A Softcomputing Knowledge Areas Model
DOI:
https://doi.org/10.6000/2371-1647.2015.01.03Keywords:
Knowledge Areas, Project Management, PMBOK® Guide, Softcomputing, ModelingAbstract
Recently, ten knowledge areas (KAs) of project management have been published by the PMBOK® Guide. They comprise specific skills and experiences to ensure accomplishing project goals, and include management of: integration, scope, cost, time, quality, communications, procurement, risk, human resources and stakeholders. This research paper focuses on the ten required KAs for a project manager or a project to be successful. It aims at applying the Softcomputing modeling techniques to describe the relations between the 47 processes and the KAs. Such a model will enable users to predict the overall competencies of the project management. Thus, it provides an assessment tool to envisage, visualize and indicate the overall performance and competency of a project.
The proposed Softcomputing Knowledge Areas Model (SKAM) is a two-stage model. The first stage involves ten models. Each model describes relations between a specific KA and its related processes. The outputs of these ten models will feed into the second stage that will represent the relationship between all the ten KAs and the overall predicted competencies of a project. A combination of Subtractive Clustering and Neurofuzzy modeling techniques are used. Three measures are used to validate the adequacy of the models: the mean average percentage errors, the correlation coefficient and the maximum percentage errors. The highest achieved values for these measures are0.5751, 0.9999 and 4.7283, respectively.
However, although the preliminary findings of the proposed SKAM model are promising, more testing is still required before declaring the adequacy of applying the Softcomputing modeling approach in the project management field.
References
A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Fifth Edition, http://www.pmi.org/ PMBOK-Guide-and-Standards.aspxAccessed on 15.04.2015
https://www.apm.org.uk/APM5Dimensions Accessed on 15.04.2015
http://www.pmsolutions.com/resources/view/what-is-the-project-management-maturity-model/ Accessed on 18.04.2015
https://www.prince2.com/what-is-prince2 Accessed on 18.04.2015
http://www.pmi.org/PMBOK-Guide-and-Standards.aspx Accessed on 20.04.2015
Singh R, Lano K. Literature Survey of previous research work in Models and Methodologies in Project Management. (IJACSA) International Journal of Advanced Computer Science and Applications 2014; 5(9). http://dx.doi.org/10.14569/IJACSA.2014.050917
Ramadan WH. Project Management Literature: Gaps and Opportunities. E-Leader Berlin 2012.
Siwani M, et al. Application of fuzzy logic for improved software project management estimations Electrical and Computer Engineering, 2004. Canadian Conference on 2004; Volume 4: pp. 1897-1900. http://dx.doi.org/10.1109/CCECE.2004.1347582
Goonawardene N, et al. A Neural Network Based Model for Project Risk and Talent Management. Advances in Neural Networks - ISNN 2010, Lecture Notes in Computer Science 2010; 6064: 532-539.
Hu Y, Huang J, Chen J, Liu M, Xie K. Software project risk management modeling with neural network and support vector machine approaches. In: ICNC 2007: Proceedings of the Third International Conference on Natural Computation, pp. 358–362. IEEE Computer Society, Washington 2007. http://dx.doi.org/10.1109/icnc.2007.672
Liu XF, Kane G, Bambroo M. An intelligent early warning system for software quality improvement and project management. Journal of Systems and Software 2006; 79(11): 1552-1564.
Tüysüz F, Kahraman C. Project Risk Evaluation Using a Fuzzy Analytic Hierarchy Process: An Application Information Technology Projects. International Journal of Intelligent Systems 2006; 21: 559-584. http://dx.doi.org/10.1002/int.20148
Recent Advances in Technologies, Edited by Maurizio A Strangio, ISBN 978-953-307-017-9, Published online 01, November, 2009, InTech, Hossein Iranmanesh, et al, “Framework of Intelligent Systems to Support Project Scope”.
Avots, Ivars. Application of expert systems concepts to schedule control. Project Management Institute 2001.
Nemati H, Todd DW, Brown, Paul D. A hybrid intelligent system to facilitate information system project management activities. Project Management Institute 2002.
Nassa VK, Yadav SK. Project Management Efficiency –A Fuzzy Logic Approach. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249–8958, Volume-1, Issue-3, February 2012.
Nassa VK, Yadav SK. Project Management Efficiency using Soft Computing and Risk Analysis. International Journal of Computer Applications 2012; 50(16): 17-22.
Hwang B-G, Ng WJ. Project management knowledge and skills for green construction: Overcoming challenges. International Journal of Project Management 2013; 31(2): 272-284.
Eastham J, et al. PLM software selection model for project management using hierarchical decision modeling with criteria from PMBOK knowledge areas. Technology Management in the IT-Driven Services (PICMET), 2013 Proceedings of PICMET '13: July 28 2013-Aug. 1 2013, 511 – 527.
Bai-Min S, et al. Notice of Retraction Primarily Research on Knowledge Audit for Evaluating Enterprise Knowledge Management Capability, Published in: Management and Service Science, 2009. MASS '09, International Conference on: 20-22 Sept. 2009; 1-5. http://dx.doi.org/10.1109/ICMSS.2009.5302226
Spalek S. Assessing project management maturity in the area of knowledge management in select companies International Journal of Economics, Finance and Management Sciences 2014; 2(2): 164-170. http://dx.doi.org/10.11648/j.ijefm.20140202.18
http://www.corpedgroup.com/consulting/online-skills-assessment.asp
Zadeh LA. Fuzzy Logic, Neural Networks and Soft computing. Communications of ACM 1994; 37(3): 77-84.
Zadeh L. ‘Fuzzy sets’. Information and Control 1965; 8: 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
http://www.cs.berkeley.edu/~zadeh/ Accessed on 22.04.2015
Jang J-SR. ANFIS: adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 1993; 23(3): 665-685.
Jang J-SR, Sun C-T. Neuro fuzzy modeling and control. IEEE Proc 1995; 83(3): 378-405.
Downloads
Published
How to Cite
Issue
Section
License
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
