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

Odysseas Manoliadis; Emmanouil Vasilakis

doi:doi:10.11648/j.ajmse.20160102.11

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

Odysseas Manoliadis, Emmanouil Vasilakis

Published in American Journal of Management Science and Engineering (Volume 1, Issue 2)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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[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
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[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.
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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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TY  - JOUR
T1  - Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER)
AU  - Odysseas Manoliadis
AU  - Emmanouil Vasilakis
Y1  - 2016/10/15
PY  - 2016
N1  - https://doi.org/10.11648/j.ajmse.20160102.11
DO  - 10.11648/j.ajmse.20160102.11
T2  - American Journal of Management Science and Engineering
JF  - American Journal of Management Science and Engineering
JO  - American Journal of Management Science and Engineering
SP  - 36
EP  - 43
PB  - Science Publishing Group
SN  - 2575-1379
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
IS  - 2
ER  -

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Author Information

Odysseas Manoliadis

Democritus University of Thrace, Department Civil Engineering, Xanthi, Greece
Emmanouil Vasilakis

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

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Plain Text BibTeX RIS

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}
}

Copy | Download

TY  - JOUR
T1  - Complexity Measurement in Engineering Projects Using Factor Analysis and the Single Multi-Attribute Rating Technique Exploiting Ranks (SMARTER)
AU  - Odysseas Manoliadis
AU  - Emmanouil Vasilakis
Y1  - 2016/10/15
PY  - 2016
N1  - https://doi.org/10.11648/j.ajmse.20160102.11
DO  - 10.11648/j.ajmse.20160102.11
T2  - American Journal of Management Science and Engineering
JF  - American Journal of Management Science and Engineering
JO  - American Journal of Management Science and Engineering
SP  - 36
EP  - 43
PB  - Science Publishing Group
SN  - 2575-1379
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
IS  - 2
ER  -

Copy | Download