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I came across market sizing case. The question is: "How many freight trucks are there in US?”. How will you approach it?

Market sizing
New answer on Feb 24, 2020
3 Answers
2.9 k Views
Spartak asked on Feb 23, 2020

My initial approach was: Number = Total weight of transported goods / truck capacity. Then the issue is to estimate the total weight of the transported goods by freight trucks.

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replied on Feb 24, 2020
McKinsey / Accenture Alum / Got all BIG3 offers / Harvard Business School


You can:

  1. Calculate the total number of cars in the US (# of households, cars per household)
  2. Add the certain % of commercial vehicles (police, service vehicles, etc)
  3. Assume the ratio of freight trucks / cars you see on the roads based on personal experience


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Samuel replied on Feb 23, 2020

Hi Spartak,

I think the best thing to deal with an open-ended market sizing case like this is to first bound the problem. Identify the major industry driver for the use of freight trucks and clarify with your interview: "I know there are X, Y, Z use of freight trucks, based on A, B, C, is it okay if I estimate the #freight required for the transportation of consumer goods?" If your interview rejects every attempt of yours to bound the problem, then you may want to approach it differently.
All Marketing questions are best solved by first understanding whether it is a demand-driven question or a supply-constrained case.

Are the #Freight trucks in the US so readily available that the demand for it can be matched by the supply of freight trucks? You could argue yes/no. I would say no because if supply is made to always match demand, then you might have to account for all major demands for freight trucks. Besides, arguing for yes is just going to complicate the estimation because now you have to account for all major demands for freight trucks. So this may not be best solved through the demand-driven approach.

It is supply-constrained if the demand for it does not automatically drive up its supply. For instance, the demand for American Visas in my country does not drive up the supply of AmE visa at the embassy (else we would have all left our country lmao).

You can argue that this case is supply-constrained. Else how would you make a case for supply chain management?
Now settling for the supply-constrained approach, you can now say #Freight trucks in US/year is the function of production capacity of factories in US/year + #Freight trucks imported/year + the #Freight trucks available from the previous year. (Assuming that damaged trucks are not part of the equation). Something like that. That should be manageable and should help you get to a realistic number.

Another approach is to use relative thinking. Compare the #cars on a typical highway to the plausible #frieght trucks on a typical highway/week multiplied by a correction factor (to account for freight trucks making multiple trips a week).

Make sure to cross-check your answer.

Estimation cases were always a pain in the ___ for me until I found FirmsConsulting. If you want to more details on the demand/supply approach I summarized above, you can find it here:

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Spartak on Feb 23, 2020

Thank you for your complete and exhaustive answer!

MB on Jul 12, 2022

The links you provided to the estimation cases doesnt work. Do you have updated ones ?

Content Creator
replied on Feb 24, 2020
#1 BCG coach | MBB | Tier 2 | Digital, Tech, Platinion | 100% personal success rate (8/8) | 95% candidate success rate

Hi Spartak,

Sorry but this is all wrong.

When you approach these problems you need to think about how to break it down logically. There's no "right" answer but there are overly simple or wrong answers.

You need to think about things you can reasonably deduce...then from a series of steps where you make accurate-enough estimates, you get to the answer.

How in the world will you accurately estimate the total weight of transported goods?

For example, you can probably estimate how many cars are in the US (based on population), then approximate freight as a %.

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Vlad gave the best answer


McKinsey / Accenture Alum / Got all BIG3 offers / Harvard Business School
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