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Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER)

Received: 8 September 2016     Accepted: 22 September 2016     Published: 15 October 2016
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Abstract

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.

Published in American Journal of Management Science and Engineering (Volume 1, Issue 2)
DOI 10.11648/j.ajmse.20160102.11
Page(s) 36-43
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Project Complexity, Factor Analysis, Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER)

References
[1] Vidal, L.-A., & Marle, F. (2008). Understanding project complexity: implications on project management. 37 (8), 1094-1110.
[2] Baccarini, D. (1996). The concept of project complexity, a review. International Journal of Project Management, 14 (4), 201-204.
[3] Masood, A., & Mini, J. (2007). Complexity in Projects. Practitioners' understanding complexity in relation to existing theoretical models. Master Thesis.
[4] Geraldi, J. (2009). What complexity assessments can tell us about projects: dialogue between conception and perception. Technology Analysis & Strategic Management, 21 (5), 665-678.
[5] Austin, S., Newton, A., Steele, J., & Waskett, P. (2002). Modelling and managing project complexity. International Journal of Project Management, 20, 191-198.
[6] Edmonds, B. (1999). Syntactic measures of complexity. Thesis for the degree of doctor of philosophy in the faculty of arts, University of Manchester.
[7] Marle, F. (2002). Modele d'informations et methodes pour aider a la prise de decision en management de projets. Thesis, Genie Industriel de l'Ecole Centrale, Paris.
[8] Docker, T., & Vincent, G. (2009). CITI CofEe Club. Accessed 11 14, 2015, www.eciti.co.uk/cofee
[9] Snowden, D., & Boone, M. (2007, November). A leader's framework for decision making. Harvard Business Review. Accessed 4 12, 2016, https://hbr.org/2007/11/a-leaders-framework-for-decision-making
[10] Geraldi, J., Maylor, H., & Williams, T. (2011). Now, let's make it really complex (complicated). International Journal of Operations & Production Management, 31 (9), 966-990.
[11] Bosch-Rekveldt, M., Jongkind, Y., Mooi, H., Bakker, H., & Verbraeck, A. (2011). Grasping project complexity in large engineering projects: the TOE (Technical, Organizational and Environmental) framework. International Journal of Project Management, 29 (6), 728-739.
[12] Hass, K. B. (2008, 10 2). Introducing the New Project Complexity Model. Accessed 2 13, 2016, https://www.projecttimes.com/articles/introducing-the-new-project-complexity-model-part-i.html
[13] Page, S. (2008). Uncertainty, difficulty, and complexity. Journal of Theoritical Politics, 20 (2), 115-149
[14] Bosch-Rekveldt, M. (2011). Managing project complexity - A study into adapting early project phases to improve project performance in large engineering projects. The Hague, The Netherlands: Delft Centre for Project Management.
[15] Vidal, L.-A., Marle, G., & Bocquet, J.-C. (2011). Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects. Expert Systems with Applications, 38 (5), 5388-5405.
[16] Xia, B., & Chan, A. P. (2012). Measuring complexity for building projects: A Delphi study. Engineering, Construction and Architectural Management, 19 (1), 7-24.
[17] Olson, D. (1996). Decision Aids for Selection Problems. Springer Series in Operations Research.
[18] Sinha, S., Kumar, B., & Thomson, A. (2006). Measuring project complexity: a project manager's tool. Architecture Engineering and Design Management, 2, 187-202.
[19] Gidado, K. (1996). Project complexity: the focal point of construction production planning. Construction Management and Economics, 14 (3), 213-225.
[20] Remington, K., Zolin, R., & Turner, R. (2009). A model of project complexity: distinguish dimensions of complexity from severity. Proceedings of the 9th International Research Network of Project Management Conference. Berlin.
[21] Roberts, R., & Goodwin, P. (2002). Weights approximations in multi-attribute decision models. Journal of Multi-Criteria Decision Analysis, 11, 291-303.
[22] Barron, F., & Barrett, B. (1996). The efficacy of SMARTER - Simple Multi-Attribute Rating Technique Extended to Ranking. Acta Psychologica, 93, 23-36.
[23] Edwards, W., & Barron, F. (1994). SMARTS and SMARTER: Improved simple methods for multiattribute utility. Organizational Behavior and Human Decision Processes, 60, 306-325.
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  • APA Style

    Odysseas Manoliadis, Emmanouil Vasilakis. (2016). 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, 1(2), 36-43. https://doi.org/10.11648/j.ajmse.20160102.11

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    ACS Style

    Odysseas Manoliadis; Emmanouil Vasilakis. Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER). Am. J. Manag. Sci. Eng. 2016, 1(2), 36-43. doi: 10.11648/j.ajmse.20160102.11

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    AMA Style

    Odysseas Manoliadis, Emmanouil Vasilakis. Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER). Am J Manag Sci Eng. 2016;1(2):36-43. doi: 10.11648/j.ajmse.20160102.11

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  • @article{10.11648/j.ajmse.20160102.11,
      author = {Odysseas Manoliadis and Emmanouil Vasilakis},
      title = {Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER)},
      journal = {American Journal of Management Science and Engineering},
      volume = {1},
      number = {2},
      pages = {36-43},
      doi = {10.11648/j.ajmse.20160102.11},
      url = {https://doi.org/10.11648/j.ajmse.20160102.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20160102.11},
      abstract = {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.},
     year = {2016}
    }
    

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    AU  - Odysseas Manoliadis
    AU  - Emmanouil Vasilakis
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    T2  - American Journal of Management Science and Engineering
    JF  - American Journal of Management Science and Engineering
    JO  - American Journal of Management Science and Engineering
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ajmse.20160102.11
    AB  - 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.
    VL  - 1
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    ER  - 

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Author Information
  • Democritus University of Thrace, Department Civil Engineering, Xanthi, Greece

  • Hellenic Open University, Management in Technical Projects, Patra, Greece

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