Quantify excess travel cost and identify optimization opportunities across your field service operation
Ranked by total avoidable cost across five dimensions. Focus on the top items in each category for the highest ROI improvements.
Which service verticals generate the most excess travel? Clinical Education stands out with very high per-WO excess.
| # | Vertical | WOs | Avoidable Cost | Impact | Avg Excess | Outliers | Flew | Actual Expense | Techs |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Kiosk | 202 | $9,849 | 100% | 72.8 mi | 20 | — | $4,925 | 14 |
States with the highest total avoidable travel cost.
| # | State | WOs | Avoidable Cost | Impact | Avg Excess | Flew |
|---|---|---|---|---|---|---|
| 1 | CO | 19 | $6,748 | 530.1 mi | — | |
| 2 | IL | 28 | $2,044 | 109.0 mi | — | |
| 3 | TX | 96 | $842 | 13.1 mi | — | |
| 4 | MO | 10 | $72 | 10.7 mi | — | |
| 5 | AZ | 4 | $31 | 11.5 mi | — | |
| 6 | OK | 39 | $6 | 0.2 mi | — | |
| 7 | KS | 3 | $5 | 2.2 mi | — |
Top cities by total avoidable cost. These are your biggest geographic hotspots.
| # | City | State | WOs | Avoidable Cost | Impact | Avg Excess | Flew |
|---|---|---|---|---|---|---|---|
| 1 | Dallas | TX | 15 | $94 | 9.4 mi | — | |
| 2 | Houston | TX | 9 | $72 | 11.9 mi | — | |
| 3 | Pearland | TX | 3 | $68 | 33.7 mi | — | |
| 4 | Plano | TX | 4 | $60 | 22.5 mi | — | |
| 5 | Irving | TX | 5 | $54 | 16.0 mi | — | |
| 6 | Mckinney | TX | 3 | $32 | 16.1 mi | — | |
| 7 | Phoenix | AZ | 4 | $31 | 11.5 mi | — | |
| 8 | Garland | TX | 4 | $25 | 9.6 mi | — | |
| 9 | Springfield | IL | 14 | $21 | 2.2 mi | — | |
| 10 | Grand Prairie | TX | 3 | $19 | 9.8 mi | — | |
| 11 | Amarillo | TX | 3 | $0 | 0.0 mi | — | |
| 12 | Edmond | OK | 3 | $0 | 0.0 mi | — | |
| 13 | Norman | OK | 3 | $0 | 0.0 mi | — | |
| 14 | Oklahoma City | OK | 13 | $0 | 0.0 mi | — | |
| 15 | Spring | TX | 3 | $0 | 0.0 mi | — | |
| 16 | Tulsa | OK | 7 | $0 | 0.0 mi | — |
Coordinators whose dispatching decisions result in the most excess travel. High avg excess suggests systematic over-dispatching; high WO count with moderate excess suggests volume-driven impact.
| # | Coordinator | WOs | Avoidable Cost | Impact | Avg Excess | Outliers | Flew | Actual Expense | Techs Used |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Kaitlyn Kelch | 202 | $9,849 | 72.8 mi | 20 | — | $4,925 | 14 |
Techs who consistently travel far beyond the closest available option. High states-served count may indicate a roaming/national tech; high avg excess with few states suggests a misplaced home base.
| # | Technician | WOs | Avoidable Cost | Impact | Avg Actual Dist | Avg Excess | Outliers | Flew | States | Actual Expense |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Jason Morris | 10 | $6,366 | 1,001 mi | 950.2 mi | 10 | — | 1 | $171 | |
| 2 | Christopher Womack | 43 | $354 | 42 mi | 12.3 mi | 0 | — | 1 | $729 | |
| 3 | Curtis Sprague | 10 | $288 | 120 mi | 43.0 mi | 3 | — | 1 | — | |
| 4 | Chane Lacy | 25 | $193 | 33 mi | 11.5 mi | 1 | — | 1 | $545 | |
| 5 | Blake Ward | 13 | $158 | 82 mi | 18.1 mi | 0 | — | 2 | $224 | |
| 6 | Thomas J Langenberg Jr | 6 | $103 | 87 mi | 25.5 mi | 2 | — | 3 | $322 | |
| 7 | Kevin Cox | 19 | $19 | 100 mi | 1.5 mi | 0 | — | 2 | $256 | |
| 8 | Darrell Hendrix | 47 | $10 | 65 mi | 0.3 mi | 0 | — | 3 | $1,112 | |
| 9 | Zachary Stieben | 9 | $3 | 20 mi | 0.5 mi | 0 | — | 1 | $865 | |
| 10 | Ben Heinz | 9 | $0 | 44 mi | 0.0 mi | 0 | — | 2 | $205 |