Crew schedule efficiency across the Heartland operating companies

Read the two right-hand groups against each other. The percentage says how much of a company's driving is avoidable; the miles say how much driving that actually is. They disagree sharply and the disagreement is the point: Signature leads on percentage at 17.1% but yields 68,537 miles, while Heartland AZ yields 89,899 miles on 9.2%, and Heritage Landscape Services — the most efficient operation in the estate at 3.4% — still gives up 49,408 miles because of its size.

Each crew-week is costed three ways as a loop out from the yard and back. The crew never changes.

“Yard” in this document means the fixed location each crew-day starts from and returns to — the branch’s operating base. Aspire has no field that identifies one, so it was established two ways: for branches that have a property record named after the branch, those coordinates; and for the rest, the place crews are observed to finish the day, taken from the shift clock’s recorded position. The two agree closely where both exist — within 35 to 395 feet across Signature’s five branches — and crews clock in there, with 92% of clock-ins inside 1,640 feet. Whether the address is a truck yard, a shop or an office is not recorded, and does not affect the measurement.

• As planned — the planned days, in the planned stop order.
• Reordered — the same visits on the same days, in the best sequence.
• Fully optimized — visits regrouped across the days that crew was already going to work, then resequenced.

The two levers follow directly. Lever 1, stop order, is the gap between as-planned and reordered. Lever 2, day grouping, is the further gap to fully optimized. They add to the total, and the worked examples below show them separately for real weeks.

Sequences are solved exactly, by dynamic programming, wherever a day has eleven stops or fewer — between 80% and 98% of crew-weeks depending on tenant. Longer days use a method that returns an upper bound on the best route, so those days understate the saving. Every approximation in this analysis is set to err low.

A solver run over past data can take advantage of knowing which day it rained, which crew was short, and which job overran. A scheduler on the Friday before knew none of that, so a saving that depends on such knowledge is not one anyone could have captured.

The headline figures therefore use only what was recorded before each week began: the planned day for each visit, the planned stop order within that day (present on 96% of visits), planned hours rather than actual hours, and a daily capacity ceiling taken from that week’s own plan. No outcome enters the calculation. For contrast, the same method applied to what actually happened is shown alongside.

How to read this table

Each row is one Aspire instance. The four numbers answer four different questions, and the interesting part is how they disagree.

• Planning-time is the headline saving — what a better plan could have achieved using only what was known before the week started.
• With hindsight is the same calculation allowed to see what actually happened. Compare it with the first column. Where hindsight is higher, the week drifted from its plan and some of that drift was itself costly. Where the two are close, the plan is a fair description of the work.
• Plan adherence is the share of planned visits worked on the day they were planned for. This is the column that decides whether any of this is actionable: if plans were routinely ignored, improving them would change nothing. Almost everyone sits above 93%. Read it as an upper bound — see the note below.
• Sequences solved exactly is a confidence measure, not a result. It is the share of crew-days small enough to solve to a proven optimum rather than with a heuristic. A high number means little of that row rests on approximation; where it is lower, the true saving is likely larger than shown, because the heuristic only ever returns a tour no better than the best one.

What “the plan” means here, precisely. Adherence compares each visit’s ScheduledDate in Aspire against the dates on which labor was actually booked to that ticket. Aspire stores ScheduledDate as current state: the WorkTicketVisit record has eight fields and none of them is a created, modified, previous or original date. When a scheduler moves a visit, the old date is overwritten and no history is exposed by the API.

So a visit moved on Wednesday and worked on its new Thursday date counts as adherent. What this column measures is that the schedule as it now stands agrees with what was recorded — not that the schedule as first published was followed. The true “was the original plan honored” figure is lower than these numbers and is not obtainable from this API. It would need either an Aspire audit trail or a nightly snapshot of the plan taken before each week begins, which is a small and worthwhile thing to start collecting.

Two rows worth looking at. Agrow Pro shows 8.3% at planning time and 14.9% with hindsight on 84.6% adherence — the weakest adherence in the estate, so its plan and its week are different things and the planning-time figure is the only one to trust. Cutting Edge Services runs the other way, 15.4% falling to 10.4% with hindsight: the week went better than the plan implied.

Removing hindsight does not remove the opportunity. Because adherence is high, the planned days and the worked days are nearly the same, so the two columns differ mainly in whether planned or actual hours set the capacity ceiling. JML is the one clear exception, where the hindsight figure is much higher; its planning-time figure is the one to rely on.

High adherence is what makes these figures actionable. If plans were routinely set aside, improving them would change nothing on the ground. At 95.9% across 784,143 matched visits, the plan is what happens.

A higher hindsight figure looks like it should mean crews made the week worse. It does not follow. The two percentages are measured against different baselines — the planned week's distance and the actual week's distance — so a crew can deviate, drive less overall, and still show a larger avoidable share because the baseline it is measured against shrank too.

Answering the question needs absolute distance, normalized per route-week because the two views do not cover the same weeks. On that basis: 22 of 23 operating companies drive fewer miles per route-week than their plan called for, typically 10–20% fewer.

So deviation is usually improvement, and that makes the headline stronger rather than weaker. The plan is systematically looser than the work; crews and dispatchers already recover much of the difference by hand, in the moment; and planning-time still finds avoidable distance after that recovery. Those recoveries are unpaid improvisation. They depend on who is dispatching that day, they are invisible in any system, and they have to be repeated every week. Fixing the plan makes them unnecessary.

Read the gap column with the adherence column beside it. A large gap on strong adherence means the plan was followed and was simply wrong for the week. A large gap on weak adherence means the week was not really run to plan at all, and the planning-time figure is the only one of the two worth quoting.

JML and HLM are the two to look at first, and they show the distinction this section exists to make. Both drive fewer miles per week than their plan called for (-7.6% and -7.1%), so neither is adding mileage. What changes is the shape of the route: the week they actually work is a less efficient arrangement of a smaller distance. The avoidable share of what they do drive roughly doubles — 7.6% to 13.5% and 6.4% to 11.2%.

That is a different problem from driving too far, and it has a different fix. These are not crews doing extra work; they are crews reorganizing the week in a way that shortens it overall while leaving the stops in a worse order than the plan already had.

This comparison is bounded by the same limitation as plan adherence itself: WorkTicketVisits.ScheduledDate is current state rather than a versioned original, so "the plan" here is the last saved plan. Some of what reads as crews improving on the plan may instead be the plan being edited during the week to match what crews did. Distinguishing the two needs a snapshot of the schedule taken before each week begins, which nothing currently captures. Excluded as too small to compare: Agrow Pro, LLC (13 hindsight weeks).

Lever 1 measures something Aspire already does: selecting a crew-day and choosing Optimize Route resequences that day’s stops. That raises an important question for management, because the answer changes the response entirely. If the feature is ineffective, the gap needs new software. If it is effective but unused, the gap needs a process change.

This is testable. If the feature ran on a given day, that day’s recorded stop order should be the best available order, or within a fraction of it. So the share of days that are already exactly optimal indicates how often it was applied — provided the figure is compared against how often a hand-built order would be optimal by luck, which on a four-stop day is one time in three.

On days of six stops or more, where landing on the best order by chance is effectively impossible, 8–21% of days are exactly optimal. That cannot happen by accident, so the feature is being used and it works. It is simply not used consistently: across all judged days the median gap to the best order is 5.9% at Signature, 3.9% at JML and 10.9% at Keesen, and roughly a quarter of days sit more than 15% off.

The pattern by day size is the most useful part. The largest days — where the saving is greatest — are the ones where it is never applied. No day of 18 or more stops was optimally sequenced at Signature. This is consistent with the feature being used opportunistically on simple days and skipped when a day looks complicated.

Distance is the unit of measurement, not the value. The avoidable planned distance is 681,788 straight-line miles over the eight months, which converts to 108,763 crew-hours: adjusted by a 1.3 road factor, at 25 mph, multiplied by the measured crew size of 3.05 people per crew-day. Fuel over the same distance is roughly an order of magnitude less valuable than the labor.

Aspire records labor two ways, and the distinction matters for putting that figure in context.

• Time booked to work tickets — 2,617,930 h. This is what job costing sees, and it averages 7.7 to 10.0 hours per person-day depending on tenant.
• Time on the shift clock — 6,144,380 h across 630,460 shifts, averaging 9.75 hours per shift. This is what the business pays for.

The difference is 951,579 hours, or 15.5% of all clocked time, that never reaches a work ticket. Against clocked labor, the avoidable driving is 1.7%; against booked ticket time it is 2.1%.

What this measurement cannot show. Visits only move between days the crew was already going to work, so the method can never indicate that five crew-days would do the work of six. Any reduction in crew-days is outside these figures and is treated separately below.

Whether freed time becomes value is an operating decision. It can become additional billable work, shorter days, or simply slack. Days are already substantial — a 9.75-hour average shift — so additional capacity is the most likely destination, provided there is demand to absorb it.

The figures above hold the working days fixed. A natural next question is whether saving enough time allows a week to be completed in fewer days. It does, and in crew-hours it is worth several times the routing saving. Whether it is worth anything net depends on how long a day crews are willing to work, because that is where it meets overtime.

Eliminating a day does not remove labor hours — the work moves to other days. What a freed day provides is capacity: the same crews covering more properties, or a six-day route becoming a five-day one. Converting that to money requires either demand to absorb it or a premium to avoid.

What each column means, and what to do about it

• Property coordinates — the share of properties with latitude and longitude. Strong everywhere (92–100%), and the foundation of any distance work. No action needed.
• Yard recorded — whether each branch has a property record for its own yard. Only 7 of 91 branches have one, and 21 of the 24 instances have none on any branch, so their yard locations had to be derived from where crews are observed to finish the day. Recommended action: record a yard property for every branch. The derivation reproduced Signature’s five known yards to within 35–395 feet, so the coordinates are available and simply need entering.
• Yard confidence — how tightly crews cluster on that derived location. Signature reaches 75%; Heartland NE and LCM sit near 14%, meaning crews there do not converge on a single place. Seven Heartland NE branches and three each at LCM and Heritage fall below 20% and should be treated as approximate. The derivation also shows several branches sharing one physical yard — four Heartland AZ branches resolve to a single Tucson location.
• Untimed labor — hours entered manually with a midnight timestamp and therefore no usable clock times. 9% at Keesen, over 17% at Heartland AZ, Heartland NE and Heritage. These hours are excluded from the analysis, and any time-of-day reporting on them is unreliable. Recommended action: move manual entry onto the mobile app where practical.
• Planned order recorded — the share of planned visits carrying a sequence number. Strong except Keesen at 88.5%. Where sequence is missing, lever 1 cannot be assessed for that day.
• Visits never worked — planned visits with no labor recorded against them at all. This ranges from 3% at LCM to 37% at Heartland NE. Some will be genuine cancellations; the remainder is work completed without time capture. Either way the planned-versus- delivered picture is incomplete, and at the high end it undermines any completion reporting. This is the single largest data gap found.
• Actual vs estimate — hours delivered against hours estimated. Most tenants sit within a few points of parity, which is genuinely good. Heritage is the exception at 120%, roughly 108,000 hours beyond estimate.

Some constraints are not recorded anywhere. Aspire’s property availability table — where a “Tuesdays only, 8 to 4” rule would be held — is empty. No service interval or recurrence field exists in the interface. Property notes are populated on about a fifth of properties and are predominantly gate codes. Where a customer genuinely requires a fixed day, the analysis will show a saving that is not available. This is precisely why nothing here moves a visit outside its planned week or to a different crew.

Distances are straight-line. Real roads are longer, so absolute miles are a lower bound while the percentages are robust. Depot placement was tested directly and changes the percentages by at most 0.1 points.

Coverage is partial by design. Crew-weeks with a single working day, fewer than four stops, or no capacity history are excluded; roughly three-quarters qualify. The excluded weeks are mostly those where sequence and grouping cannot be wrong.

Earlier figures were higher. A previous version of this analysis reported a group median of 14.7% and 23.4% for Signature. Building the worked examples revealed two faults: the solver could empty a working day entirely, which counts a crew-day reduction as a routing saving, and the daily capacity ceiling did not bind where a multi-day job was recorded as a single visit. Both are now constrained, and both corrections reduce the reported saving. The figures in this document are the corrected ones.

The next action is not further analysis. It is a review of specific proposed changes with the people who build the schedules: take the weakest crew-weeks to a production manager and record how many of the proposed regroupings survive scrutiny. Begin with Signature KCMO, the widest single branch at 21.7%, and include Heritage as a control, where the analysis indicates there is little to find. The proportion accepted is what converts a percentage into a forecast, and it requires one meeting rather than another data exercise.

In parallel, three data changes would improve every figure here and are worth making regardless: record a yard property for each branch, reduce manual midnight time entry, and investigate why 37% of Heartland NE’s planned visits carry no recorded labor.