AI Skill Report Card
Analyzing Maritime Logistics
Maritime Logistics Analysis
Quick Start12 / 15
Python# Extract key vessel performance metrics vessel_data = { 'vessel_name': 'MILD JASMINE', 'network_savings': 24.0, 'status': 'UNDER WAY USING ENGINE', 'draft': 7.8, 'next_port': 'CMPSHK', 'eta': '02 Apr 2026 12:52' } # Calculate network efficiency total_savings = sum([vessel['savings'] for vessel in fleet if vessel['savings'] > 0]) apt_performance = (completed_calls / total_calls) * 100
Recommendation▾
Add more concrete input/output examples showing actual dashboard data structures and expected analysis outputs
Workflow13 / 15
Progress:
- Extract fleet overview metrics (total vessels, APT percentages)
- Identify vessels with significant time savings/losses
- Analyze port congestion patterns
- Map vessel routing efficiency
- Generate optimization recommendations
1. Fleet Performance Analysis
- Overall APT: 83.0% (network-wide performance)
- Completed vs Incoming calls ratio
- Vessel status distribution (MOORED, UNDER WAY, AT ANCHOR)
2. Time Savings Identification
- Positive savings: MILD JASMINE (+24.0 hrs), MILD ORCHID (+10.6 hrs)
- Major delays: CMA CGM ALASKA (-227.5 hrs, 241.0 hrs wait time)
- Zero impact vessels requiring attention
3. Port Congestion Assessment
- PSASG: High traffic hub (multiple pending vessels)
- MDNBG, PSAGP: Secondary bottlenecks
- Route optimization opportunities through alternate ports
Recommendation▾
Include specific thresholds and decision frameworks (e.g., when exactly to escalate delays, what constitutes 'high congestion')
Examples15 / 20
Example 1: Input: Control tower data showing vessel MILD JASMINE with 24.0 hrs network savings Output: "High-performing vessel - replicate routing strategy. SIPG departure optimization achieved 24hrs ahead of schedule."
Example 2: Input: CMA CGM ALASKA showing -227.5 hrs APT savings, 241.0 hrs wait time Output: "Critical delay - investigate PSASG berth allocation. 10+ day delay requires immediate intervention and customer notification."
Recommendation▾
Provide templates for common analysis outputs like fleet performance reports or optimization recommendations
Best Practices
- Monitor vessels with draft >14m for port restrictions
- Flag negative APT savings >100 hours for immediate review
- Track repeat high-performers (MILD series vessels) for best practice replication
- Cross-reference ETA vs Berthing Date for schedule accuracy
- Use regional clustering (PSASG, MDNBG hubs) for optimization
Common Pitfalls
- Don't ignore vessels with 0.0 network savings - may indicate missed optimization
- Avoid focusing only on large negative savings - systematic small delays compound
- Don't assume PENDING status means on-schedule - verify against ETA
- Avoid berth allocation without considering vessel draft restrictions
- Don't optimize individual vessels without considering network effects