Project Management (PM) takes into account a large number of parameters of different nature. Their diversity and mutual dependencies are one of the main hindrances to successful management of projects. Certain parameters such as time, cost, risk are measurable, nevertheless they should be estimated before the project realization. These estimations are to an extent always vulnerable to uncertainty, owing to a multitude of unstable factors. Therefore, despite the measurability of such quantities, the problem of uncertainty remains, affecting negatively the project management process. In case of parameters that are immeasurable, the situation is much more complicated. As examples, let us take elements (phenomena, states) of mental, psychological character like Project Manager’s and project team qualities, stakeholders satisfaction, or customer ability to formulate his requirements. The fuzzy approach is commonly recognized as an apparatus to pattern uncertainty in a large family of research and practical applications. Since several years one can observe this trend in PM research. There is a number of papers on project time, cost, and risk management, employing fuzzy numbers as a tool of uncertainty modelling of these project parameters management. On the contrary, one can seldom encounter conceptual or applied research for immeasurable PM parameters. The objective of this paper is to offer a contribution to partially fill this gap. It will be achieved in the form of Project Customer Requirements Management Model, using fuzzy numbers.
| Published in | Journal of Public Policy and Administration (Volume 4, Issue 1) |
| DOI | 10.11648/j.jppa.20200401.12 |
| Page(s) | 9-15 |
| 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), 2020. Published by Science Publishing Group |
Project Management, Customer Requirements Process, Customer Requirements Management, Requirements Recognition, Requirements Definition, Fuzzy Numbers
| [1] | Frame J. D., Managing Projects in Organizations. How to make the Best Use of Time, Techniques, and People, Jossey-Bass Inc., Publishers, 1995. |
| [2] | Kerzner H., Advanced Project Management, Helion, Gliwice 2005 (in polish). |
| [3] | Patzak G. and Rattay G., Project Management. Guideline for the management of projects, projects portfolios, programs and project-oriented companies, Linde Verlag, Wien 2012. |
| [4] | Darnall R. W., The World’s Greatest Project. One Project Team on the Path to Quality. Project Management Institute, 1996. |
| [5] | Alireza B. and Meysam S. M., Fuzzy resource-constrained project scheduling with multiple routes: A heuristic solution, Automation in Construction 2019, Vol. 100, pp. 84-102. |
| [6] | Dorfeshan Y. et al., Selecting project-critical path by a new interval type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods, Computers & Industrial Engineering 2018, Vol. 120, pp. 160-178. |
| [7] | Zhang Bei-Bei et al., Explicit cost-risk trade-off for optimal energy management in CCHP microgrid system under fuzzy-risk preferences, Energy Economics 2018, Vol. 70, pp. 525-535. |
| [8] | Karasan A. et al., A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets, Safety Science 2018, Vol. 108, pp. 173-187. |
| [9] | Mazlum M. and Fuat Güneri A., CPM, PERT and Project Management with Fuzzy Logic Technique and Implementation on a Business, Procedia - Social and Behavioral Sciences 2015, Vol. 210, pp. 348-357. |
| [10] | Maravas A. and Pantouvakis J-P., Guidelines for Modelling Time and Cost Uncertainty in Project and Programme Management, Procedia - Social and Behavioral Sciences 2013, Vol. 74, pp. 203-211. |
| [11] | Carlsson Ch. et al., A fuzzy approach to R&D project portfolio selection, International Journal of Approximate Reasoning 2007, Vol. 44, Issue 2, pp. 93-105. |
| [12] | Yuanyuan Liu, Jian Zhou, Yizeng Chen, Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD, Neurocomputing 2014, Vol. 142, pp. 115-124. |
| [13] | Ståhle M. and Ahola T., Martinsuo M., Cross-functional integration for managing customer information flows in a project-based firm, International Journal of Project Management 2019, Vol. 37, Issue 1, pp. 145-160. |
| [14] | OlssonT. and Wnuk K., Gorschek T., An empirical study on decision making for quality requirements, Journal of Systems and Software 2019, Vol. 149, pp. 217-233. |
| [15] | Zadeh L. A., Fuzzy Sets, Information and Control 1965, no 8. |
| [16] | Kuchta D., Soft Mathematics in Management, Wroclaw University of Science and Technology ed., Wroclaw 2001 (in polish). |
APA Style
Jan Betta. (2020). Project Customer Requirements Management Using Fuzzy Numbers. Journal of Public Policy and Administration, 4(1), 9-15. https://doi.org/10.11648/j.jppa.20200401.12
ACS Style
Jan Betta. Project Customer Requirements Management Using Fuzzy Numbers. J. Public Policy Adm. 2020, 4(1), 9-15. doi: 10.11648/j.jppa.20200401.12
AMA Style
Jan Betta. Project Customer Requirements Management Using Fuzzy Numbers. J Public Policy Adm. 2020;4(1):9-15. doi: 10.11648/j.jppa.20200401.12
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Download@article{10.11648/j.jppa.20200401.12,
author = {Jan Betta},
title = {Project Customer Requirements Management Using Fuzzy Numbers},
journal = {Journal of Public Policy and Administration},
volume = {4},
number = {1},
pages = {9-15},
doi = {10.11648/j.jppa.20200401.12},
url = {https://doi.org/10.11648/j.jppa.20200401.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jppa.20200401.12},
abstract = {Project Management (PM) takes into account a large number of parameters of different nature. Their diversity and mutual dependencies are one of the main hindrances to successful management of projects. Certain parameters such as time, cost, risk are measurable, nevertheless they should be estimated before the project realization. These estimations are to an extent always vulnerable to uncertainty, owing to a multitude of unstable factors. Therefore, despite the measurability of such quantities, the problem of uncertainty remains, affecting negatively the project management process. In case of parameters that are immeasurable, the situation is much more complicated. As examples, let us take elements (phenomena, states) of mental, psychological character like Project Manager’s and project team qualities, stakeholders satisfaction, or customer ability to formulate his requirements. The fuzzy approach is commonly recognized as an apparatus to pattern uncertainty in a large family of research and practical applications. Since several years one can observe this trend in PM research. There is a number of papers on project time, cost, and risk management, employing fuzzy numbers as a tool of uncertainty modelling of these project parameters management. On the contrary, one can seldom encounter conceptual or applied research for immeasurable PM parameters. The objective of this paper is to offer a contribution to partially fill this gap. It will be achieved in the form of Project Customer Requirements Management Model, using fuzzy numbers.},
year = {2020}
}
TY - JOUR T1 - Project Customer Requirements Management Using Fuzzy Numbers AU - Jan Betta Y1 - 2020/03/10 PY - 2020 N1 - https://doi.org/10.11648/j.jppa.20200401.12 DO - 10.11648/j.jppa.20200401.12 T2 - Journal of Public Policy and Administration JF - Journal of Public Policy and Administration JO - Journal of Public Policy and Administration SP - 9 EP - 15 PB - Science Publishing Group SN - 2640-2696 UR - https://doi.org/10.11648/j.jppa.20200401.12 AB - Project Management (PM) takes into account a large number of parameters of different nature. Their diversity and mutual dependencies are one of the main hindrances to successful management of projects. Certain parameters such as time, cost, risk are measurable, nevertheless they should be estimated before the project realization. These estimations are to an extent always vulnerable to uncertainty, owing to a multitude of unstable factors. Therefore, despite the measurability of such quantities, the problem of uncertainty remains, affecting negatively the project management process. In case of parameters that are immeasurable, the situation is much more complicated. As examples, let us take elements (phenomena, states) of mental, psychological character like Project Manager’s and project team qualities, stakeholders satisfaction, or customer ability to formulate his requirements. The fuzzy approach is commonly recognized as an apparatus to pattern uncertainty in a large family of research and practical applications. Since several years one can observe this trend in PM research. There is a number of papers on project time, cost, and risk management, employing fuzzy numbers as a tool of uncertainty modelling of these project parameters management. On the contrary, one can seldom encounter conceptual or applied research for immeasurable PM parameters. The objective of this paper is to offer a contribution to partially fill this gap. It will be achieved in the form of Project Customer Requirements Management Model, using fuzzy numbers. VL - 4 IS - 1 ER -
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