Sample · Board-Ready Assessment

Continental Freight Solutions, Inc.

Executive Operational Intelligence Assessment · Feb – Jun 2026
Prepared for
Board of Directors
Prepared by
WGS Operational Intelligence
Data window
Feb 2 – Jun 30, 2026
Shipments analysed
200

This is a fictional sample built from a real CSV file that ships with the product. Every finding, row number and dollar figure in this report is derived from that same file, so you can open it and verify each item line by line before you buy. Your own report will be built from your own data in exactly the same way.

01

Executive Summary

Analysis of five or more operational datasets across finance, procurement, inventory, payroll, and operations surfaced 17+ findings across the full data set. Aggregate estimated annualised exposure is $268,000 – $412,000, of which approximately 62% is addressable within 30 days using controls already in place.

The most material risk category is unrecovered accessorial revenue. 148.5 hours of shipper detention across the 200-load window were logged as non-billable despite being clear customer-caused delay. Three loads alone (SHP100014, SHP100064, SHP100076) carried 63 hours of unbilled detention. Annualised, this pattern represents an estimated $92K – $138K in leaked revenue.

The second material category is variable-cost overrun: 11 shipments burned fuel materially above the planned MPG baseline, driving an aggregate fuel overrun of $10,823 in the observation window against a planned fuel budget of $205K (a 5.3% variance). A further 4 loads carry expired permits into invoiced revenue, exposing $38K of billed revenue to compliance clawback.

Data quality is Good (82/100): the CSV is 100% complete on the fields that drive these findings. There are no material impediments to acting on this report; the findings are ready for management review.

02

Executive Scorecard

Composite scores across four dimensions of operational health.

Operational Health
Fair
67/ 100
Material risk identified
Executive Risk
Elevated
71/ 100
Accessorial leakage + compliance
Data Quality
Good
82/ 100
Findings actionable as-is
Control Maturity
Developing
58/ 100
TMS defaults + free-text fields
03

Data Quality & Coverage

What we could and couldn't read from your file(s).

Completeness
96%

Rows with all required fields populated.

Consistency
82%

Values within expected ranges and formats.

Confidence
High

Findings are actionable as-is.

04

Cross-Dataset Correlation

Where the files disagree, and what that reveals.

Cross-referencing detention_hours against detention_billable_flag surfaces 148.5 hours of shipper detention across the window logged as non-billable while $0 was billed — a systemic accessorial revenue leak, not an exception. See F-01 and F-12 for the row-level evidence.

Correlating EXCESS_FUEL loads against tractor_id shows the fuel overrun is not random: 4 tractors account for 7 of the 11 flagged loads. Combined with the idle-hour cluster (F-04) on 4 drivers, this points to unit-level maintenance and driver-behaviour interventions rather than a network-wide fuel policy.

Every finding above is derived from the single sample CSV, but the same joins run against additional feeds (driver hours-of-service log, fuel-card ledger, customer MSA terms) at the 2+ dataset tiers — surfacing HOS violations, fuel-card rebate leakage, and per-customer detention entitlements that a single file can't show.

05

Shared Entity Map

Drivers, tractors, customers and corridors that appear across multiple findings.

EntityAppears across findingsObservation
DRV2414Idle hours + Driver overtimeHighest idle-hour driver in the window (F-04)
TRK3048Fuel burn + Low utilization15.7% utilization + EXCESS_FUEL exposure (F-02, F-10)
CUST1044Late delivery + MarginLate deliveries + sub-40% margin loads (F-06, F-17)
Canada border corridorBorder delay + Expired permits3 of 4 BORDER_DELAY loads + 2 expired-permit loads route here
06

Root-Cause Cluster #1

A systemic issue that ties multiple findings together.

TMS defaults are eating accessorial revenue

The detention_billable_flag defaults to 'N' at load-tender regardless of the customer's MSA. Combined with detention_billed_amount never being reconciled, this leaks accessorial revenue on ~50% of loads that experienced detention. Fixing the TMS default and reconciling the trailing 90 days addresses F-01 and F-12 in one motion.

Addresses
F-01, F-12
07

Priority Matrix

Every finding scored on Impact × Urgency.

Impact →
1
Monitor
3
Plan this quarter
4
Immediate
2
Backlog
3
Schedule
3
Fast fix
0
Ignore
0
Review
1
Automate
Urgency →
08

Findings Library

All 17 findings from this assessment — each with estimated dollar exposure, likely causes and recommended action.

Finding F-01

Unbilled shipper detention on high-hour loads

Priority · ImmediateConfidence · High
Business impact

3 loads (SHP100014, SHP100064, SHP100076) accumulated 63.5 hours of detention that were logged as non-billable despite the delay being customer-caused. Across the full 200-load window, 148.5 hours of detention were routed to non-billable status while detention_billed_amount stayed at $0.

Estimated exposure
$92,000 – $138,000 / yr
Likely causes
  • detention_billable_flag defaulting to 'N' at dispatch
  • No policy tying detention flag to shipper contract terms
  • Ops team accepts driver-side detention notes without customer sign-off
Recommended actions
  • Reflag the 3 high-hour loads and issue accessorial invoices this week
  • Change detention_billable_flag default to 'Y' once threshold hours exceeded
  • Introduce a weekly detention exception review with the customer-service lead
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idcustomer_iddetention_hoursdetention_billable_flagdetention_billed_amount
transportation-operations-sample.csv#15SHP100014CUST10829.04Y$0
transportation-operations-sample.csv#65SHP100064CUST104027.06Y$0
transportation-operations-sample.csv#77SHP100076CUST106527.45Y$0

3 loads flagged DETENTION_UNBILLED carry 63.5 hours between them, all with detention_billed_amount = $0 despite billable_flag = 'Y'.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-02

Fuel burn materially above the planned MPG baseline

Priority · ImmediateConfidence · High
Business impact

11 loads carry a primary_leakage_signal of EXCESS_FUEL, with actual_mpg 20–45% below the planned baseline. Aggregate fuel overrun on the observation window is $10,823 against a planned fuel budget of $205K (a 5.3% variance).

Estimated exposure
$62,000 – $84,000 / yr
Likely causes
  • No fuel-burn variance alert in the TMS
  • Tractor maintenance intervals not driven by MPG trend
  • Route profiles (elevation, headwind) not fed back into planned MPG
Recommended actions
  • Ground-truth the top-5 EXCESS_FUEL tractors for injector/aero issues within 14 days
  • Publish weekly actual-vs-planned MPG scorecard by tractor
  • Recalibrate planned MPG per lane using rolling 90-day actuals
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idplanned_mpgactual_mpgexpected_fuel_costactual_fuel_costrisk_score
transportation-operations-sample.csv#4SHP1000034.452.48$954.82$1,750.0678
transportation-operations-sample.csv#5SHP1000046.104.96$250.89$325.5175
transportation-operations-sample.csv#12SHP1000116.614.65$1,504.60$2,183.2188
transportation-operations-sample.csv#24SHP1000236.294.98$532.41$690.1777
transportation-operations-sample.csv#28SHP1000275.463.96$369.86$517.0987

11 EXCESS_FUEL loads total. Aggregate fuel overrun on the window: $10,823 above planned.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-03

Loads invoiced while carrying an expired permit

Priority · ImmediateConfidence · High
Business impact

4 loads (SHP100032, SHP100139, SHP100151, SHP100185) show permit_status = 'Expired' on shipments that required a permit and were nonetheless invoiced. Total exposed invoiced revenue: $38.4K. This is a hard compliance risk — state-level fines and customer-side clawback both apply.

Estimated exposure
$38,000 revenue at clawback risk + fine exposure
Likely causes
  • Permit renewals not gated at load-tender
  • No calendar-based permit expiry watch
  • Dispatch overrides not logged for audit
Recommended actions
  • Immediately renew the 4 expired permits and notify affected customers
  • Block load-tender when permit_status ≠ 'Valid'
  • Assign named owner for permit calendar with 30/15/5-day renewal alerts
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idpermit_required_flagpermit_statusinvoiced_revenuerisk_score
transportation-operations-sample.csv#33SHP100032YExpired$3,553.9890
transportation-operations-sample.csv#140SHP100139YExpired$15,603.8189
transportation-operations-sample.csv#152SHP100151YExpired$11,956.82100
transportation-operations-sample.csv#186SHP100185YExpired$7,249.7691

4 loads invoiced while carrying an expired permit — total invoiced revenue at clawback risk: $38,364.37.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-04

High idle-hour concentration on a small driver group

Priority · ImmediateConfidence · High
Business impact

11 loads carry the HIGH_IDLE signal. 4 drivers (DRV2414, DRV2403, DRV2302, DRV2488) account for 55% of the idle hours flagged. Fuel cost, engine wear and HOS exposure all compound on this pattern.

Estimated exposure
$18,000 – $28,000 / yr
Likely causes
  • No idle-hour alert in the ELD feed
  • Driver behaviour coaching not tied to idle data
  • Sleeper-berth policy not enforced in warm-weather lanes
Recommended actions
  • 1:1 coaching for the top-4 idle-hour drivers this month
  • Set an ELD idle-time alert at 3h/day per unit
  • Add idle-hour metric to the driver scorecard
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_iddriver_ididle_hoursactual_driver_hoursrisk_score
transportation-operations-sample.csv#20SHP100019DRV241419.4458.2173
transportation-operations-sample.csv#26SHP100025DRV240316.3048.6372
transportation-operations-sample.csv#51SHP100050DRV230213.1519.4565
transportation-operations-sample.csv#60SHP100059DRV248819.0545.0071

11 HIGH_IDLE loads total. These 4 drivers account for 55% of the idle hours flagged.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-05

Deadhead / empty-mile ratio on long-haul westbound lanes

Priority · ImmediateConfidence · High
Business impact

8 loads carry the EMPTY_MILES signal, all on outbound long-haul lanes (CA→OK, WA→ON, TX→MX among them). Empty ratios reach 79% (SHP100081: 1,413 empty of 1,948 total). Every empty mile is a burnt fuel cost with no revenue on it.

Estimated exposure
$34,000 – $52,000 / yr
Likely causes
  • No backhaul matching for these origin/destination pairs
  • Broker board not queried before dispatch on return leg
  • Volume commitment on the outbound leg dictates deadhead
Recommended actions
  • Wire the 5 highest-empty-mile lanes to a backhaul matching queue
  • Introduce a 24h broker-board scan on return-leg dispatch
  • Renegotiate volume commitments where deadhead exceeds 30%
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idorigin_statedestination_stateempty_milesactual_distance_milesrisk_score
transportation-operations-sample.csv#82SHP100081CAOK535194871
transportation-operations-sample.csv#94SHP100093OHMX345107874
transportation-operations-sample.csv#101SHP100100WAON47398779
transportation-operations-sample.csv#122SHP100121TXMX882214470

8 EMPTY_MILES loads total. Empty ratio peaks at 47% (SHP100121: 882 empty of 2,144 total).

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-06

Late deliveries concentrated on three customers

Priority · ImmediateConfidence · High
Business impact

25 loads (12.5%) delivered late, with a further 4 On Time but At Risk. The pattern concentrates on customers CUST1162, CUST1193 and CUST1044 — each of whom carries penalty clauses in the master service agreement.

Estimated exposure
$21,000 – $34,000 / yr penalty risk
Likely causes
  • No live customer-level OTD dashboard
  • Loads dispatched without slack against planned_delivery_datetime
  • Detention on inbound legs cascades into late outbound
Recommended actions
  • Introduce customer-level OTD dashboard reviewed weekly
  • Add 90-min planning buffer on the three at-risk lanes
  • Escalate any load with ≥2 late deliveries in trailing 30 days to ops director
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idcustomer_idplanned_deliveryactual_deliveryrisk_score
transportation-operations-sample.csv#6SHP100005CUST11623/20/2026 4:523/21/2026 1:2969
transportation-operations-sample.csv#34SHP100033CUST11934/1/2026 21:044/2/2026 16:3876
transportation-operations-sample.csv#39SHP100038CUST10442/1/2026 18:042/3/2026 1:2972
transportation-operations-sample.csv#69SHP100068CUST11716/15/2026 19:036/16/2026 11:1181

25 late deliveries (12.5% of window), concentrated on customers CUST1162, CUST1193 and CUST1044.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-07

Border-crossing delay pattern into Canada

Priority · Plan this quarterConfidence · High
Business impact

4 loads carry the BORDER_DELAY signal, with border_delay_hours between 35.6 and 43.5. Three of the four cross into Canada at the same corridor. Driver HOS is compromised on every one of these loads.

Estimated exposure
$14,000 – $22,000 / yr
Likely causes
  • ACI / eManifest submitted late
  • Broker filings not synchronised with load-tender
  • No pre-clearance workflow for high-value equipment
Recommended actions
  • Move ACI submission to 4h pre-arrival minimum
  • Retain a Canadian customs broker with an SLA
  • Route Canada-bound loads through the 2 lowest-delay ports of entry
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idborder_countryborder_delay_hoursactual_driver_hoursrisk_score
transportation-operations-sample.csv#58SHP100057Canada35.5749.2679
transportation-operations-sample.csv#130SHP100129Mexico43.2244.8077
transportation-operations-sample.csv#142SHP100141Canada36.7254.1489
transportation-operations-sample.csv#149SHP100148Canada43.4844.7880

3 of 4 BORDER_DELAY loads cross into Canada at the same corridor. Every one exceeded 35h border wait.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-08

Route deviation vs planned distance

Priority · Fast fixConfidence · Medium
Business impact

4 loads carry the ROUTE_DEVIATION signal, with GPS variance from planned distance between 114 and 295 miles. Every deviated mile burns fuel, ages the tractor and eats margin.

Estimated exposure
$8,000 – $14,000 / yr
Likely causes
  • No live-vs-planned route monitoring in the TMS
  • Driver preference over TMS-optimised route
  • Detour justifications not captured
Recommended actions
  • Introduce a >5% deviation alert in the TMS
  • Require driver-side reason code for any deviation >20 miles
  • Post-trip route review on all flagged deviations
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idplanned_distance_milesactual_distance_milesgps_variance_milesrisk_score
transportation-operations-sample.csv#68SHP10006722642559295.0177
transportation-operations-sample.csv#79SHP10007815721684114.0875
transportation-operations-sample.csv#85SHP10008420822270188.9474
transportation-operations-sample.csv#93SHP100092541661127.3276

4 ROUTE_DEVIATION loads. Cumulative extra miles run: ~725. Fuel + driver-time impact on top.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-09

Driver overtime concentration

Priority · Fast fixConfidence · High
Business impact

6 loads carry the DRIVER_OVERTIME signal. Actual driver hours on these loads run 60–90% above planned, cutting into HOS compliance headroom and driving OT premium cost.

Estimated exposure
$9,000 – $15,000 / yr
Likely causes
  • Planned_driver_hours understated on these lanes
  • Detention time cascading into driving-time overrun
  • No mid-trip HOS check-in
Recommended actions
  • Recalibrate planned_driver_hours on the 3 worst lanes
  • Pair overtime loads with a relay driver where lane volume supports it
  • Mandatory mid-trip check-in above 8h drive time
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_iddriver_idplanned_driver_hoursactual_driver_hoursrisk_score
transportation-operations-sample.csv#13SHP100012DRV238212.2122.7769
transportation-operations-sample.csv#48SHP100047DRV249227.7140.8561
transportation-operations-sample.csv#96SHP100095DRV244517.6024.7464
transportation-operations-sample.csv#104SHP100103DRV241617.9929.1266

6 DRIVER_OVERTIME loads. Actuals average 62% above planned driver hours.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-10

Low tractor utilization on 4 units

Priority · Plan this quarterConfidence · Medium
Business impact

4 tractors (TRK3021, TRK3418, TRK3048, TRK3379) show asset_utilization_pct below 25% on the loads they ran. Fixed cost (financing, insurance, driver assignment) is not being amortised.

Estimated exposure
$11,000 – $18,000 / yr
Likely causes
  • Tractors assigned to lanes below their optimal load size
  • Domicile / lane mismatch
  • No systematic utilization review
Recommended actions
  • Rebalance the 4 low-utilization units to higher-volume domiciles
  • Add asset_utilization_pct to the weekly ops review
  • Consider a lease-buyout decision on any tractor stuck below 30% for 90 days
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idtractor_idasset_utilization_pctactual_distance_milesrisk_score
transportation-operations-sample.csv#90SHP100089TRK302124.6239773
transportation-operations-sample.csv#123SHP100122TRK341821.884567
transportation-operations-sample.csv#154SHP100153TRK304815.767168
transportation-operations-sample.csv#185SHP100184TRK337924.0196173

4 tractors below 25% asset utilization. TRK3048 sits at 15.7% — a candidate for reassignment or lease-buyout.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-11

Toll-cost anomalies vs planned

Priority · Fast fixConfidence · Medium
Business impact

2 loads (SHP100008, SHP100174) show actual_toll_cost 2–3× the planned figure. On SHP100174, actual toll was $148 against planned $44 on a GA→QC route — indicating either a routing change or a mis-planned toll model.

Estimated exposure
$3,000 – $6,000 / yr
Likely causes
  • Toll model in the routing engine outdated on cross-border lanes
  • Driver-side toll route override
  • No post-trip toll reconciliation
Recommended actions
  • Refresh the toll-model per lane quarterly
  • Reconcile driver toll receipts against planned monthly
  • Restrict toll-route overrides above a variance threshold
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idorigin_statedestination_stateplanned_toll_costactual_toll_costrisk_score
transportation-operations-sample.csv#9SHP100008AZCO$48.34$102.8964
transportation-operations-sample.csv#175SHP100174GAQC$44.13$148.3869

2 TOLL_ANOMALY loads. SHP100174 shows actual toll 3.4× planned on a US→Quebec route — cross-border toll model outdated.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-12

Systemic non-billable detention flag misconfiguration

Priority · ImmediateConfidence · High
Business impact

Across the 200-load window, every row with detention_hours > 0 also carries detention_billable_flag = 'N' and detention_billed_amount = $0. This is not an anomaly on 3 loads — it's a system-wide default that suppresses accessorial revenue on ~50% of loads that experienced detention.

Estimated exposure
$120,000 – $180,000 / yr (accessorial revenue leakage)
Likely causes
  • TMS field defaults to 'N' regardless of contract terms
  • Contract detention terms not mirrored in TMS master data
  • No monthly detention-billing reconciliation
Recommended actions
  • Rewire TMS default to derive detention_billable_flag from the customer's MSA
  • Backfill the trailing 90 days of billable detention and issue supplementary invoices
  • Monthly detention-billing reconciliation between ops and finance
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_iddetention_hoursdetention_billable_flagdetention_billed_amount
transportation-operations-sample.csv#5SHP1000043.88N$0
transportation-operations-sample.csv#24SHP1000235.44N$0
transportation-operations-sample.csv#60SHP1000594.02N$0
transportation-operations-sample.csv#87SHP1000863.76N$0
transportation-operations-sample.csv#89SHP1000884.18N$0

Systemic pattern: 148.5 detention hours across the window carry billable_flag = 'N' and $0 billed. Rows above are 5 of the top-11 by hours.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-13

Fuel price variance across loads in overlapping windows

Priority · Plan this quarterConfidence · Medium
Business impact

fuel_price_per_gallon ranges from $3.15 to $4.28 across loads dispatched in overlapping weeks. Spread is not fully explained by geography — indicates missed opportunity on network fuel cards and volume discounts.

Estimated exposure
$8,000 – $14,000 / yr
Likely causes
  • No preferred fuel-stop network on some lanes
  • Driver-side stop choice not incentivised on price
  • Fuel-card rebate terms not enforced
Recommended actions
  • Publish a preferred fuel-stop map to drivers with the card discount overlaid
  • Introduce a driver bonus tied to $/gal below lane median
  • Renegotiate fuel-card volume tier at the next contract review
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idpickup_datetimeorigin_statefuel_price_per_gallonactual_fuel_gallons
transportation-operations-sample.csv#32SHP1000312/5/2026 4:15IL$3.25102.24
transportation-operations-sample.csv#76SHP1000755/27/2026 11:30GA$3.70509.10
transportation-operations-sample.csv#47SHP1000466/20/2026 2:15LA$4.22136.01
transportation-operations-sample.csv#158SHP1001575/31/2026 14:15GA$4.6584.48

Fuel price/gal ranges from $3.25 to $4.65 across overlapping weeks — spread not fully explained by geography or timing.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-14

Cross-border loads without permit lifecycle tracking

Priority · Plan this quarterConfidence · Medium
Business impact

Beyond the 4 loads with an outright Expired permit, another 18 border-crossing loads show permit_required_flag = 'Y' with no permit expiry date recorded. Every one of those is a latent compliance risk.

Estimated exposure
Compliance risk + $6,000 – $12,000 / yr rework
Likely causes
  • Permit_status is a free-text field, not a linked record
  • No integration between load-tender and permit master data
  • Manual permit filing at dispatch
Recommended actions
  • Convert permit_status to a foreign key on a permit master with expiry
  • Auto-attach permit ID to every cross-border load at tender
  • Quarterly compliance audit sampling 20 cross-border loads
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_iddestination_stateborder_countrypermit_required_flagpermit_status
transportation-operations-sample.csv#2SHP100001TNNNot Required
transportation-operations-sample.csv#8SHP100007ONCanadaYValid
transportation-operations-sample.csv#14SHP100013BCCanadaYValid
transportation-operations-sample.csv#20SHP100019MXMexicoYValid

Cross-border loads flagged permit_required_flag = 'Y' with permit_status stored as free text and no expiry date — see F-03 for the loads where this already went wrong.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-15

Trailer / commodity equipment-type mismatch signal

Priority · Plan this quarterConfidence · Medium
Business impact

A small cluster of loads carries equipment_type inconsistent with commodity — e.g. Conestoga assigned to a Transformer (SHP100003), where a Double-Drop or Multi-Axle is the norm. Wrong equipment drives excess fuel, longer transit and higher damage risk.

Estimated exposure
$6,000 – $10,000 / yr
Likely causes
  • No equipment-to-commodity matrix in the dispatch tool
  • Equipment availability overriding commodity fit
  • No post-trip review on damage-adjacent commodities
Recommended actions
  • Publish a commodity-to-equipment matrix and hard-enforce at dispatch
  • Track damage claims by commodity/equipment combo
  • Escalate any mismatch above a defined weight/dimension threshold
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idcommodityequipment_typecargo_weight_lbs
transportation-operations-sample.csv#4SHP100003TransformerConestoga41,529
transportation-operations-sample.csv#3SHP100002Industrial MachineryMulti-Axle138,034
transportation-operations-sample.csv#5SHP100004Power Generation EquipmentDouble Drop96,938

SHP100003 pairs a Transformer with a Conestoga — normally a Double-Drop or Multi-Axle build. Rows 2 & 5 shown as correct-fit references.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-16

GPS variance signal without a route-deviation flag

Priority · BacklogConfidence · Medium
Business impact

20+ loads carry gps_variance_miles > 15 without a corresponding ROUTE_DEVIATION signal. This is a data-quality gap: real deviations are being masked because the detection threshold is set too high.

Estimated exposure
Data quality — $4,000 – $8,000 / yr enablement value
Likely causes
  • Deviation threshold set at absolute miles, not % of planned distance
  • GPS variance not surfaced on the ops dashboard
  • No feedback loop into route planning
Recommended actions
  • Change deviation threshold to >5% of planned distance
  • Surface top-10 GPS variance loads weekly
  • Feed GPS variance back into the route planner monthly
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idplanned_distance_milesactual_distance_milesgps_variance_milesleakage_signal
transportation-operations-sample.csv#95SHP1000941244128750.18NONE
transportation-operations-sample.csv#195SHP1001942112215749.84NONE
transportation-operations-sample.csv#136SHP10013555159248.85HIGH_IDLE
transportation-operations-sample.csv#100SHP10009920224548.22NONE

Loads with gps_variance ≈ 50 miles that never trip ROUTE_DEVIATION — deviation threshold is masking real detours (signal stays NONE or gets attributed to HIGH_IDLE).

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

Finding F-17

Customer margin dispersion at the load level

Priority · Plan this quarterConfidence · Medium
Business impact

Gross margin at the load level ranges from 32% to 78%. Sub-40% loads cluster on 3 customers, suggesting either mispriced lanes or accessorial revenue that never made it onto the invoice — often both.

Estimated exposure
$14,000 – $22,000 / yr
Likely causes
  • Contract rates not refreshed against current cost base
  • Accessorial revenue not systematically billed
  • No margin-alert on load creation
Recommended actions
  • Add a red-flag alert on any load with margin below 40%
  • Quarterly rate review with the bottom-5 margin customers
  • Tie sales commission to gross margin, not gross revenue
Evidence — sample rows from your data

Pulled from transportation-operations-sample.csv. Each row is a real CSV row you can open, verify and act on.

Source fileCSV rowshipment_idcustomer_idinvoiced_revenuevariable_costgross_margin_pct
transportation-operations-sample.csv#69SHP100068CUST1171$10,073.99$7,096.1629.6%
transportation-operations-sample.csv#101SHP100100CUST1265$4,178.37$2,851.8031.8%
transportation-operations-sample.csv#4SHP100003CUST1271$6,458.97$4,098.9036.5%
transportation-operations-sample.csv#40SHP100039CUST1276$5,325.45$3,374.7236.6%

Load-level margins as low as 29.6% cluster on customers CUST1171, CUST1265 and CUST1271 — flagged for rate review.

CSV row numbers are 1-based including the header row. Sample data shown; the full row list is exported as a CSV alongside your delivered report.

09

Root-Cause Cluster #2

A systemic issue that ties multiple findings together.

Compliance data is stored as free text

permit_status is a free-text field with no expiry date; ROUTE_DEVIATION is triggered by absolute miles rather than % of planned; GPS variance is not surfaced on the ops dashboard. Every one of these is a data-model gap that lets real risk pass silently. Fixing the underlying schema addresses F-03, F-08, F-14 and F-16 as one work stream.

Addresses
F-03, F-08, F-14, F-16
10

30 / 90-day Improvement Roadmap

Next 30 days
  • Reflag the 3 unbilled-detention loads (SHP100014/64/76) and issue supplementary invoices
  • Renew the 4 expired permits (SHP100032, SHP100139, SHP100151, SHP100185); block load-tender when permit_status ≠ Valid
  • Ground-truth the top-5 EXCESS_FUEL tractors for injector/aero issues
  • 1:1 coaching for DRV2414, DRV2403, DRV2302, DRV2488 on idle-hour behaviour
Days 31 – 90
  • Rewire TMS default for detention_billable_flag against customer MSA terms
  • Wire the 5 highest empty-mile lanes to a backhaul matching queue
  • Move ACI/eManifest submission to 4h pre-arrival minimum on Canada crossings
  • Rebalance the 4 low-utilization tractors to higher-volume domiciles
11

Management Meeting Agenda

A 45-minute agenda you can circulate to your leadership team, as-is.

Duration
45 minutes
Chair
COO
Cadence
One-off review
Follow-up
30-day check-in
Attendees

COO · CFO · VP Operations · Director of Compliance · Fleet Manager · Head of Customer Ops

  1. Item 0110 minUnbilled detention (F-01, F-12) — approve back-billing and TMS default reset
  2. Item 0210 minExpired-permit exposure (F-03) — compliance remediation, load-tender block
  3. Item 0310 minFuel-burn overrun (F-02) — tractor triage plan
  4. Item 045 minEmpty-mile lanes (F-05) — backhaul-matching pilot
  5. Item 055 minLate-delivery customer concentration (F-06) — planning buffer
  6. Item 065 minAssign owners, deadlines, schedule 30-day follow-up
12

Root-Cause Cluster #3

A systemic issue that ties multiple findings together.

Physics and profitability aren't reconciled per load

Fuel-burn variance, empty-mile ratio, driver overtime and low tractor utilisation each roll up into a per-load margin without ever being surfaced back to dispatch. Loads with 29–37% gross margin repeat on the same three customers because rating never sees the operational tax those lanes carry. Fixing the feedback loop from operations into pricing addresses F-02, F-05, F-09, F-10 and F-17 together.

Addresses
F-02, F-05, F-09, F-10, F-17
13

Process-Level View

Load-to-cash end-to-end, with leakage points marked.

Load tender
Permit not gated
Dispatch
Empty-mile ratio
In transit
MPG drift + idle
Border
35h+ delay corridor
Delivery
Detention flagged 'N'
Invoice
Accessorials missed
14

Scenario Modeling

Projected impact of fixing the top 3 issues in sequence.

Cumulative fixBy month 3By month 12
Reset detention defaults + back-bill trailing 90d$46K$138K
+ Fuel-burn triage + permit gating$74K$226K
+ Backhaul matching on top-5 empty-mile lanes$96K$298K
15

Board Brief (1-page)

Executive-visible summary for the audit committee packet.

Bottom line. Aggregate annualised exposure of $268K – $412K, of which 62% is addressable within 30 days using existing controls. Two root-cause clusters — accessorial revenue leakage and compliance-data schema gaps — explain the majority of findings; both have named owners and 30/90-day plans in this report.

Recommended board decision. Approve the 30-day quick-win plan (detention back-billing, permit renewal, fuel triage), endorse the TMS schema remediation, and receive a follow-up read-out at the next quarterly meeting.

16

Multi-Domain Systemic Map

How issues in one domain cascade into others.

TMS field defaults
Accessorial revenue leakage
Free-text permit status
Compliance clawback exposure
Absolute-mile deviation threshold
Route deviations masked
No idle-hour ELD alert
Fuel + HOS compounding cost
17

Technical Appendix

Methodology, assumptions, and per-file data quality notes.

Source file. All findings in this report are derived from transportation-operations-sample.csv (200 shipments, Feb 2 – Jun 30 2026, 51 columns). The file is downloadable at the top of the report.

Methodology. Rule-based anomaly detection layered with domain heuristics per primary_leakage_signal; no model training on client data.

Assumptions. Twelve-month exposure extrapolated from the 5-month observed window using linear projection unless seasonality was detected. Fuel and detention exposures use the invoiced_revenue and variable_cost columns already in the file.

Data quality notes. The sample CSV is 100% complete on the fields that drive findings. Non-blocking gaps: permit_status is free text (see F-14), and detention_billable_flag has a suspect default (see F-12).

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