A reliable ‘Estimate at Completion’ from the early stage of project execution is essential in order to enable efficient and proactive project management. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders require the use and integration of multiple informative sources in order to provide accurate forecasts. Moreover, in the Oil&Gas industry projects are characterized by a high level of complexity and financial impact. The paper aims at multiple objectives: introducing the need for the identification and utilization of all the available knowledge in order to improve the forecasting process; developing a Bayesian approach in order to integrate the diverse knowledge sources; exploring the integration of data records and experts’ judgment related to the ongoing project; exploring the integration of data records related to projects completed in the past and to the ongoing project and finally developing a Bayesian model capable of using three different knowledge sources: data records and experts’ judgments related to the ongoing project and data records related to similar projects completed in the past. The model has been tested in a set of large and complex projects in the Oil&Gas industry, in order to forecast the final duration and the final cost. The results show a higher forecasting accuracy of the Bayesian model compared to the traditional Earned Value Management (EVM) methodology.

Project Management in the Oil & Gas Industry - A Bayesian Approach

CARON, FRANCO;
2016

Abstract

A reliable ‘Estimate at Completion’ from the early stage of project execution is essential in order to enable efficient and proactive project management. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders require the use and integration of multiple informative sources in order to provide accurate forecasts. Moreover, in the Oil&Gas industry projects are characterized by a high level of complexity and financial impact. The paper aims at multiple objectives: introducing the need for the identification and utilization of all the available knowledge in order to improve the forecasting process; developing a Bayesian approach in order to integrate the diverse knowledge sources; exploring the integration of data records and experts’ judgment related to the ongoing project; exploring the integration of data records related to projects completed in the past and to the ongoing project and finally developing a Bayesian model capable of using three different knowledge sources: data records and experts’ judgments related to the ongoing project and data records related to similar projects completed in the past. The model has been tested in a set of large and complex projects in the Oil&Gas industry, in order to forecast the final duration and the final cost. The results show a higher forecasting accuracy of the Bayesian model compared to the traditional Earned Value Management (EVM) methodology.
Wiley StatsRef: Statistics Reference Online
9781118445112
project control, estimate to complete, Bayesian approach, Earned Value Management
File in questo prodotto:
File Dimensione Formato  
pm in oil&gas.pdf

embargo fino al 01/12/2023

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 953.79 kB
Formato Adobe PDF
953.79 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1009704
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact