Gulf Shrimping Operations
Fleet Optimization & Market Timing for a Commercial Shrimping Fleet
$127K
Fuel Savings
annual
18%
Revenue Increase
year-over-year
2.3x
CPUE Spread
top vs bottom
01The Challenge
A five-boat commercial shrimping fleet operating out of Port Isabel was running on instinct. Captains chose fishing areas based on habit, priced their catch at whatever the dock buyer offered that morning, and had no visibility into which trips were actually profitable after accounting for fuel, crew, and ice costs.
Three problems drove the engagement:
- Blind trip economics: The fleet owner knew total revenue and total fuel cost per month, but couldn't attribute profitability to individual trips, areas, or captains. Some trips were almost certainly losing money — but which ones?
- Fuel waste: Captains routinely steamed to distant grounds (Aransas Pass, 3+ hours each way) without evidence that the higher catch justified the $280+ fuel cost. Closer grounds like Brownsville Ship Channel were underutilized.
- No market timing: Shrimp prices fluctuate 40% seasonally, but the fleet sold everything at the dock the same day. No one tracked whether holding catch for 24–48 hours in cold storage could capture a price swing.
Data Landscape
02Our Approach
We built a trip-level economics model that assigns profit/loss to every trip, then layered on an area optimization model and a price forecasting system to answer: where should each boat go tomorrow, and should we sell or hold today's catch?
- XGBoost — gradient-boosted model predicting trip profitability from area, season, fuel cost, and gear type
- Trip Economics Engine — cost–revenue simulation modeling catch × price minus fuel, crew, and ice costs per trip
- Price Forecasting — 14-day wholesale shrimp price forecast using seasonal decomposition and market signals
- Captain Benchmarking — multi-metric performance radar comparing CPUE, fuel efficiency, ROI, and consistency across captains
- Pandas + Recharts — data pipeline and interactive fleet dashboard for the operations manager
Trip Logs
2,200+ trips
Cost Modeling
Fuel, crew, ice
XGBoost Predict
Trip ROI model
Price Forecast
14-day ahead
Fleet Dashboard
Daily decisions
03Key Findings
Trip Profitability by Fishing Area
Each dot is one trip. X-axis is fuel cost, Y-axis is revenue. Trips above the diagonal are profitable; below are losses. Laguna Madre and Baffin Bay consistently cluster in the high-revenue zone. Aransas Pass shows high fuel costs with inconsistent returns.
Seasonal Catch by Area
Monthly catch volume (thousands of lbs) by fishing area. May–July is peak brown shrimp season across all areas. Laguna Madre and Baffin Bay dominate year-round, while Brownsville Ship Channel peaks narrowly in June.
Captain Performance Comparison
Five-axis radar comparing captains on catch-per-unit-effort (CPUE), fuel efficiency, trip ROI, consistency (low variance), and season coverage. Capt. Martinez leads on CPUE and ROI; Capt. Garcia excels at fuel efficiency.
Shrimp Price Trend & Forecast
Monthly wholesale shrimp price ($/lb) with 14-day forecast overlay. Prices peak in winter (low supply) and dip in summer (peak catch). The forecast model enables hold/sell decisions — holding catch during rising price windows captured an additional $42K in the first year.
04Business Impact
Projected Annual Value
$127K fuel savings + 18% revenue increase
The trip economics model revealed that 23% of trips to Aransas Pass were net-negative after fuel costs. Redirecting those trips to Laguna Madre and Baffin Bay cut annual fuel spend by $127K while maintaining catch volume.
Captain benchmarking exposed a 2.3x spread in catch-per-unit-effort (CPUE) between the best and worst performers. Pairing Capt. Williams with Capt. Martinez for three months of mentored trips narrowed the fleet CPUE spread from 3.1x to 1.8x.
The price forecast model identified 34 hold-worthy windows in the first year — periods where holding catch 24–48 hours would capture a 5–12% price increase. Implementing a cold-storage hold protocol added $42K in incremental revenue.
05Technical Details
Trip Economics Model
- Granularity: per-trip, per-vessel
- Revenue: catch_lbs × species_price × grade_adjustment
- Costs: fuel (gallons × diesel_price) + crew_share + ice
- ROI = (revenue - total_cost) / total_cost
Area Optimization (XGBoost)
- Features: area, month, sea_state, moon_phase, recent_catch
- Target: trip_roi (regression)
- Validation: 5-fold CV, RMSE = 0.18 on normalized ROI
- Feature importance: area > month > recent_catch
Price Forecast
- Method: seasonal ARIMA + supply indicators
- Horizon: 14-day rolling forecast
- Accuracy: MAPE = 8.2% on 3-month holdout
- Hold signal: >5% predicted price increase in next 48h
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