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Complexity Risk and Modeling Disorder

Complexity Risk and Modeling Disorder

John K. Hollmann
Copyright: © 2017 |Pages: 13
ISBN13: 9781522517900|ISBN10: 1522517901|EISBN13: 9781522517917
DOI: 10.4018/978-1-5225-1790-0.ch007
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MLA

Hollmann, John K. "Complexity Risk and Modeling Disorder." Handbook of Research on Leveraging Risk and Uncertainties for Effective Project Management, edited by Yuri Raydugin, IGI Global, 2017, pp. 139-151. https://doi.org/10.4018/978-1-5225-1790-0.ch007

APA

Hollmann, J. K. (2017). Complexity Risk and Modeling Disorder. In Y. Raydugin (Ed.), Handbook of Research on Leveraging Risk and Uncertainties for Effective Project Management (pp. 139-151). IGI Global. https://doi.org/10.4018/978-1-5225-1790-0.ch007

Chicago

Hollmann, John K. "Complexity Risk and Modeling Disorder." In Handbook of Research on Leveraging Risk and Uncertainties for Effective Project Management, edited by Yuri Raydugin, 139-151. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1790-0.ch007

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Abstract

Despite 50 years of cost accuracy research, companies are generally unable to quantify the worst outcomes. In the process industries about 10 percent of large projects overrun their budgets by 70 percent or more. The system behavior of these blowouts often reflects disorder. For complex projects, the blowout proportion is 15 to 30 percent of projects. Many risk analysts ignore the worst outcomes as “unknown-unknowns” or “black swans”; but they are neither—we know the causes and their impact is somewhat predictable. Cost disasters start with a mix of systemic weakness and risk events. The cost of mundane projects may overrun by 20 to 40% which is bad but no disaster (financiers assume they will overrun by 25%). Add complexity and stress and the projects can cross a “tipping point” into disorder and chaos with cost overruns of 50, 100 or 200 percent-true disasters. This chapter describes complexity risk and the disorder it can lead to, practical measures of complexity and stress and how to incorporate those measures in non-linear risk quantification models.

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