Problem Definition
RapidHaul is a last-mile delivery company headquartered in Atlanta, Georgia, with $680 million in annual revenue. The company operates across 12 U.S. metropolitan areas, processing over 320,000 packages daily for e-commerce retailers, direct-to-consumer brands, and marketplace sellers. RapidHaul employs approximately 8,400 drivers and operates a fleet of owned and leased delivery vehicles.
Over the past two years, RapidHaul's EBITDA margin has collapsed from 11% to 4%, falling from roughly $75 million to $27 million. Three forces are driving the deterioration: fuel costs have risen 30%, driver wages have climbed 22% due to labor market tightness, and the rate of failed first-attempt deliveries has spiked to 14% of all packages. Each failed delivery that requires a reattempt costs the company an average of $12 in marginal expense (driver time, fuel, vehicle wear).
Compounding the urgency, RetailMax, a major e-commerce client representing 28% of RapidHaul's revenue, is threatening to switch to a competitor unless RapidHaul can demonstrate a credible plan to reduce per-delivery costs by 15% within six months. The COO has hired you to diagnose the profitability problem, quantify the opportunity, and recommend a turnaround plan.
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If asked, please share that:
- RapidHaul charges clients an average delivery fee of $5.80 per package
- The average all-in cost per delivery is currently $5.45, up from $4.65 two years ago
- The company's delivery mix by geography is approximately 40% urban, 40% suburban, and 20% rural
- Urban route density averages 18 stops per hour, suburban 11 stops per hour, and rural 6 stops per hour
- RetailMax accounts for $190 million of RapidHaul's annual revenue
- The company has not raised prices in 18 months due to competitive pressure
- RapidHaul's top three competitors operate at EBITDA margins of 8-10%
Question 1Structuring
How would you structure your approach to diagnosing and fixing RapidHaul's profitability problem?
Hint · Structuring
Build 3–4 branches that are specific to this client and question, not a generic framework. Check they don't overlap and together cover the problem.
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- Share the cost-per-delivery breakdown (Exhibit 1) if the candidate asks about the cost structure
- Confirm the margin decline timeline: 11% two years ago, 7% one year ago, 4% today
Exhibit 1Cost Per Delivery Breakdown
| Cost Component | Current ($/delivery) | Two Years Ago ($/delivery) | Change |
|---|---|---|---|
| Driver labor | $2.09 | $1.71 | +22% |
| Fuel | $1.44 | $1.11 | +30% |
| Vehicle (lease, maintenance, depreciation) | $0.84 | $0.82 | +2% |
| Technology and routing | $0.44 | $0.43 | +2% |
| Hub and warehouse operations | $0.38 | $0.35 | +9% |
| Insurance and compliance | $0.26 | $0.23 | +13% |
| Total cost per delivery | $5.45 | $4.65 | +17% |
Note: Costs are averaged across all packages, including the amortized impact of failed delivery reattempts. Each reattempt incurs approximately $12 in additional marginal cost.
Source: RapidHaul case file
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A strong structure would organize the analysis into four workstreams:
1. Cost Diagnosis
- a. Decompose cost per delivery into components (labor, fuel, vehicle, technology, hub, reattempts)
- b. Identify which cost categories have grown fastest and why
- c. Benchmark against competitors operating at 8-10% EBITDA margins
2. Failed Delivery Analysis
- a. Understand root causes of the 14% failure rate (customer not home, wrong address, access issues)
- b. Segment failure rates by metro area, time window, and delivery type
- c. Quantify the total cost of reattempts and its impact on margin
3. Route and Network Optimization
- a. Analyze route density differences across urban, suburban, and rural segments
- b. Identify metros where density is too low to operate profitably
- c. Evaluate whether geographic rationalization could improve the cost base
4. Revenue and Pricing
- a. Assess whether pricing can be adjusted for underpriced segments (rural, reattempt surcharges)
- b. Evaluate the RetailMax relationship: volume vs. profitability trade-off
- c. Consider whether exiting unprofitable metros frees capacity for higher-margin work
What the interviewer is looking forShow guidanceHide guidance
A good candidate identifies that profitability has both a revenue and a cost dimension and does not jump immediately to cost-cutting. A strong candidate recognizes that the failed delivery rate is a compounding problem (it inflates fuel, labor, and vehicle costs simultaneously). An excellent candidate also considers the client retention risk as a constraint that shapes which levers are actionable within the six-month timeline.
Push candidates who produce generic frameworks (e.g., "revenue minus cost") to be specific: which costs, which routes, which client segments.
Question 2Numeracy
If RapidHaul reduces its failed delivery rate from 14% to 8% and simultaneously optimizes suburban routes to increase density from 11 to 14 stops per hour, what is the total annual profit impact?
Hint · Numeracy
Write the formula before you plug in numbers, keep units and zeros explicit, and sanity-check the order of magnitude at the end.
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Share the following data points as the candidate requests them (do not volunteer all at once):
- Annual package volume: approximately 117 million deliveries per year
- Delivery mix: 40% urban, 40% suburban, 20% rural
- Marginal cost per failed delivery reattempt: $12
- Fully loaded driver labor cost: $24 per hour
- Fuel and vehicle operating cost: $16 per hour of driving
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Part A: Failed Delivery Reduction (14% to 8%)
| Step | Calculation | Result |
|---|---|---|
| Annual deliveries | Given | 117M |
| Reduction in failure rate | 14% - 8% | 6 percentage points |
| Avoided reattempts | 117M x 6% | 7.0M reattempts |
| Cost per reattempt | Given | $12 |
| Annual savings | 7.0M x $12 | $84M |
Part B: Suburban Route Optimization (11 to 14 stops/hr)
| Step | Calculation | Result |
|---|---|---|
| Suburban deliveries | 117M x 40% | 46.8M |
| Current driver-hours needed | 46.8M / 11 stops per hr | 4.25M hours |
| New driver-hours needed | 46.8M / 14 stops per hr | 3.34M hours |
| Hours saved | 4.25M - 3.34M | 0.91M hours |
| Labor savings | 0.91M x $24/hr | $21.8M |
| Fuel and vehicle savings | 0.91M x $16/hr | $14.6M |
| Subtotal savings | $36.4M |
Combined Annual Profit Impact
| Component | Savings |
|---|---|
| Failed delivery reduction | $84M |
| Suburban route optimization | $36M |
| Total | $120M |
The $120 million in combined savings would increase EBITDA from $27 million to approximately $147 million, lifting the theoretical margin from 4% to roughly 22%.
Critical reality check: This theoretical maximum exceeds competitor margins of 8-10% and is unrealistic to achieve in full. A strong candidate should flag that:
- Reducing failed deliveries to 8% requires investment in technology (predictive delivery windows, SMS notifications, secure drop-off infrastructure) — estimated $15-25M in capex and $8-12M in ongoing opex
- Suburban route optimization requires dynamic routing software and driver retraining — implementation takes 6-9 months
- A realistic realization rate is 50-60% of theoretical savings, yielding $60-72M in annual EBITDA improvement
- This would bring the margin to approximately 13-15%, which is competitive with top-performing peers
Candidates who present the $120M as achievable without these qualifiers are demonstrating a calculation without business judgment.
What the interviewer is looking forShow guidanceHide guidance
This question tests structured quantitative thinking. A good candidate separates the problem into two independent calculations and states assumptions clearly. A strong candidate checks whether the combined savings are additive (they are, since they address different cost pools). An excellent candidate also pressure-tests the result by comparing it to current EBITDA and commenting on feasibility.
Question 3Judgement & Insights
Review Exhibit 2 and Exhibit 3. Which metros should RapidHaul consider exiting, and where should it double down? What patterns do you see?
Hint · Judgement & Insights
Read the exhibit title, axes and units first. Lead with the ‘so what’, then back it with one or two numbers.
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- Share Exhibit 2 (failed delivery rates by metro and time window) and Exhibit 3 (route density vs. profitability)
- If asked, confirm that exiting a metro takes approximately 90 days and requires renegotiating affected client contracts
Exhibit 2Failed Delivery Rate by Metro and Time Window
| Metro | Overall Rate | Morning (6am-12pm) | Afternoon (12-5pm) | Evening (5-9pm) |
|---|---|---|---|---|
| New York | 19% | 13% | 17% | 26% |
| Miami | 18% | 13% | 17% | 24% |
| Philadelphia | 17% | 12% | 16% | 23% |
| Chicago | 16% | 11% | 15% | 22% |
| Seattle | 15% | 10% | 14% | 20% |
| Los Angeles | 14% | 9% | 13% | 19% |
| Atlanta | 13% | 9% | 12% | 17% |
| Dallas | 12% | 8% | 11% | 16% |
| Houston | 11% | 7% | 10% | 15% |
| Minneapolis | 11% | 7% | 10% | 15% |
| Phoenix | 10% | 7% | 9% | 14% |
| Denver | 9% | 6% | 8% | 12% |
| Company Average | 14% | 10% | 13% | 19% |
Source: RapidHaul case file
Exhibit 3Route Density vs. Profitability by Metro
| Metro | Avg Stops/Hr | Annual Revenue ($M) | Contribution Margin (%) | Daily Volume (K packages) |
|---|---|---|---|---|
| New York | 16.2 | $98 | 6.1% | 46 |
| Los Angeles | 14.8 | $85 | 5.3% | 40 |
| Chicago | 13.5 | $72 | 3.8% | 34 |
| Philadelphia | 13.8 | $52 | 3.2% | 24 |
| Dallas | 12.5 | $55 | 3.5% | 26 |
| Houston | 12.1 | $58 | 2.9% | 27 |
| Seattle | 13.2 | $45 | 4.1% | 21 |
| Miami | 12.8 | $52 | 1.8% | 24 |
| Atlanta | 11.9 | $48 | 2.1% | 22 |
| Phoenix | 11.4 | $42 | 1.2% | 20 |
| Denver | 10.8 | $38 | 0.5% | 18 |
| Minneapolis | 9.6 | $35 | -1.4% | 16 |
Source: RapidHaul case file
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Pattern 1: Route density strongly correlates with profitability. Metros above 13 stops per hour generally achieve margins above 3%, while those below 11 stops per hour struggle to break even (Denver at 0.5%, Minneapolis at -1.4%).
Pattern 2: High failure rates erode margins even in dense markets. Miami has reasonable density (12.8 stops/hr) but a disproportionately low margin (1.8%) driven by its 18% failure rate. Similarly, Philadelphia has strong density (13.8) but only 3.2% margin, dragged down by 17% failures. Compare this to Dallas, which has lower density (12.5) but achieves 3.5% margin with only 12% failures.
Pattern 3: Evening delivery windows drive the majority of failures. Across all metros, the 5-9 PM window shows failure rates 60-80% higher than morning windows. This suggests that customer availability, not address accuracy, is the primary failure driver.
Recommendations:
| Category | Metros | Action | Rationale |
|---|---|---|---|
| Exit | Minneapolis | Wind down within 90 days | Negative margin, lowest density, no path to profitability |
| Restructure | Denver | Consolidate to urban-only zones | Marginal profitability; reduce rural exposure |
| Fix operations | Miami, Philadelphia | Reduce failure rates with smart delivery windows | Good density undermined by high failure rates |
| Double down | New York, Los Angeles, Seattle | Invest in capacity and client acquisition | Highest margins, strong density, room to grow volume |
| Maintain | Chicago, Houston, Dallas, Atlanta, Phoenix | Optimize incrementally | Moderate margins with improvement potential |
Exhibit trap — intentional outlier: Denver appears to have low density (10.8) and low margin (0.5%), making it an obvious exit candidate. However, Denver also has the lowest failed delivery rate (9%) of any metro. A strong candidate will notice this and investigate further: Denver's problem is purely structural (low population density in the delivery footprint) not operational. Exiting Denver is the right call, but for a different reason than Miami or Philadelphia. Candidates who treat all underperformers the same way are missing this distinction.
Exiting Minneapolis alone would eliminate approximately $35 million in revenue but remove a $0.5 million annual loss and free up driver and vehicle capacity that can be redeployed to higher-margin metros.
What the interviewer is looking forShow guidanceHide guidance
A good candidate identifies the obvious exit candidates (negative or near-zero margin metros). A strong candidate connects high failed delivery rates to margin erosion even in metros with decent route density (e.g., Miami at 12.8 stops/hr but only 1.8% margin due to 18% failure rate). An excellent candidate distinguishes between structural problems (low density markets like Minneapolis) and operational problems (high failure rates in dense markets like Philadelphia) and recommends different interventions for each.
Question 4Synthesis
The COO needs to present two plans: one to RetailMax's procurement team next week, and one to the board next month. What should each plan contain?
Hint · Synthesis
Answer first: the recommendation, two or three reasons with numbers, then risks and next steps.
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- RetailMax's primary concern is cost per delivery; they want a 15% reduction
- The board cares about sustainable margin recovery and long-term competitive positioning
- RapidHaul's current cost per delivery is $5.45; a 15% reduction would bring it to $4.63
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Plan 1: RetailMax Presentation (Next Week)
The goal is to retain $190 million in revenue by demonstrating a credible path to 15% cost reduction (from $5.45 to $4.63 per delivery).
- Commit to reducing failed deliveries on RetailMax packages from 14% to 8% within 90 days by implementing predictive delivery windows, SMS confirmation before dispatch, and secure drop-off authorization. This alone reduces per-delivery cost by approximately $0.72 (the marginal reattempt cost savings of 6 percentage points times $12, amortized across all deliveries).
- Offer RetailMax priority routing in high-density metros (New York, Los Angeles, Chicago) where costs are lowest, reducing their blended cost per delivery.
- Propose a 12-month volume commitment from RetailMax in exchange for a guaranteed cost-reduction schedule tied to quarterly milestones.
Target: achieve $4.63 or better within 6 months, with interim progress visible at 90 days.
Plan 2: Board Presentation (Next Month)
The goal is to restore EBITDA margin from 4% to at least 10% within 18 months.
- Phase 1 (0-6 months): Operational quick wins generating approximately $120 million in annualized savings
- Failed delivery reduction program across all metros ($84M potential)
- Suburban route optimization through dynamic routing technology ($36M potential)
- Conservative realization target: 50-60% of potential, or $60-72M
- Phase 2 (6-12 months): Network rationalization
- Exit Minneapolis, restructure Denver to urban-only
- Redeploy freed capacity (drivers, vehicles) to New York, Los Angeles, and Seattle
- Expected margin uplift: 1-2 percentage points
- Phase 3 (12-18 months): Pricing and mix optimization
- Introduce delivery-window pricing (lower cost for flexible windows, premium for guaranteed)
- Implement reattempt surcharges passed through to clients with high failure rates
- Selective rural delivery repricing or exit
Key risks to flag:
- RetailMax departure before initiatives take effect (mitigate with contractual commitments)
- Driver attrition during operational changes (mitigate with retention bonuses funded by savings)
- Technology investment required for dynamic routing ($8-12M estimated capex)
Contributed by CaseDrill practice community
What the interviewer is looking forShow guidanceHide guidance
A good candidate distinguishes between the two audiences and tailors recommendations accordingly. A strong candidate proposes specific, quantified commitments for RetailMax rather than vague promises. An excellent candidate sequences the initiatives by timeline (quick wins for RetailMax, structural changes for the board) and identifies risks to each plan.