The stochastic project network can be a useful tool in analysing project risk. But if it is to be more widely adopted the interface with the real management world needs attention. These papers explore some aspects of data input and output metrics that can help management implement and interpret stochastic project network analysis.
Data for project risk analyses
Bowers J (1994) International Journal of Project Management Vol.12, pp 9-16.
Numerous techniques are available for the quantitative analysis of project risk but without competent data they are worthless. The data may come from a variety of sources, representing experiences of the project team, the organisation and the outside world, and in various forms, both qualitative and quantitative. A mechanism for combining these sources into input suitable for a project risk analysis is described: the uncertainty rating. The method is illustrated with an example incorporating features from a number of project risk analyses undertaken by the author.
Identifying the critical activities in stochastic resource constrained projects
Bowers J (1996) Omega Vol.24 pp.37-46.
The analysis of the stochastic project network can provide indications of both the magnitude of temporal risk and the sources of that risk. In a project dominated by technological dependencies rather than resource constraints, the sources of risk can be identified by examining the probabilities of each activity lying on a critical path. Similar criticality probabilities can also be derived for resource constrained stochastic networks if the definition of the critical path is revised. The use of this revised criticality probability is illustrated in an analysis of an example project and other possible measures of identifying the important activities are considered. A quantitative test of the value of the information provided by the criticality probability is developed and applied to a set of 100 randomly generated project networks, comparing the possible measures. This test indicates that the criticality probability provides valuable management information, extending the familiar concept of the critical path to the resource constrained stochastic network.