Scratchs Analysis of an LCC Project Using a Bayesian Network Model

Emphasizing the need to use models capable of dealing with the uncertainty of the project environment, especially those that can alleviate any scratchs that may arise during the course of the project, the following question arises- How to assess the scratchs of a project developed in LCC at from measurements from a Bayesian Network model? In this context, the goal of the study is to measure and analyze the scratchs of a project developed by the LCC method from a model elaborated in Bayesian Networks. The RB structure is composed of two parts, one with a qualitative approach, the other with a quantitative approach. From the information collected with a specialist, we sought to understand the strength that each item present in the key factors Requirements, Restrictions, Time Deliveries and Assumptions exerts on the key factor scratchs, in addition to the strength that each item of the key factor scratchs exerts together with the General Project Performance Indicator (IGDP). Based on the generated simulations, it seen which scratchs affected by a larger number of parameters in the project, as well as the scratchs that present a high or moderate probability of inferior performance with the IGDP. The research developed a probabilistic model that supplied the quantification of possible scratchs in the project, based on the belief of an expert. The developed RB allowed project managers to measure, and so deliberate on practical solutions to problems that affected by project scratchs, based on parameterized analyses.