The
classical method of project risk analysis, for example, the Program
Evaluation and Review Technique (PERT), ignores statistical dependence
among project activities, which puts limits to its effectiveness as
a robust method for probabilistic schedule analysis. Also, some programmatic
risk factors have been identified as significant sources of uncertainty
in project performance. However, given a project that is progressing
within the project life cycle, monitoring of uncertainty in the project
completion times resulting from a programmatic risk factor while taking
advantage of activity duration statistical dependence, has not been
addressed, to the best of our knowledge. In this study, we develop two
methods that are based on Bayesian Networks (BN) for evaluating and
monitoring uncertainty in project completion times, when an ongoing
or progressing project is impacted by a programmatic risk factor.
The
BN methods developed in this study model statistical dependence in project
networks using parametric relationships between nodes which reduce the
burden of dependence specification in the BN model. In modeling the
relationships of the variables of the BN models developed in this study,
concerns about computational complexities and efficient modeling of
interactions of variables of a project are enabled by the capacity of
the specialized Bayesian Networks software (AgenaRisk®) used in
the analysis. Other concerns of classical PERT such as: (i) assumption
of statistical independence is addressed by using conditional median
as a measure of statistical dependence, and (ii) the constant PERT variance
assumption is addressed using the Modified PERT Variance.
Using
the BN models developed in this study, we demonstrate that failure to
incorporate statistical dependence grossly underestimates the total
uncertainty in project completion times. The graphical dimension of
our model, which benefits from the capacities of Bayesian Networks,
gives more visibility about the model development and uncertainty analysis
process, and which could be helpful to project analysts and managers
by providing greater insight and formal mechanisms for interpreting
how uncertainties in project performance measures emerge. More so, the
faster learning about remaining completion time uncertainty combined
with the precision of the BN approach may provide project managers more
time to take corrective action to avoid schedule slippage.
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