EconPapers    
Economics at your fingertips  
 

ANALYSIS OF FORECASTING METHODS FROM THE POINT OF VIEW OF EARLY WARNING CONCEPT IN PROJECT MANAGEMENT

Florin Popescu
Additional contact information
Florin Popescu: Doctoral School - Entrepreneurship, Business Engineering & Management; University “Politehnica” of Bucharest, Romania

SEA - Practical Application of Science, 2017, issue 15, 331-346

Abstract: Early warning system (EWS) based on a reliable forecasting process has become a critical component of the management of large complex industrial projects in the globalized transnational environment. The purpose of this research is to critically analyze the forecasting methods from the point of view of early warning, choosing those useful for the construction of EWS. This research addresses complementary techniques, using Bayesian Networks, which addresses both uncertainties and causality in project planning and execution, with the goal of generating early warning signals for project managers. Even though Bayesian networks have been widely used in a range of decision-support applications, their application as early warning systems for project management is still new.

Keywords: Early warning; Forecasting methods; Project management (search for similar items in EconPapers)
JEL-codes: L00 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://seaopenresearch.eu/Journals/articles/SPAS_15_2.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cmj:seapas:y:2017:i:15:p:331-346

Access Statistics for this article

SEA - Practical Application of Science is currently edited by Romanian Foundation for Business Intelligence

More articles in SEA - Practical Application of Science from Romanian Foundation for Business Intelligence, Editorial Department
Bibliographic data for series maintained by Serghie Dan ().

 
Page updated 2025-03-19
Handle: RePEc:cmj:seapas:y:2017:i:15:p:331-346