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It is often necessary to make quick estimates when neither time nor resources are available for making traditional assessments. This is particularly true at the idea stage of product development when even a gross estimate could be useful for heading off ill-advised expenditures. The Fermi question, with which the scientific and engineering community has long been comfortable, is a helpful starting point for gaining insight into order of magnitude estimation. Although numerous worked-out solutions to Fermi questions are available, a systematic approach to solving them is not. This led the authors to develop a methodology which could be easily implemented in a traditional business course. A more pragmatic reason for introducing Fermi questions to business students is that corporations now employ these questions in the job interview process, as a means of gauging applicants' analytical skills. On this basis alone, business students should be taught the methodology, as it may immediately have relevance to furthering their careers.
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INTRODUCTION
In business, it is often necessary to make quick estimates when neither time nor resources are available for making traditional assessments. For example, this is particularly true at the idea stage of product development when experiential data specific to the commercial viability of the idea does not yet exist, yet deciding whether to continue investing in the idea cannot be avoided. At this juncture, even a gross estimate is very useful to head off ill-advised expenditures, which are unlikely to generate a baseline profit. A back-of-the-envelope determination of market size, costs, or technical feasibility may be needed. Such a calculation ignores details, focuses only on major factors, and aims at an estimate within an order of magnitude (a power of 10) of the actual answer. Whether or not to proceed to the next level of development can then at least be based upon an educated guess of the product's potential.
A quick estimation process, with which the scientific and engineering community has long been comfortable for order of magnitude estimation, is the Fermi question (Orzel, 2007). Order of magnitude estimation seeks an estimate at least as accurate as the correct answer rounded to the nearest power of ten. In other words, if the correct answer is actually 40 million, an estimate...