Fuel Cost Optimization and Theft Detection for Maritime Fleets
Reduce fuel waste, detect suspicious consumption behavior, and standardize captain throttle guidance with auditable operational data.
Status: Pilot-ready module for route-level baseline, anomaly signal, and weekly savings governance.
Operational scope
Qail Optimize is built for teams that already run daily schedules and need measurable fuel control, not generic dashboard charts.
Route-level burn baseline
Compute expected liters by vessel class, route, departure window, and load bucket. Baseline refresh can be weekly or bi-weekly.
Anomaly and theft signal layer
Detect rapid-drop events while idle, route variance outliers, and refuel mismatch between purchased and observed tank increase.
Throttle discipline program
Define target speed/throttle bands per route and weather window. Track violations and route-level correction rate by captain shift.
Weekly ROI close
Publish weekly savings summary with gross savings, net savings, and confidence score based on baseline quality and data completeness.
Core KPIs and formulas
| KPI | Formula | Use |
|---|---|---|
| Liters per voyage | (Fuel start + refuel in voyage) - fuel end | Main operating efficiency metric |
| Liters per nautical mile | Liters per voyage / distance NM | Cross-route comparability |
| Fuel cost per paid pax | (Liters per voyage x fuel price) / paid passengers | Pricing and margin control |
| Savings vs baseline | (Baseline liters - actual liters) x fuel price | Weekly ROI reporting |
| Anomaly rate | Anomalous voyages / total voyages | Quality and control signal |
Recommendation: communicate savings as "up to" range until 8-12 weeks of stable baseline data are available.
Data contract (minimum fields)
Voyage context
- vessel_id, route_id, departure_time, captain_shift
- distance_nm, weather_band, wave/current note
- passenger_count, payload_bucket
Fuel and telemetry
- fuel_start_liter, fuel_end_liter, refuel_liter
- fuel_price_per_liter, supplier_receipt_id
- optional: RPM, throttle band, idle duration
30-60-90 rollout
0-30 days
Data onboarding, baseline build, and first anomaly tuning for 1 vessel class and 1 route cluster.
31-60 days
Captain feedback loop, throttle guidance enforcement, and weekly management review cadence.
61-90 days
Fleet expansion, KPI normalization, and contract-level ROI report for leadership close.
Pilot success thresholds
- Data completeness >= 95% on pilot routes
- Baseline error band within +/-8% for recurring schedules
- Anomaly investigation SLA <= 24 hours for high-severity cases
- Measured net savings trend positive for at least 4 consecutive weeks