Maturity of Quantitative Risk Analysis undertaken for Capital Projects



Robert J Chapman, PhD

Director of Dr Chapman and Associates Limited, UK.

United Kingdom




Sponsors of capital projects are commonly required to make investment decisions based on imperfect information, particularly early in the project life cycle. To aid decision making, stochastic models of real world conditions are created to predict project behaviour over time. The risk for project sponsors is that they decide to base funding application decisions on outturn cost and duration predictions derived from poorly developed or evaluated stochastic models. These models typically take the form of Quantitative Risk Analysis (QRA) incorporating Monte Carlo simulation. The objective of a QRA is to calculate the combined impact of the model’s various uncertainties in order to create a probability distribution of the possible model outcomes. The reliance that can be placed on results generated by QRA models largely depends on the overall maturity of the risk management practices adopted. In particular the quality of the inputs, the application of enablers, an understanding and management of the constraints, and the construction of the model. In terms of inputs, project sponsors together with project and risk managers have to be wary of garbage in, garbage out. Garbage in, garbage out (commonly abbreviated to the acronym GIGO) is a phrase used in the fields of computer science as well as information and communication technology. It is commonly used to describe failures in human decision making due to faulty, incomplete or imprecise data. In simple language it is used to call attention to the fact that computers that are fed poor quality input data (“garbage in”) will as a consequence produce poor quality output (“garbage out”). The significance of the quality of the uncertainty models is brought into perspective when considering the value of current rail projects such as High Speed Two, where the contingency for Phase 1 (London to Birmingham) exceeds £5billion

Maturity of QRA practices

The overall maturity of the in-house approach to QRA implementation can be assessed with the aid of a risk maturity model (commonly abbreviated to RMM) which specifically focusses on the preparation of QRAs rather than the embedding of risk management as a whole. A RMM can be viewed as a set of incremental levels of capability that describe how well the behaviors, practices and processes of an organization can reliably and repeatedly produce required outcomes. Specifically a RMM may identify gaps in current capabilities that detract from the delivery of required risk products (outputs). Primarily the structure of current project risk management maturity models have been influenced by the Capability Maturity Model (CMM) for software produced by the Software Engineering Institute (SEI), a research center located at the Carnegie Mellon University in Pittsburgh, Pennsylvania, United States. The center was established and funded by the U.S. Department of Defense. The reference to maturity models within the APM and PMI Bodies of Knowledge demonstrates that maturity models have now become an established part of documented practice.

Maturity Levels

Maturity models are typically based on a number of maturity levels (see Figure 1) whereby capability criteria (sometimes referred to as attributes, perspectives and domains) are assessed against each level, (see Figure 2). The development of an RMM forces risk teams to consider their current, required and target capabilities. The maturity levels included in Figure 1 are based on the Carnegie Mellon University process improvement Capability Maturity Model Integration (CMMI) program.


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About the Author

pmwj41-Dec2015-Chapman-PHOTORobert J. Chapman, PhD

United Kingdom



Robert J Chapman
is an international risk management specialist and Director of Dr Chapman and Associates Limited (www.drchapman-assoc.com). . He is author of ‘Simple tools and techniques for enterprise risk management’ 2nd edition, published by John Wiley and Sons Limited and ‘The Rules of Project Risk Management, implementation guidelines for major projects’ published by Gower Publishing. He holds a PhD in risk management from Reading University and is a fellow of the IRM, APM and ICM and a member of the RIBA. He has provided risk management services in the UK, the Republic of Ireland, Holland, UAE, South Africa, Malaysia and Qatar on multi-billion programmes and projects. Robert has passed the M_o_R, APM and PMI risk examinations and provided M_o_R risk management training to representatives of multiple industries. He can be reached by email at [email protected]