Knowledge Base Repository
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BAYESIAN PROJECT MONITORING
Matthews, Peter C; Philip, Alex D M
Type:
Year:
2011
Editor:
Culley, S.J.; Hicks, B.J.; McAloone, T.C.; Howard, T.J. & Clarkson, P.J.
Author:
Series:
ICED
Section:
Design Processes
Page(s):
69-78
Abstract:
This paper studies how subtle signals that can be observed from the execution order of a project with several tasks can be used to diagnose potential problems that will hinder the project. Specifically, by representing the workflow of the project as a Markov Chain and observing how long the project takes to arrive at its first gateway, it is possible to infer the nature of any potential problems with the project. This diagnosis is achieved through using Bayesian methods, and provides a ranked list of potential problems, along with the probability for each problem. Two examples are used to illustrate how this approach works.
Keywords:
