Estimation question
How many drones would be needed to deliver all of Fedex's packages in the US?
- Estimation
- Hard
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What this question tests
Multi-step estimation combining volume, drone capacity, and operational constraints.
How to approach it
- Estimate FedEx's daily US package volume, assume roughly 15 million packages per day based on its scale relative to known competitors.
- Assume what share could realistically go by drone, likely small last-mile lightweight packages, assume 20 percent qualify, about 3 million daily.
- Estimate deliveries per drone per day, accounting for battery range, load and unload time, and charging, assume roughly 20 deliveries per drone.
- Calculate: 3,000,000 divided by 20 gives roughly 150,000 drones needed.
- Sanity check against real-world drone delivery pilots, which operate at far smaller scale, suggesting this is plausible as a full national buildout.
- State the estimate as a range, roughly 100,000 to 200,000 drones, given uncertainty in eligibility share and throughput.
What a strong answer includes
- Decomposes the problem into volume, eligibility share, and per-drone throughput instead of guessing a single number.
- States the eligible-package assumption explicitly, weight and distance limits, a realistic operational constraint often missed.
- Accounts for real drone throughput limits, battery and charging, rather than assuming continuous operation.
- Grounds the sanity check in real-world drone delivery pilots' current scale.
Common mistakes
- Assuming 100 percent of packages could go by drone, ignoring weight and range limits.
- Skipping the per-drone daily throughput constraint.
- Not sanity-checking against real-world drone delivery pilot scale.
Likely follow-up questions
- What package weight and distance limits would you assume for drone eligibility?
- How would weather and airspace regulation affect this estimate?
- How would you phase a rollout instead of deploying all drones at once?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop