Highly recommended.
Most capital investments are poorly analyzed. Doug Hubbard provides remedies for the common shortcomings:
o Typical cost-benefit analyses use single-value best estimates for inputs; these ignore or inadequately address risk and uncertainty. Also, often, important benefits are omitted because they are "intangibles" such as "improved customer service." Remedies in the book: Capture expert judgments as probability distributions; then solve the forecast model with Monte Carlo simulation. Decompose and explicitly represent former-intangibles in measurable units.
o Multi-criteria scoring approaches often feel good yet have little theoretical foundation. They are entirely subjective and have not been shown to improve decision-making. Remedy: clarify the business (or other) objective and craft a quantified decision policy accordingly. Judge and/or model inputs in meaningful, quantitative terms.
Everything important to a decision should be in a forecasting model. And everything in those models is either structural or quantified.
For me, three primary themes emerge in the book:
1. Calibration. Hubbard asserts that everything can be quantified--and he enjoys challenging individuals and groups to find any exception. Most people, even if expert in their field, are biased when making judgments. Hubbard shows ways to "calibrate" these experts (including the reader) with perhaps a half-day of practice. Most readers (as did I) will find that they initially fare poorly on the engaging calibration exercises in the book. We suffer from overconfidence and other cognitive biases. With feedback and practice, most people can quickly improve at assessing probabilities and probability distributions, and confidence ranges.
2. What needs to be measured further? The most interesting calculation in the book is a form of sensitivity analysis. Which variables are most important to measure further? Hubbard calculates the value of perfect information for each variable with a straightforward expected opportunity loss calculation. Though an analysis may have dozens of identified variables with uncertainty, in his experience typically only 1 to 3 variables are worthy of further measurement.
3. Deliberately seek information about the most-important risks and uncertainties where the additional time and cost are justified. This usually means obtaining more data with targeted investigation. Hubbard offers these encouraging maxims:
o It has been done before
o You have more data than you think
o You need less data than you think
o It is more economical than you think
Additional highlights of the book include:
o Abundant war story examples. Much of Hubbard's work has been in the information technology (IT) sector. I especially enjoyed the case about forecasting fuel consumption for the U.S. Marine Corps.
o Demystifying parameters that the reader might initially think as "intangibles." For example, "Improved Customer Service" might be measured in terms of percent of customers re-ordering; percent of returns; average delivery time, etc.
o Example calculations, all done with Microsoft® Excel. He hosts a website, [...], where the reader can download example calculation worksheets, additional calibration quizzes, papers, articles, and reader comments.
The first edition is excellent. Upon learning of a second edition, I immediately bought the updated book for a fresh re-read. This was again a good investment of time and money. The new edition is updated, expanded (about 15%), and more crisp. The editing and layout are again excellent. Several references are now embedded to his other best-selling and companion book, The Failure of Risk Management.
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Top reviews from other countries
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Jan Fischbach (www.teamworkblog.de)Reviewed in Germany on December 14, 2012
Ein Plädoyer für Selbst-Denken und Ausprobieren und Messen (statt Meinungen)
- 6**45Reviewed in the United Kingdom on August 26, 2012
Brilliant book and a great help
- Prairy EarthReviewed in Canada on September 30, 2016
Good book on the topic of Measuring Activities
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Egon RüffelReviewed in Germany on April 1, 2012
Langathmig, aber nicht uninteressant
