Bayesian-based Project Monitoring: Framework Development and Model Testing

During project implementation, risk becomes an integral part of project monitoring. Therefore. a tool that could dynamically include elements of risk in project progress monitoring is needed. This objective of this study is to develop a general framework that addresses such a concern. The developed...

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Main Authors: Budi Hartono, Riesa Ayuningtyas, Yun Prihantina Mulyani
Format: Article
Language:English
Published: Petra Christian University 2015-12-01
Series:Jurnal Teknik Industri
Subjects:
Online Access:http://puslit2.petra.ac.id/ejournal/index.php/ind/article/view/19319
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spelling doaj-28b7d8dde69f4b56bd4073f0369dd4a12020-11-24T23:28:38ZengPetra Christian UniversityJurnal Teknik Industri1411-24852087-74392015-12-011726170Bayesian-based Project Monitoring: Framework Development and Model TestingBudi Hartono0Riesa Ayuningtyas1Yun Prihantina Mulyani2 Industrial Engineering Program, <br />Mechanical and Industrial Engineering Department, Universitas Gadjah Mada <br />Jl. Grafika 2, Yogyakarta Industrial Engineering Program, <br />Mechanical and Industrial Engineering Department, Universitas Gadjah Mada <br />Jl. Grafika 2, Yogyakarta Industrial Engineering Program, <br />Mechanical and Industrial Engineering Department, Universitas Gadjah Mada <br />Jl. Grafika 2, Yogyakarta During project implementation, risk becomes an integral part of project monitoring. Therefore. a tool that could dynamically include elements of risk in project progress monitoring is needed. This objective of this study is to develop a general framework that addresses such a concern. The developed framework consists of three interrelated major building blocks, namely: Risk Register (RR), Bayesian Network (BN), and Project Time Networks (PTN) for dynamic project monitoring. RR is used to list and to categorize identified project risks. PTN is utilized for modeling the relationship between project activities. BN is used to reflect the interdependence among risk factors and to bridge RR and PTN. A residential development project is chosen as a working example and the result shows that the proposed framework has been successfully applied. The specific model of the development project is also successfully developed and is used to monitor the project progress. It is shown in this study that the proposed BN-based model provides superior performance in terms of forecast accuracy compared to the extant models.http://puslit2.petra.ac.id/ejournal/index.php/ind/article/view/19319Bayesian NetworksRisk RegisterRisk FactorsProject Monitoring
collection DOAJ
language English
format Article
sources DOAJ
author Budi Hartono
Riesa Ayuningtyas
Yun Prihantina Mulyani
spellingShingle Budi Hartono
Riesa Ayuningtyas
Yun Prihantina Mulyani
Bayesian-based Project Monitoring: Framework Development and Model Testing
Jurnal Teknik Industri
Bayesian Networks
Risk Register
Risk Factors
Project Monitoring
author_facet Budi Hartono
Riesa Ayuningtyas
Yun Prihantina Mulyani
author_sort Budi Hartono
title Bayesian-based Project Monitoring: Framework Development and Model Testing
title_short Bayesian-based Project Monitoring: Framework Development and Model Testing
title_full Bayesian-based Project Monitoring: Framework Development and Model Testing
title_fullStr Bayesian-based Project Monitoring: Framework Development and Model Testing
title_full_unstemmed Bayesian-based Project Monitoring: Framework Development and Model Testing
title_sort bayesian-based project monitoring: framework development and model testing
publisher Petra Christian University
series Jurnal Teknik Industri
issn 1411-2485
2087-7439
publishDate 2015-12-01
description During project implementation, risk becomes an integral part of project monitoring. Therefore. a tool that could dynamically include elements of risk in project progress monitoring is needed. This objective of this study is to develop a general framework that addresses such a concern. The developed framework consists of three interrelated major building blocks, namely: Risk Register (RR), Bayesian Network (BN), and Project Time Networks (PTN) for dynamic project monitoring. RR is used to list and to categorize identified project risks. PTN is utilized for modeling the relationship between project activities. BN is used to reflect the interdependence among risk factors and to bridge RR and PTN. A residential development project is chosen as a working example and the result shows that the proposed framework has been successfully applied. The specific model of the development project is also successfully developed and is used to monitor the project progress. It is shown in this study that the proposed BN-based model provides superior performance in terms of forecast accuracy compared to the extant models.
topic Bayesian Networks
Risk Register
Risk Factors
Project Monitoring
url http://puslit2.petra.ac.id/ejournal/index.php/ind/article/view/19319
work_keys_str_mv AT budihartono bayesianbasedprojectmonitoringframeworkdevelopmentandmodeltesting
AT riesaayuningtyas bayesianbasedprojectmonitoringframeworkdevelopmentandmodeltesting
AT yunprihantinamulyani bayesianbasedprojectmonitoringframeworkdevelopmentandmodeltesting
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