Generally, may be made in project risk

Generally, risk is considered to be a
concept which is difficult to measure.
Uncertainty in cost and risk has always been a complex part of every project. The
purpose of cost analysis is to help decision makers to recognize and represent
different variables affecting investment risk and eventually predict the cost
of the project. The general practice in cost analysis is to divide the project
into smaller cost variables and probabilistically estimate the uncertainty of
each item which ultimately decides the calculation of project cost. However, dependencies
among these items should also be considered otherwise the accuracy of the cost
estimation is jeopardized.

Successful achievement of project success requires
careful management of uncertainty and risk. Yet such uncertainty is rarely
effectively calculated when analyzing project costs and benefits. This paper
presents a Bayesian network (BN) modeling framework to calculate the costs,
risk and benefits of a project with flexibility regarding changes in
circumstances and trade-offs over time. This paper considers the trade-offs that
may be made in project risk management,specifically time, cost and quality. The
main goal of this paper is to provide a model which addresses thereal problems
and questions that various project managers encounter.

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The approach is based on Bayesian Networks (BNs). BNs
provide a framework for causal modelling and offer a potential solution to some
of the classical modeling problems. Researchershave recently attempted to build
BN models that incorporate relationships between time, cost,quality,
functionality and various process variables.

Our paper is a result of the study made from the paper
(Odimabo, Oduoza, & Suresh, 2017). However, the
abstract and ambiguous information has also been extended using various other
sources and softwares.

In this paper, a general framework that
estimates the costs and the effects of risk factors in a project by taking both
the uncertainty of parameters and the unpredictability of risks into account is
shown.