TruckDisp Fleet Performance Metrics and KPIs Flashcards

A fleet dispatcher notices that the fleet's Empty Miles Percentage has risen from 12% to 19% over the past quarter despite load volume remaining constant. Which corrective action would MOST directly target the root cause of this KPI degradation?

Answer: Implement a backhaul optimization algorithm that matches outbound loads with return freight corridors before drivers depart

Empty miles percentage measures non-revenue miles as a proportion of total miles driven. When this ratio climbs without a load volume drop, the fleet is completing outbound hauls without securing return freight. A backhaul optimization tool addresses the root cause directly by pre-matching return lanes before the driver goes empty, reducing deadhead miles at the point of dispatch rather than through structural changes to contracts or routes.

A carrier's Revenue Per Truck Per Week (RPTW) is $4,200, but the fleet's Cost Per Mile (CPM) is $2.10 and average weekly miles per truck is 2,300. What does this data indicate about the fleet's financial position, and what KPI should a dispatcher prioritize to correct it?

Answer: The fleet is operating at a loss; dispatchers should prioritize increasing loaded miles per truck

Total cost per truck per week = CPM × miles = $2.10 × 2,300 = $4,830. Since RPTW is $4,200, the fleet is losing $630 per truck per week. The dispatcher's lever is loaded miles — increasing revenue-generating miles without proportionally raising costs will improve RPTW. Simply adding more trucks would scale the loss. The fleet is definitively not at breakeven or profitable based on these figures.

When evaluating On-Time Delivery (OTD) performance, a dispatcher observes that the fleet's OTD rate is 94% but customer complaint volume has surged. Which scenario BEST explains this apparent contradiction?

Answer: The OTD measurement window is too wide (e.g., ±4 hours) and masks near-misses that customers consider late

OTD rate is highly sensitive to how 'on time' is defined. A ±4-hour delivery window allows arrivals that customers perceive as late to still count as on-time in the KPI. This creates a gap between the metric and the customer experience. Shippers with tight production schedules or JIT receiving may consider a 3.5-hour-late delivery a failure even if it registers as OTD in the dispatcher's system. Reviewing and tightening the OTD measurement window is the diagnostic step that resolves the contradiction.

A fleet tracking Truck Utilization Rate (TUR) defines it as (Revenue Hours ÷ Available Hours) × 100. Driver A logs 58 revenue hours out of 70 available hours in a week. Driver B logs 62 revenue hours out of 80 available hours. A dispatcher must allocate a high-priority load requiring maximum reliability. Based solely on TUR, which driver should receive the load and why?

Answer: Driver A, because a TUR of ~82.9% indicates more efficient use of available time than Driver B's ~77.5%

Driver A's TUR = 58/70 × 100 ≈ 82.9%. Driver B's TUR = 62/80 × 100 = 77.5%. TUR measures efficiency of time usage, not raw volume. A higher TUR means the driver consistently converts available hours into revenue-generating activity, which signals operational discipline and reliability — the key criterion for a high-priority load. Driver B's higher absolute hours are offset by a larger idle-time proportion, indicating less efficient utilization.

A fleet dispatcher is reviewing the Fuel Efficiency KPI and notices that trucks on the I-10 corridor average 6.8 MPG while trucks on the I-80 corridor average 5.9 MPG. All trucks are the same model year and spec. Which factor would a dispatcher investigate FIRST before concluding the I-80 lanes are underperforming on fuel efficiency?

Answer: The elevation profile and prevailing headwind patterns along the I-80 corridor

Before attributing a fuel efficiency gap to driver behavior or operational factors, a dispatcher must account for route geography. I-80 crosses the Rocky Mountains and Sierra Nevada, featuring significant elevation gain and strong seasonal headwinds — both of which dramatically increase fuel consumption for any truck regardless of driver skill. Penalizing drivers or changing tactics on I-80 without first normalizing for terrain would lead to incorrect conclusions. Elevation and wind are the primary uncontrollable variables that explain the MPG delta between flat-terrain I-10 and mountainous I-80.

A carrier uses a composite Fleet Performance Score (FPS) weighted as follows: OTD 30%, RPTW 25%, Driver Retention 20%, Empty Miles % 15%, CSA Score 10%. Fleet A scores: OTD 96%, RPTW index 88, Retention 72%, Empty Miles 11%, CSA 78. Fleet B scores: OTD 89%, RPTW index 95, Retention 91%, Empty Miles 8%, CSA 91. Which fleet has the higher FPS, and what does the gap reveal about strategic priorities?

Answer: Fleet B wins with a higher FPS, revealing that revenue and retention metrics can overcome OTD deficits at scale

Fleet A FPS = (96×0.30)+(88×0.25)+(72×0.20)+(11 inverted: assume 100-11=89×0.15)+(78×0.10) = 28.8+22+14.4+13.35+7.8 = 86.35. Fleet B FPS = (89×0.30)+(95×0.25)+(91×0.20)+(100-8=92×0.15)+(91×0.10) = 26.7+23.75+18.2+13.8+9.1 = 91.55. Fleet B scores higher. The gap reveals that strong driver retention and revenue efficiency, when weighted correctly, can more than compensate for a lower OTD rate — a critical insight for dispatchers who over-index on delivery timeliness at the expense of workforce stability and revenue-per-truck metrics.