Transportation and logistics cases test your ability to optimize asset-intensive operations under capacity and network constraints. Based on our analysis of 180+ T&L cases, the core challenge is maximizing asset utilization while meeting service level commitments—a constant tension between efficiency and flexibility.
The Transportation Value Chain
Understanding where you are in the value chain shapes your analysis approach:
flowchart LR
A[Shipper] --> B[Freight Forwarder]
B --> C[Carrier]
C --> D[Last Mile]
D --> E[Recipient]
C --> C1[Trucking]
C --> C2[Rail]
C --> C3[Ocean]
C --> C4[Air]
subgraph Network
B
C
D
end
Key Transportation Metrics
These metrics appear in virtually every T&L case. Commit them to memory:
| Metric | Definition | Good Benchmark | Why It Matters |
|---|---|---|---|
| Revenue per Mile | Total revenue / Miles driven | Varies by mode | Core pricing metric |
| Cost per Mile | Total cost / Miles driven | $1.50-2.50 (trucking) | Margin driver |
| Load Factor | Actual load / Maximum capacity | >85% | Asset utilization |
| Empty Miles | Miles without cargo / Total miles | <15% | Efficiency indicator |
| On-Time Delivery | Deliveries on time / Total deliveries | >95% | Service quality |
| Dwell Time | Time waiting at pickup/delivery | <2 hours | Hidden cost driver |
Cost Structure Analysis
Transportation costs follow predictable patterns. This breakdown helps identify improvement levers:
mindmap
root((Transportation Costs))
Fixed Costs
Vehicles
Depreciation
Financing
Insurance
Facilities
Terminals
Warehouses
Labor
Salaried staff
Benefits
Variable Costs
Fuel
Price volatility
Efficiency
Driver Labor
Per-mile pay
Hours of service
Maintenance
Scheduled
Unscheduled
Tolls & Fees
Highway tolls
Port charges
Indirect Costs
Administration
Technology
Compliance
Typical Cost Breakdown by Mode
| Cost Category | Trucking | Rail | Ocean | Air |
|---|---|---|---|---|
| Labor | 30-40% | 25-30% | 15-20% | 25-30% |
| Fuel | 25-35% | 15-20% | 40-50% | 25-35% |
| Equipment | 15-20% | 30-40% | 20-25% | 25-30% |
| Other | 15-25% | 15-25% | 15-20% | 15-20% |
Common T&L Case Patterns
Pattern 1: Network Optimization
Situation: Distribution network is inefficient—too many facilities, wrong locations, or suboptimal routing.
Framework:
- Map current network: origins, hubs, destinations, volumes
- Analyze cost drivers: transportation vs. inventory vs. facility costs
- Model alternatives: consolidation, hub relocation, mode shifts
- Calculate total landed cost for each scenario
- Consider service level trade-offs
quadrantChart
title Network Strategy Trade-offs
x-axis Low Cost --> High Cost
y-axis Slow Service --> Fast Service
quadrant-1 Premium service
quadrant-2 Optimal balance
quadrant-3 Cost leader
quadrant-4 Suboptimal
Direct shipping: [0.75, 0.85]
Hub and spoke: [0.45, 0.55]
Milk run: [0.3, 0.4]
Multi-tier: [0.55, 0.7]
Pattern 2: Fleet Economics
Situation: Fleet costs are too high or asset utilization is poor.
Analysis approach:
- Calculate current utilization metrics: load factor, empty miles, revenue per mile
- Benchmark against industry standards
- Identify root causes: demand imbalance, routing inefficiency, maintenance issues
- Evaluate solutions: dynamic pricing, backhaul optimization, fleet right-sizing
- Quantify improvement potential
Pattern 3: Mode Selection
Situation: Which transportation mode should we use?
Evaluation matrix:
| Factor | Truck | Rail | Ocean | Air |
|---|---|---|---|---|
| Speed | Fast | Medium | Slow | Fastest |
| Cost | Medium | Low | Lowest | Highest |
| Flexibility | High | Low | Low | Medium |
| Capacity | Medium | High | Highest | Low |
| Reliability | High | Medium | Medium | High |
| Best for | Regional, time-sensitive | Bulk, long-haul | International, large volume | High-value, urgent |
Pattern 4: Last-Mile Optimization
Situation: Last-mile delivery costs are unsustainable or service is poor.
Key levers:
- Density: Deliveries per route—higher is better
- Failed deliveries: Each requires re-attempt, doubling cost
- Time windows: Tighter windows reduce efficiency
- Vehicle type: Right-sizing for urban vs. suburban
- Alternative models: Lockers, pickup points, crowdsourced delivery
Industry Disruption Themes
Transportation cases increasingly involve disruption dynamics:
E-commerce impact: Shift from B2B pallets to B2C parcels. Implication: smaller shipments, more stops, higher cost per unit.
Autonomous vehicles: Potential to eliminate driver costs (30-40% of trucking). Implication: massive cost restructuring, but regulatory and technical uncertainty.
Electric vehicles: Lower fuel costs but higher upfront investment and range limitations. Implication: TCO analysis becomes critical for fleet decisions.
Platform logistics: Uber Freight, Convoy, Flexport digitizing fragmented markets. Implication: price transparency, capacity optimization, but margin pressure.
Sustainability pressure: Carbon reporting, emissions regulations, customer demands. Implication: mode shifts, network redesign, green premium pricing.
Sample Case Walkthrough
Prompt: “A regional trucking company’s margins have declined from 8% to 3% over three years. How should they respond?”
Strong approach:
-
Understand the decline: What’s driving margin compression? Revenue per mile down, costs up, or both?
-
Revenue analysis:
- Pricing trends: Have rates declined? Market-wide or company-specific?
- Mix shift: More competitive lanes? Lower-margin customers?
- Utilization: Is load factor declining?
-
Cost analysis:
- Fuel: Price changes and efficiency trends
- Labor: Driver wages, benefits, turnover costs
- Equipment: Age of fleet, maintenance costs
- Overhead: Fixed cost leverage changing?
-
Competitive position:
- Where do we win? Where do we lose?
- What’s our cost position vs. competitors?
- Are we differentiated or commoditized?
-
Improvement options:
- Pricing: Selective increases, surcharges, contract renegotiation
- Network: Lane optimization, backhaul improvement
- Operations: Fuel efficiency, maintenance programs, driver retention
- Strategy: Niche focus, exit unprofitable lanes, differentiation
-
Recommendation: “Based on the analysis, I recommend a three-pronged approach: (1) Selective price increases on lanes where we have leverage, (2) Operational improvements targeting 2-3% margin recovery, and (3) Strategic exit from chronically unprofitable lanes. This should restore margins to 6-7% within 18 months.”
Key Takeaways
- Transportation cases center on network economics and asset utilization
- Master the core metrics: revenue per mile, cost per mile, load factor, empty miles
- Fixed costs are high—utilization is the primary profitability lever
- Network optimization requires total landed cost analysis, not just transportation cost
- Mode selection depends on speed, cost, flexibility, and reliability trade-offs
- Disruption themes (e-commerce, EVs, platforms) provide context for strategic questions
- Always quantify: T&L cases have abundant data for financial analysis
Practice Transportation Cases
Develop operational thinking with operations cases and cost reduction cases from the case library. Ready to demonstrate your skills under pressure? Try our AI Mock Interview.