
- Crews on the road: 6 · benchmark working day: 8 hrs per crew (48 crew-hours)
- Distances and drive times: computed from the job addresses in your schedule file
- Customer availability: treated as flexible within the working day (see the note above)
- Disagree with any figure? Send your real numbers and we re-run the audit — the numbers move, the method doesn't.
About your audit results (indemnification)
Shadow Schedule Audit findings are generated internally using Crisphive's own scheduling algorithms and large language models (LLMs). They are illustrative estimates and suggestions — not hard recommendations, guarantees, or professional advice — and are non-binding on you and on us. Every business and business owner should conduct their own due diligence and run their own calculations before acting on any figure in an audit report. See Section 4.5 of our Privacy Policy for the full terms.
These figures come from one day’s schedule annualised over 250 working days, so your year will differ. Your file was read by our algorithms and LLMs, which can make mistakes.