113R-20: Integrated Cost and Schedule Risk Analysis and Contingency Determination Using Combined Parametric and Expected ValueAACE International, May 27, 2021
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This recommended practice (RP) of AACE International (AACE) defines general practices and considerations for integrated cost and schedule risk analysis and estimating contingency using a combination or hybrid of parametric risk modeling and integrated cost and schedule expected value analysis with Monte Carlo Simulation methods. P+EV is used as a shorthand designation for the combination. In this RP, EV always refers to expected value (not earned value). The base methods are covered separately in RP 42R-08 (Risk Analysis and Contingency Determination Using Parametric Estimating), RP 44R-08 (Risk Analysis and Contingency Determination Using Expected Value) and RP 65R-11 (Integrated Cost and Schedule Risk Analysis and Contingency Determination using Expected Value) respectively. Those RPs should be reviewed for details of the respective methods; this RP is focused on how to use them in combination. Descriptions of other recommended risk quantification practices can be found in AACE Professional Guidance Document PGD-02, Guide to Quantitative Risk Analysis.
The P+EV method is a fit-for-use, practical, risk-driven method intended to support management’s need for integrated distributions of potential project cost and schedule outcomes to support investment decision making and from which contingency and reserves can be established. It is focused on the bottom-line cost and completion date. This is not an ideal method for understanding risk at a more detailed level such as what the risk impact might be on an intermediate schedule milestone, or an estimate WBS element. For such in-depth analysis and understanding, the hybrid combination of the parametric method with the risk-driven critical path schedule method is recommended (i.e., P+CPM) (117R-21).
The integrated, hybrid cost and schedule risk quantification method covered by this RP combines parametric modeling of systemic risks (parametric) and expected value with Monte-Carlo simulation (EV w/MCS) modeling of project-specific risks. P+EV is used as a shorthand designation for the combination. The component methods are addressed in RPs 42R-08, 44R-08 and 65R-11 respectively. Two methods are combined because no single method is optimal for quantifying both systemic and project-specific risks. The parametric model offers empirically-based input to risk quantification that is largely missing with pure MCS. MCS is required in this hybrid combination to model the EV portion and to integrate the analyses results. Only the mean values of method outputs are additive (e.g., the overall cost or duration at say p70 confidence level is not the sum of the separate analyses p70 values).
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The P+EV method is a fit-for-use, practical, risk-driven method intended to support management’s need for integrated distributions of potential project cost and schedule outcomes to support investment decision making and from which contingency and reserves can be established. It is focused on the bottom-line cost and completion date. This is not an ideal method for understanding risk at a more detailed level such as what the risk impact might be on an intermediate schedule milestone, or an estimate WBS element. For such in-depth analysis and understanding, the hybrid combination of the parametric method with the risk-driven critical path schedule method is recommended (i.e., P+CPM) (117R-21).
The integrated, hybrid cost and schedule risk quantification method covered by this RP combines parametric modeling of systemic risks (parametric) and expected value with Monte-Carlo simulation (EV w/MCS) modeling of project-specific risks. P+EV is used as a shorthand designation for the combination. The component methods are addressed in RPs 42R-08, 44R-08 and 65R-11 respectively. Two methods are combined because no single method is optimal for quantifying both systemic and project-specific risks. The parametric model offers empirically-based input to risk quantification that is largely missing with pure MCS. MCS is required in this hybrid combination to model the EV portion and to integrate the analyses results. Only the mean values of method outputs are additive (e.g., the overall cost or duration at say p70 confidence level is not the sum of the separate analyses p70 values).
[Look Inside]