Abstract
This paper reports the results obtained from use of project complexity parameters in modeling effort estimates. It highlights the attention that complexity has recently received in the project management area. After considering that traditional knowledge has consistently proved to be prone to failure when put into practice on actual projects, the paper endorses the belief that there is a need for more open-minded and novel approaches to project management. With a view to providing some insight into the opportunities that integrate complexity concepts into model building offers, we extend the work previously undertaken on the complexity dimension in project management. We do so analyzing the results obtained with classical linear models and artificial neural networks when complexity is considered as another managerial parameter. For that purpose, we have used the International Software Benchmarking Standards Group data set. The results obtained proved the benefits of integrating the complexity of the projects at hand into the models. They also addressed the need of a complex system, such as artificial neural networks, to capture the fine nuances of the complex systems to be modeled, the projects.
Similar content being viewed by others
References
Ackerman, F., Eden, C., & Williams, T. (1996). A persuasive approach to delay and disruption using ‘mixed methods’. Interfaces, Nov-Dec 1996.
Atkinson, R., Crawford, L., & Ward, S. (2006). Fundamental uncertainties in projects and the scope of project management. International Journal of Project Management, 24(8), 687–698.
Austin, S., Newton, A., Steele, J., & Waskett, P. (2002). Modelling managing project complexity. International Journal of Project Management, 20(3), 191–198.
Baccarini, D. (1996). The concept of project complexity–a review. International Journal of Project Management, 14(4), 201–204.
Bertelsen, S. (2004). Construction management in a complexity perspective. In The 1st SCRI international s!ymposium, The University of Salford, UK.
Brogliato, B., Lozano, R., Maschke, B., & Egeland, O. (2007). Dissipative systems analysis and control. Theory and applications. Berlin: Springer.
Charalambous, C., Charitou, A., & Kaourou, F. (2000). Comparative analysis of artificial neural network models: application in bankruptcy prediction. Annals of Operations Research, 99(1), 403–425.
Cicmil, S., Williams, T., Thomas, J., & Hodgson, D. (2006). Rethinking project management: researching the actuality of projects. International Journal of Project Management, 24(8), 675–686.
Clift, T. B., & Vandenbosch, M. B. (1999). Project complexity and efforts to reduce product development cycle time. Journal of Business Research, 45(2), 187–198.
Crawford, L., Morris, P., Thomas, J., & Winter, M. (2006). Practitioner development: from trained technicians to reflective practitioners. International Journal of Project Management, 24(8), 722–733.
Francalanci, C., & Merlo, F. (2008). The impact of complexity on software design quality and costs: An exploratory empirical analysis of open source applications. In W. Golden, T. Acton, K. Conboy, H. van der Heijden, V. Tuunainen (Eds.), 16th European conference on information systems (pp. 1442–1453), Galway, Ireland.
Geraldi, J. (2008). Patterns of complexity: the thermometer of complexity. Project Perspectives, 29, 4–9.
Geraldi, J., & Adlbrecht, G. (2006). Unravelling complexities in engineering projects. In Second European conference on management of technology, Birmingham, UK.
Han, W.-M., & Huang, S.-J. (2007). An empirical analysis of risk components and performance on software projects. Journal of Systems and Software, 80(1), 42–50.
Hao, G., Lai, K. K., & Tan, M. (2004). A neural network application in personnel scheduling. Annals of Operations Research, 128(1), 65–90.
Ivanova, P., & Tagarev, T. (2000). Indicator space configuration for early warning of violent political conflicts by genetic algorithms. Annals of Operations Research, 97(1), 287–311.
Jiao, T., Peng, J., & Terlaky, T. (2009). A confidence voting process for ranking problems based on support vector machines. Annals of Operations Research, 166(1), 23–38.
Józefowska, J., Mika, M., Rózycki, R., Waligóra, G., & Weglarz, J. (2001). Simulated annealing for multi-mode resource-constrained project scheduling. Annals of Operations Research, 102(1), 137–155.
Kähkönen, K., & Latvanne, A. (Eds.) (2008). Project perspectives (Vol. 29). International Project Management Association.
Kainen, P., Kurková, V., & Vogt, A. (2001). Continuity of approximation by neural networks in lp spaces. Annals of Operations Research, 101(1), 143–147.
Kiel, L. D., & Elliot, E. W. (1997). Chaos theory in the social sciences. Perseus Publishing.
Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612.
Menache, I., Mannor, S., & Shimkin, N. (2005). Basis function adaptation in temporal difference reinforcement learning. Annals of Operations Research, 134(1), 215–238.
Perantonis, S. J., Ampazis, N., & Virvilis, V. (2000). A learning framework for neural networks using constrained optimization methods. Annals of Operations Research, 99(1), 385–401.
Pernía-Espinoza, A., Ordieres-Meré, J., Martínez de Pisón, F. J., & González-Marcos, A. (2005). Tao-robust backpropagation learning algorithm. Neural Networks, 18, 191–204.
R Development Core Team (2008). A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
Schuster, H. (2005). Collective dynamics of nonlinear and disordered systems. (pp. 259–369). Berlin: Springer.
Schwardt, M. & Fischer, K. (2009). Combined location–routing problems—a neural network approach. Annals of Operations Research, 167(1), 254–269.
Stacey, R., Griffin, D., & Shaw, P. (2000). Complexity and management: fad or radical challenge to systems thinking? (complexity and emergence in organizations). Routledge.
Teodorovic, D., Varadarajan, V., Popovic, J., Chinnaswamy, M., Ramaraj, S. (2006). Dynamic programming—neural network real-time traffic adaptive signal control algorithm. Annals of Operations Research, 143(1), 123–131.
Thangavel, K., & Kumar, D. (2006). Optimization of code book in vector quantization. Annals of Operations Research, 143(1), 317–325.
Thomas, J., & Mengel, T. (2008). Preparing project managers to deal with complexity. International Journal of Project Management, 26(3), 304–315.
Turner, J. R., & Cochrane, R. A. (1993). Goals-and-methods matrix: coping with projects with ill defined goals and/or methods of achieving them. International Journal of Project Management, 11(2), 93–102.
Williams, T. M. (1995). Holistic methods in project management. In Proceedings of INTERNET symposium (pp. 332–336), St. Petersburg, Russia.
Williams, T. M. (1999). The need for new paradigms for complex projects. International Journal of Project Management, 17(5), 269–273.
Williams, T. (2005). Assessing and moving on from the dominant project management discourse in the light of project overruns. IEEE Transactions on Engineering Management, 52(4), 497–508.
Williams, T., Eden, C., Ackerman, F., & Tait, A. (1995). The effects of design changes and delays on project costs. The Journal of the Operational Research Society, 46(7), 809–818.
Winter, M., Andersen, E. S., Elvin, R., & Levene, R. (2006a). Focusing on business projects as an area for future research: an exploratory discussion of four different perspectives. International Journal of Project Management, 24(8), 699–709.
Winter, M., Smith, C., Morris, P., & Cicmil, S. (2006b). Directions for future research in project management: the main findings of a UK government-funded research network. International Journal of Project Management, 24(8), 638–649.
Xia, W., & Lee, G. (2005). Complexity of information systems development projects: conceptualization and measurement development. Journal of Management Information Systems, 22(1), 45–83.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Castejón-Limas, M., Ordieres-Meré, J., González-Marcos, A. et al. Effort estimates through project complexity. Ann Oper Res 186, 395–406 (2011). https://doi.org/10.1007/s10479-010-0776-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-010-0776-0