Abstract
For improving the conditions of project intended purpose and reaching high score in the project success, project Stakeholders (including employer, contractor, consultant and its users) try to comply with the implementation of project Critical Success Factors(CSFs) at the beginning of each project. This implementation is in two terms: economic and executive. Artificial neural networks are one of the new methods which have been developed to estimate and predict parameters by using inherent relationship among data. In this research, it tried to propose a model to determine the project success score by using radial basis neural networks. For reaching this purpose, firstly, the key indicators of project success (employer, contractor and consultant) among the main elements involved in the industry of macro-civil construction projects in Iran reviewed. Secondly, ten CSFs key project success indicators were recognized in five categories: (i) financial, (ii) interaction processes, (iii) manpower, (iv) contract settings and (v) characteristic nature of the project (based on conditions of the present research in Iran). Then, some projects were selected by random sampling of projects operated during the last 5 years in the country's Ministry of Energy. Among those projects, project information was collected by managers of large projects. After training the designed neural network, the project success model was provided based on an assessment of project objectives including factors of Scope, Time, Cost, and Quality of the projects. For facilitating other researches’ use, the applied equation of the model was presented as well. Outputs, calculated by the proposed model, were in good agreement with the actual number of projects assessed in Iran. The results of this study may be used as a tool in implementing projects for the rapid assessment of achieving project goals’ facilities.
Highlights
The success of a project is of the largest and most important objectives and concerns of managers and all those involved in a project which is somehow unifying the efforts of all team members of the project
Defining a model for predicting project success index based on identified 10 Critical Success Factors (CSFs) Iran's projects as inputs and defining project objectives based on Project Management Body of Knowledge (PMBOK) standard as output
After entering the information in the designed neural network, the correlation coefficient obtained in the assessment of the proposed success model presented (Total Regression Test) was calculated at a rate of 0.8689, and the error rate calculated by the Mean Square Error (MSE), (Total Performance Test) as 0.01088
Summary
The success of a project is of the largest and most important objectives and concerns of managers and all those involved in a project which is somehow unifying the efforts of all team members of the project. Success factors of the project are expressed as "a set of environmental factors, facts or influential factors that can affect the output of the projects. These are factors that can accelerate a project or make it difficult. Project Management Body of Knowledge (PMBOK) standard is a unique effort to deliver a series of products (output) in the defined Scope, Time, Cost, and Quality [3]. The objectives of this assessment (1) are essential to identify the critical factors which overall determine project success (2) define and identify key CSFs of construction projects from the perspectives of different participants of the project with different goals. Management can take necessary steps to (1) avoid project failure (2) identify promising projects and keep track of them, and (3) identify the problematic areas of the project for undergoing necessary corrective actions
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