WGS Operational Intelligence
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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 one operational dataset (transportation-operations-sample.csv, 200 shipments) surfaced 4 findings. 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

Findings Library

All 4 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.

05

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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