Sample · Two-Dataset Correlation

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 the transportation dataset correlated with a companion driver-hours-of-service log surfaced 9 findings across the correlated files. 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

Findings Library

All 9 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.

08

Quick Wins (0–30 days)

Actions you can take this month without approvals.

  • Reflag and back-bill the 3 unbilled-detention loads this week
  • Renew the 4 expired permits before the next dispatch on those lanes
  • Set an ELD idle-time alert at 3h/day per tractor
  • Publish a weekly actual-vs-planned MPG scorecard by tractor
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Two-Dataset Correlation
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Two files, cross-correlated. Find the leakage no single department can see.

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