Skip to main content
SpringerLink
Log in

Effort estimates through project complexity

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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.

    Article  Google Scholar 

  • Austin, S., Newton, A., Steele, J., & Waskett, P. (2002). Modelling managing project complexity. International Journal of Project Management, 20(3), 191–198.

    Article  Google Scholar 

  • Baccarini, D. (1996). The concept of project complexity–a review. International Journal of Project Management, 14(4), 201–204.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hao, G., Lai, K. K., & Tan, M. (2004). A neural network application in personnel scheduling. Annals of Operations Research, 128(1), 65–90.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Menache, I., Mannor, S., & Shimkin, N. (2005). Basis function adaptation in temporal difference reinforcement learning. Annals of Operations Research, 134(1), 215–238.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • R Development Core Team (2008). A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0.

    Google Scholar 

  • Schuster, H. (2005). Collective dynamics of nonlinear and disordered systems. (pp. 259–369). Berlin: Springer.

    Google Scholar 

  • Schwardt, M. & Fischer, K. (2009). Combined location–routing problems—a neural network approach. Annals of Operations Research, 167(1), 254–269.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Thangavel, K., & Kumar, D. (2006). Optimization of code book in vector quantization. Annals of Operations Research, 143(1), 317–325.

    Article  Google Scholar 

  • Thomas, J., & Mengel, T. (2008). Preparing project managers to deal with complexity. International Journal of Project Management, 26(3), 304–315.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Xia, W., & Lee, G. (2005). Complexity of information systems development projects: conceptualization and measurement development. Journal of Management Information Systems, 22(1), 45–83.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Ingenierías Mecánica, Informática y Aeroespacial, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s.n., 24071, León, Spain

    Manuel Castejón-Limas & Víctor González-Castro

  2. Dept. Ingeniería de Organización, Administración de Empresas y Estadística, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, c/José Gutiérrez Abascal, 2, 28006, Madrid, Spain

    Joaquín Ordieres-Meré

  3. Dept. Ingeniería Mecánica, Universidad de La Rioja, c/Luis de Ulloa, 20, 26004, Logroño, La Rioja, Spain

    Ana González-Marcos

Authors
  1. Manuel Castejón-Limas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joaquín Ordieres-Meré
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana González-Marcos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Víctor González-Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joaquín Ordieres-Meré.

Rights and permissions

Reprints 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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-010-0776-0

Keywords

Search

Navigation