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A Fuzzy-Network Analysis Approach for Modeling and Analyzing Team Dependencies in Product Development Projects

Received: 6 February 2021     Accepted: 11 March 2021     Published: 17 March 2021
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Abstract

Developing a complex product in a concurrent engineering environment requires managing information flow among ten or even hundreds of people of different specialties organized in a large number of product development (PD) teams. Managing these teams effectively requires understanding the level of information dependencies among them which are often vague and cannot be precisely predicted. Taking into account the limitations of relevant previous studies, this article proposes a fuzzy-social network analysis approach for modeling and analyzing the information flow among PD teams. The approach involves four major steps: mapping of dependencies, measuring the level of information dependencies based on the fuzzy set theory, visualizing the network, and performing quantitative analysis using three measures (network density, in-degree centrality, and out-degree centrality). To validate its practicality, the approach is used to model and analyze the dependencies among 22 PD teams in a real project adapted from the literature and involved developing an automobile engine. An advantage of the approach is that in addition to providing a holistic view of the interactions among PD teams, it permits PD project managers to identity the most important PD teams with respect to information flow control using a proposed new classification system that classifies PD teams under four categories: autonomous, receivers, transmitters, and transceivers.

Published in Engineering and Applied Sciences (Volume 6, Issue 1)
DOI 10.11648/j.eas.20210601.12
Page(s) 18-25
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), 2021. Published by Science Publishing Group

Keywords

Concurrent Engineering, Product Development Teams, Information Flow, Fuzzy, Social Network Analysis

References
[1] Cheng, X., Lan, X. G., and Zhu, Q. (2015) ‘Scalable product platform design based on design structure matrix and axiomatic design’, International Journal of Product Development. Vol. 20, No. 2, pp. 91–106.
[2] Leite, M., Baptista, A. J. and Ribeiro, A. M. R. (2016) ‘A road map for implementing lean and agile techniques in SMEs product development teams’, International Journal of Product Development, Vol. 21, No. 1, pp. 20-40.
[3] Pourjavad, E. and Peng, W. (2017) ‘An integrated fuzzy MCDM approach for risk evaluation of new product in a pipe industry’, International Journal of Product Development, Vol. 22, No. 3, pp. 165-188.
[4] Bashir, H. A., Alzebdeh, K., and Abdo, J. (2009) ‘An Eigenvalue Based Approach for Assessing the Decomposability of Interdependent Design Tasks’, Concurrent Engineering: Research and Applications (CERA Journal), Vol. 19, No. 1. pp. 35-42.
[5] Creese, R. C. and Moore, T. L. (1990) ‘Cost modeling for concurrent engineering’, Cost Engineering, Vol. 32, No. 6, pp. 23-26.
[6] Lawson, M. and Karandikar, H. M. (1994) ‘A survey of concurrent engineering’, Concurrent Engineering: Research and Applications, Vol. l, No. 2, pp. 1-6.
[7] Batallas, D. A. and Yassine, A. A. (2006) ‘Information Leaders in product development organizational networks: social network analysis of the design structure matrix’, IEEE Transactions on Engineering Management, Vol. 53, No. 4, pp. 570-582.
[8] Loch, C. H. and Terwiesch, C. (1998) ‘Communication and uncertainty in concurrent engineering’, Management Science, Vol. 44, No. 8, pp. 1032–1048.
[9] Smith, R. P. and Eppinger, S. D. (1998) ‘Deciding between sequential and parallel tasks in engineering design’, Concurrent Engineering: Research and Applications, Vol. 6, No. 1, pp. 15–25.
[10] Blanco, E., Grebici, K. and Rieu, D. (2007) ‘A unified framework to manage information maturity in design process’, International Journal of Product Development, 4, No. 3–4, pp. 255–279.
[11] Moultrie, J., Clarkson, P. and Probert, D. (2006) ‘A tool to evaluate design performance in SMEs’, International Journal of Productivity and Performance Management, Vol. 55, No. 3, pp. 184–216.
[12] Newman, M. E. J. (2001). Scientific collaborations networks. I. Network construction and fundamental results’, Physical Review E, 64 (1). Available from https://journals.aps.org/pre/abstract/10.1103/PhysRevE.64.016131
[13] Varma, C., A., Lee, R. P. and McCullough, J. (2007) ‘Information use and new product outcomes: The contingent role of strategy’, Journal of Product Innovation Management, Vol. 24, No. 3, pp. 259-273. DOI: 10.1111/j.1540-5885.2007.00249.x
[14] Yang, Q., Yao, Lu, T., and Zhang, B. (2014) ‘An overlapping-based design structure matrix for measuring interaction strength and clustering analysis in product development project’, IEEE Transactions on Engineering Management, Vol. 61, No. 1, pp. 159-169.
[15] McCord, K. R., and Eppinger, S. D. (1993) ‘Managing the integration problem in concurrent engineering. Cambridge, MA: MIT Sloan School of Management Working Paper No. 3594. Retrieved from http://web.mit.edu/~eppinger/www/pdf/McCord_SloanWP3594.pdf
[16] Browning, T. R. (1998) ‘Integrative mechanisms for multiteam integration: Findings from five case studies, Systems Engineering, Vol. 1, No. 2, pp. 95–112.
[17] Eppinger, S. D. (2001) ‘Innovation at the speed of information’, Harvard Business Review, Vol. 79, No. 1, pp. 149–158.
[18] Leenders, R. and Dolfsma, W. A. (2016) ‘Social Networks for innovation and new product development’, The Journal of Product Innovation Management, Vol. 33, No. 2, pp. 123-131.
[19] Kratzer, J., Leenders, R. T. A. J. and Van Engelen, J. M. L. (2010) ‘The social network among engineering design teams and their creativity: A case study among teams in two product development programs’, International Journal of Project Management, Vol. 28, No. 5, pp. 428–436.
[20] Steward, D. V. (1981) ‘Design structure system: A method for managing the design of complex systems’, IEEE Transactions on Engineering Management, Vol. 28, No. 3, pp. 71–74.
[21] Browning, T. R. (2006) ‘Design structure matrix extensions and innovations: A survey and new opportunities’, IEEE Transactions on Engineering Management, Vol. 63, No. 1, pp. 27-52.
[22] Laslo, Z. (2010) ‘Project portfolio management: An integrated method for resource planning and scheduling to minimize planning/scheduling- dependent expenses’, International Journal of Project Management, Vol. 28, No. 6, pp. 609-618.
[23] Moreno, J. L. (1960) The sociometry reader. Glencoe, Illinois: The Free Press.
[24] Al Zaabi, H., and Bashir, H. (2018) ‘Analyzing interdependencies in a project portfolio using social network analysis metrics’, The 5th International Conference on Industrial Engineering and Applications (ICIEA). Singapore, April 26-28, 2018. Retrieved from https://ieeexplore.ieee.org/document/8387150
[25] Mok, K. Y., Shen, G. Q. and Yang, R. J. (2017) ‘Addressing stakeholder complexity and major pitfalls in large cultural building projects’, International Journal of Project Management, Vol. 35, pp. 463–478.
[26] Parraguez, P., Eppinger, S. D. and Maier, A. M. (2015) ‘Information flow through stages of complex engineering design projects: A dynamic network analysis approach’, IEEE Transactions on Engineering Management, Vol. 62, No. 4, pp. 604-617.
[27] Bashir, H., Ojiako, U., and Mota, C. (2020) ‘Modeling and Analyzing Factors Affecting Project Delays Using an Integrated Social Network-Fuzzy MICMAC Approach’, Engineering Management Journal, Vol. 32, No. 1, pp. 26-36.
[28] Bashir, H. and Ojiako, U. Marshall A., Chipulu M., Yousif, A. A. (2020) ‘The Analysis of Information Flow Interdependencies within Projects’, Production Planning and Control, Published online: 24th Sep.
[29] Brunelli, M. and Fedrizzi, M. (2009) ‘A fuzzy approach to social network analysis’, The 2009 International Conference on Advances in Social Network Analysis and Mining (pp. 225–230). Available from https://dl.acm.org/citation.cfm?id=1602691
[30] Chu, J., Liu, X. and Wang, Y. (2016) ‘Social network analysis based approach to group decision making problem with fuzzy preference relations’, Journal of Intelligent & Fuzzy Systems, Vol. 31, No. 3, pp. 1271–1285.
[31] Yager, R. R. (2010) ‘Concept representation in database structures in fuzzy social relational networks’, IEEE Transactions on Systems, Man and Cybernetics: Part A, Vol. 40, No. 2, pp. 413–419.
[32] Yager, R. R., Kacprzyk, J. and Beliakov, G. (2011) ‘Recent developments in the ordered weighted averaging operators: Theory and practice’, Berlin Heidelberg: Springer-Verlag.
[33] Malone, D. W. (1975). An introduction to the application of interpretive structural modeling. Proceedings of the IEEE, Vol. 63, No. 3, pp. 397-404.
[34] Jawad, A. N. A and Bashir, H. (2015) ‘Hierarchical structuring of organizational performance using interpretive structural modeling’, the 2015 International Conference on Industrial Engineering and Operations Management Dubai (pp. 607-613). United Arab Emirates (UAE), March 3 – 5, 2015. Retrieved from http://ieomsociety.org/ieom_2015/papers/422.pdf
[35] Zadeh, L. A. (1999) ‘Some reflections on the anniversary of fuzzy sets and systems’, Fuzzy Sets and Systems, Vol. 100, pp., 1–3.
[36] Pedrycz, W. (1994) ‘Why triangular membership functions?’, Fuzzy Sets and Systems, 64, 21–30.
[37] Duperrin, J. C. and Godet, M. (1973) ‘Méthode de Hiérarchisation des éléments d’un système: essai de prospectivité du système de l’énergie nucléaire dans son contexte sociétal [Hierarchy method elements of a system: test system prospectively of nuclear energy in its societal context]’, Commissariat à l’Energie Atomique [Commissioner for Atomic Energy, Report CEA-R-4541], Rapport CEA-R-4541.
[38] R. K. Kazanjian, R. Drazin, and M. A. Glynn, “Creativity and technological learning: The roles of organization architecture and crisis in large-scale projects,” Journal of Engineering and Technology Management, vol. 17, pp. 273–298, 2000.
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    Messa Alhammadi, Hamdi Bashir. (2021). A Fuzzy-Network Analysis Approach for Modeling and Analyzing Team Dependencies in Product Development Projects. Engineering and Applied Sciences, 6(1), 18-25. https://doi.org/10.11648/j.eas.20210601.12

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    Messa Alhammadi; Hamdi Bashir. A Fuzzy-Network Analysis Approach for Modeling and Analyzing Team Dependencies in Product Development Projects. Eng. Appl. Sci. 2021, 6(1), 18-25. doi: 10.11648/j.eas.20210601.12

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    AMA Style

    Messa Alhammadi, Hamdi Bashir. A Fuzzy-Network Analysis Approach for Modeling and Analyzing Team Dependencies in Product Development Projects. Eng Appl Sci. 2021;6(1):18-25. doi: 10.11648/j.eas.20210601.12

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  • @article{10.11648/j.eas.20210601.12,
      author = {Messa Alhammadi and Hamdi Bashir},
      title = {A Fuzzy-Network Analysis Approach for Modeling and Analyzing Team Dependencies in Product Development Projects},
      journal = {Engineering and Applied Sciences},
      volume = {6},
      number = {1},
      pages = {18-25},
      doi = {10.11648/j.eas.20210601.12},
      url = {https://doi.org/10.11648/j.eas.20210601.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20210601.12},
      abstract = {Developing a complex product in a concurrent engineering environment requires managing information flow among ten or even hundreds of people of different specialties organized in a large number of product development (PD) teams. Managing these teams effectively requires understanding the level of information dependencies among them which are often vague and cannot be precisely predicted. Taking into account the limitations of relevant previous studies, this article proposes a fuzzy-social network analysis approach for modeling and analyzing the information flow among PD teams. The approach involves four major steps: mapping of dependencies, measuring the level of information dependencies based on the fuzzy set theory, visualizing the network, and performing quantitative analysis using three measures (network density, in-degree centrality, and out-degree centrality). To validate its practicality, the approach is used to model and analyze the dependencies among 22 PD teams in a real project adapted from the literature and involved developing an automobile engine. An advantage of the approach is that in addition to providing a holistic view of the interactions among PD teams, it permits PD project managers to identity the most important PD teams with respect to information flow control using a proposed new classification system that classifies PD teams under four categories: autonomous, receivers, transmitters, and transceivers.},
     year = {2021}
    }
    

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    T1  - A Fuzzy-Network Analysis Approach for Modeling and Analyzing Team Dependencies in Product Development Projects
    AU  - Messa Alhammadi
    AU  - Hamdi Bashir
    Y1  - 2021/03/17
    PY  - 2021
    N1  - https://doi.org/10.11648/j.eas.20210601.12
    DO  - 10.11648/j.eas.20210601.12
    T2  - Engineering and Applied Sciences
    JF  - Engineering and Applied Sciences
    JO  - Engineering and Applied Sciences
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    EP  - 25
    PB  - Science Publishing Group
    SN  - 2575-1468
    UR  - https://doi.org/10.11648/j.eas.20210601.12
    AB  - Developing a complex product in a concurrent engineering environment requires managing information flow among ten or even hundreds of people of different specialties organized in a large number of product development (PD) teams. Managing these teams effectively requires understanding the level of information dependencies among them which are often vague and cannot be precisely predicted. Taking into account the limitations of relevant previous studies, this article proposes a fuzzy-social network analysis approach for modeling and analyzing the information flow among PD teams. The approach involves four major steps: mapping of dependencies, measuring the level of information dependencies based on the fuzzy set theory, visualizing the network, and performing quantitative analysis using three measures (network density, in-degree centrality, and out-degree centrality). To validate its practicality, the approach is used to model and analyze the dependencies among 22 PD teams in a real project adapted from the literature and involved developing an automobile engine. An advantage of the approach is that in addition to providing a holistic view of the interactions among PD teams, it permits PD project managers to identity the most important PD teams with respect to information flow control using a proposed new classification system that classifies PD teams under four categories: autonomous, receivers, transmitters, and transceivers.
    VL  - 6
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Author Information
  • Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, United Arab Emirates

  • Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, United Arab Emirates

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