Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER)
American Journal of Management Science and Engineering
Volume 1, Issue 2, November 2016, Pages: 36-43
Received: Sep. 8, 2016;
Accepted: Sep. 22, 2016;
Published: Oct. 15, 2016
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Odysseas Manoliadis, Democritus University of Thrace, Department Civil Engineering, Xanthi, Greece
Emmanouil Vasilakis, Hellenic Open University, Management in Technical Projects, Patra, Greece
A lot of problems that emanate from complexity could have been mitigated or even avoided, if the factors that render a project complex and the risks that they induce, were fully comprehensible in order for a more appropriate management process to be established. The aim of this study is the comprehension of the meaning of complexity in engineering projects through the identification of the factors that affects it based on which a project complexity measurement model is proposed. To that end an extensive literature review has been conducted in order to detect as many factors already identified by previous researchers as possible and to categorize them in a way that can integrate the existing theoretical and empirical approaches. Through that study, 21 factors that contribute to the complexity of engineering projects were distinguished. Afterwards, following the results of a questionnaire survey that was carried out and upon implementing factor analysis on its data, 7 key factors were discerned as the main components of the complexity variables. Finally, using a simplified method of multiple-criteria decision analysis, namely Single Multi Attribute Rating Technique Exploiting Ranking – SMARTER, a practical and approachable model of complexity measurement has been introduced, named Complexity Level Indicator – CLI.
Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER), American Journal of Management Science and Engineering.
Vol. 1, No. 2,
2016, pp. 36-43.
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