Artificial Intelligence Applied to Project Success: A Literature Review
DOI:
https://doi.org/10.9781/ijimai.2015.3510Keywords:
Artificial Intelligence, DSS, Project ManagementAbstract
Project control and monitoring tools are based on expert judgement and parametric tools. Projects are the means by which companies implement their strategies. However project success rates are still very low. This is a worrying situation that has a great economic impact so alternative tools for project success prediction must be proposed in order to estimate project success or identify critical factors of success. Some of these tools are based on Artificial Intelligence. In this paper we will carry out a literature review of those papers that use Artificial Intelligence as a tool for project success estimation or critical success factor identification.Downloads
References
[1] J. K. Pinto and J. E. Prescott, “Variations In Critical Success Factors Over The Stages In T,” J. Manage., vol. 14, no. 1, 1988.
[2] Project Management Institute, Project Management Body of Knowledge (PMBOK), 5th editio. Project Management Institute, Inc., 2013.
[3] A. Murray, N. Bennett, and C. Bentley, Managing successful projects with PRINCE2, 2009 edition manual. London: The Stationery Office, 2009.
[4] “Directrices para la gestión de proyectos,” UNE-ISO 21500. AENOR, 2012.
[5] The Standish Group, “CHAOS MANIFESTO 2013.” The standish Group, 2013.
[6] R. L. Glass, “IT Failure Rates - 70% or 10-15%?,” IEEE Softw., vol. 22, no. 3, pp. 112, 110–111, May 2005.
[7] D. Baccarini, “The logical framework method for defining project success,” Proj. Manag. J., vol. 30, no. 4, pp. 25–32, 1999.
[8] H. Kerzner, “The Future of Project Management By The Importance of Recognizing Change.” pp. 1–127, 2013.
[9] M. Saynisch, “Beyond frontiers of traditional project management: An approach to evolutionary, self-organizational principles and the complexity theory-results of the research program,” Proj. Manag. J., vol. 41, no. 2, pp. 21–37, Apr. 2010.
[10] K. Davis, “ScienceDirect Different stakeholder groups and their perceptions of project success,” JPMA, vol. 32, no. 2, pp. 189–201, 2014.
[11] A. J. Shenhar, O. Levy, and D. Dvir, “Mapping the Dimensions of Project Success.,” Proj. Manag. J., vol. 28, no. 2, pp. 5–13, 1997.
[12] G. Indelicato, “Project Management Metrics, KPIs, and Dashboards: A Guide to Measuring and Monitoring Project Performance,” Proj. Manag. J., vol. 43, no. 2, pp. 102–102, Apr. 2012.
[13] X. Wang and J. Huang, “The relationships between key stakeholders’ project performance and project success: Perceptions of Chinese construction supervising engineers,” Int. J. Proj. Manag., vol. 24, pp. 253–260, 2006.
[14] F. N. D. Piraquive, R. G. Crespo, and V. H. M. García, “Analysis and Improvement of the Management of IT Projects,” IEEE Lat. Am. Trans., vol. 13, no. 7, pp. 2366–2371, 2015.
[15] L. A. Ika, “Project success as a topic in project management journals,” Proj. Manag. J., vol. 40, no. 4, pp. 6–19, Dec. 2009.
[16] J. G. Geraldi, L. Lee-Kelley, and E. Kutsch, “The Titanic sunk, so what? Project manager response to unexpected events,” … J. Proj. Manag., vol. 28, no. 6, pp. 547–558, 2010.
[17] B. Sauser, R. Reilly, and A. Shenhar, “Why projects fail? How contingency theory can provide new insights–A comparative analysis of NASA’s Mars Climate Orbiter loss,” Int. J. Proj. …, vol. 27, no. 7, pp. 665–679, Oct. 2009.
[18] J. G. Geraldi and G. Adlbrecht, “On faith, fact and interaction in projects,” Proj. Manag. J., vol. 38, no. 1, pp. 32–44, 2007.
[19] B. Shore, “Systematic biases and culture in project failures,” Proj. Manag. J., vol. 39, no. 4, pp. 5–16, Nov. 2008.
[20] R. G. C. Flor Nancy Díaz Piraquive, Víctor Hugo Medina García, “Knowledge Management Model for Project Management,” in Knowledge Management in Organizations, Springer International Publishing, 2015, pp. 235–247.
[21] K. Jugdev, F. Business, S. Winston, C. Avenue, and S. Albert, “Learning from Lessons Learned : Project Management Research Program,” Am. J. Econ. Bus. Adm. 4 13-22, 2012 ISSN 1945-5488, vol. 4, no. 1, pp. 13–22, 2012.
[22] J. Julian, “How project management office leaders facilitate cross-project learning and continuous improvement,” Proj. Manag. J., vol. 39, no. 3, pp. 43–58, Sep. 2008.
[23] F. F. T. Anbari, E. G. Carayannis, and R. J. Voetsch, “Post-project reviews as a key project management competence,” Technovation, vol. 28, no. 10, pp. 633–643, 2008.
[24] J. M. Castillo, C. Cortes, J. Gonzalez, and A. Benito, “Prospecting The Future with AI,” Int. J. Artif. Intell. Interact. Multimed., vol. 1, no. 2, pp. 1–53.
[25] J. M. Castillo, “A crystal ball made of agents,” Int. J. Interact. Multimed. Artif. Intell., vol. 1, no. 3, p. 13, 2010.
[26] J. K. Pinto and D. P. Slevin, “Critical success factors across the project life cycle,” Proj. Manag. J., vol. 19, pp. 67–75, 1988.
[27] T. Cooke-Davies, “The ‘real’ success factors on projects,” Int. J. Proj. Manag., vol. 20, no. 3, pp. 185–190, Apr. 2002.
[28] J. K. Pinto and S. J. Mantel, “The causes of project failure,” IEEE Trans. Eng. Manag., vol. 37, no. 4, pp. 269–276, 1990.
[29] A. de Wit, “Measurement of project success,” Int. J. Proj. Manag., vol. 6, no. 3, pp. 164–170, Aug. 1988.
[30] I. Hyvari, “Success of projects in different organizational conditions,” Proj. Manag. J., vol. 37, no. 4, pp. 31–42, 2006.
[31] A. J. Shenhar, D. Dvir, O. Levy, and A. C. Maltz, “Project Success: A Multidimensional Strategic Concept,” Long Range Plann., vol. 34, no. 6, pp. 699–725, Dec. 2001.
[32] L. a. Kappelman, L. Zhang, and R. McKeeman, “Early Warning Signs of it Project Failure: The Dominant Dozen,” Inf. Syst. Manag., vol. 23, no. 4, pp. 31–36, Sep. 2006.
[33] F. Hartman and R. Ashrafi, “Project management in the information systems and information technologies industries,” Proj. Manag. J., 2002.
[34] K. L. Priddy and P. E. Keller, Artificial Neural Networks. 1000 20th Street, Bellingham, WA 98227-0010 USA: SPIE, 2005.
[35] J. K. Pinto and D. P. Slevin, “Critical factors in successful project implementation,” IEEE Trans. Eng. Manag., vol. EM-34, no. 1, pp. 22–27, 1987.
[36] L. Rodriguez-Repiso, R. Setchi, and J. L. Salmeron, “Modelling IT projects success: Emerging methodologies reviewed,” Technovation, vol. 27, no. 10, pp. 582–594, Oct. 2007.
[37] J. H. Holland, Adaptation in Natural and Artificial Systems, vol. Ann Arbor. 1975.
[38] M.-Y. Cheng, L. Li-Cuan, H.-C. Tsai, and C. Pi-Hung, “Artificial Intelligence Approaches to Dynamic Project Success Assessment Taxonomic,” Life Sci. J., 2012.
[39] L. Gingnell, U. Franke, R. Lagerström, E. Ericsson, and J. Lilliesköld, “Quantifying Success Factors for IT Projects—An Expert-Based Bayesian Model,” Inf. Syst. Manag., vol. 31, no. 1, pp. 21–36, 2014.
[40] M.-Y. Cheng, H.-C. Tsai, and E. Sudjono, “Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry,” Autom. Constr., vol. 21, pp. 46–51, Jan. 2012.
[41] D. Goldberg, B. Korb, and K. Deb, “Messy genetic algorithms: Motivation, analysis, and first results,” Complex Syst., vol. 3, pp. 493–530, 1989.
[42] Y.-R. Wang, C.-Y. Yu, and H.-H. Chan, “Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models,” Int. J. Proj. Manag., vol. 30, no. 4, pp. 470–478, 2012.
[43] D. K. H. Chua, P. K. Loh, Y. C. Kog, and E. J. Jaselskis, “Neural networks for construction project success,” Expert Syst. Appl., vol. 13, no. 4, pp. 317–328, Nov. 1997.
[44] D. Dvir, A. Ben-David, A. Sadeh, and A. J. Shenhar, “Critical managerial factors affecting defense projects success: A comparison between neural network and regression analysis,” Eng. Appl. Artif. Intell., vol. 19, no. 5, pp. 535–543, 2006.
[45] L. Rodriguez-Repiso, R. Setchi, and J. L. Salmeron, “Modelling IT projects success with Fuzzy Cognitive Maps,” Expert Syst. Appl., vol. 32, no. 2, pp. 543–559, Feb. 2007.
[46] F. Reyes, N. Cerpa, A. Candia-Véjar, and M. Bardeen, “The optimization of success probability for software projects using genetic algorithms,” J. Syst. Softw., vol. 84, no. 5, pp. 775–785, May 2011.
[47] G. Thomas and W. Fernández, “Success in IT projects: A matter of definition?,” Int. J. Proj. Manag., vol. 26, no. 7, pp. 733–742, Oct. 2008.
[48] E. J. Jaselskis, “Achieving construction project success through predictive discrete choice models,” University of Texas, Austin, 1988.
[49] K. Peffers, C. E. Gengler, and T. Tuunanen, “Extending Critical Success Factors Methodology to Facilitate Broadly Participative Information Systems Planning,” J. Manag. Inf. Syst., vol. 20, no. 1, pp. 51–85, 2003.
[50] J. L. Salmeron and I. Herrero, “An AHP-based methodology to rank critical success factors of executive information systems,” Comput. Stand. Interfaces, vol. 28, pp. 1–12, 2005.
[51] S. Abe, O. Mizuno, T. Kikuno, N. Kikuchi, and M. Hirayama, “Estimation of project success using Bayesian classifier,” Proceeding 28th Int. Conf. Softw. Eng. - ICSE ’06, no. 4, p. 600, 2006.
[52] C.-H. Ko and M.-Y. Cheng, “Dynamic Prediction of Project Success Using Artificial Intelligence,” Journal of Construction Engineering and Management, vol. 133. pp. 316–324, 2007.
[53] Y.-R. Wang and G. Edward Gibson Jr., “A study of preproject planning and project success using ANNs and regression models,” Autom. Constr., vol. 19, no. 3, pp. 341–346, 2010.
[54] M.-Y. Cheng, Y.-W. Wu, and C.-F. Wu, “Project success prediction using an evolutionary support vector machine inference model,” Autom. Constr., vol. 19, no. 3, pp. 302–307, May 2010.
[55] J. S. Russell, E. J. Jaselskis, and S. P. Lawrence, “Continous assessment of project performance,” J. Constr. Eng. Manag., vol. 123, no. 1, pp. 64–71, 1997.
Downloads
Published
-
Abstract89
-
PDF30
Submit Article
Information
