Success evaluation model for project management

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dc.contributor.authorDoskočil, Radek
dc.contributor.authorŠkapa, Stanislav
dc.contributor.authorOlšová, Petra
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2016-12-05
dc.date.available2016-12-05
dc.date.issued2016-12-05
dc.description.abstractThe article presents an expert fuzzy model for evaluation of the project success rate. The model is implemented with the use of fuzzy logic. First, fundamental theoretical principles related to the problems of project success rate, fuzzy sets and fuzzy logic are introduced, after which a fuzzy model for project success rate evaluation, including partial sub-models, is presented in the form of a case study which represents the main goal of the article. The fuzzy model is implemented in the MATLAB software environment with the use of the Fuzzy Logic Toolbox application, where it is also verified and further specified. The fuzzy model consists of six input variables which are divided according to their character into three categories in each block (RB1, RB2, RB3) and are separately evaluated. Partial outputs from the blocks (RB1, RB2, RB3) are simultaneously inputs for block RB4, from which there is a single output variable – project success (PS). The RB1 rule block evaluates the situation from the point of view of the state of the project. The RB2 rule block evaluates the total value of project risk. The RB3 rule block evaluates project quality. The RB4 rule block evaluates the total project success rate. Experimenting with the fuzzy model allows simulation of the uncertainty that is always involved in projects. The case study introduces an overall diagram of the fuzzy model, the input and output variables, including their attributes, and the evaluation rules of the four rule blocks. The proposed fuzzy model is used to evaluate project success primarily in the implementation phase, then repeatedly after each phase of the project is completed. This provides project managers with a tool that allows relatively rapid evaluation of the success of the project and the opportunity of applying appropriate measures in good time if necessary.en
dc.format.extent167-185 s.cs
dc.identifier.doi10.15240/tul/001/2016-4-012
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/19276
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonBalkienė, K. (2013). Sustainable innovativeness: issues and public policy. Journal of Security and Sustainability Issues, 3(2), 53-76. doi:10.9770/jssi.2013.3.2(5).
dc.relation.isbasedonBočková, H.K., Vořechová, E., & Valenta, K. (2005). State of the Project Management Practice in the Czech Republic – the Research Methodology Framework. E&M Ekonomie & Management, 8(3), 79-84.
dc.relation.isbasedonBushan, R. P., & Ravi, S. N. (2012). Fuzzy Critical Path Method Based on Lexicographic Ordering of Fuzzy Numbers. Pakistan Journal of Statistics & Operation Research, 8(1), 139-154.
dc.relation.isbasedonChan, A. (2001). Framework for measuring success of construction projects (Report). Brisbane: CRC for Construction Innovation.
dc.relation.isbasedonChan, A. P. C., & Chan, A. P. L. (2004). Key performance indicators for measuring construction success. Benchmarking: An International Journal, 11(2), 203-221. doi:10.1108/14635770410532624.
dc.relation.isbasedonChanas, S., & Zielinski, P. (2001). Critical path analysis in the network with fuzzy activity times. Fuzzy sets and Systems, 122(2), 195-204. doi:10.1016/S0165-0114(00)00076-2.
dc.relation.isbasedonChipulu, M. et al. (2014). Exploring the impact of cultural values on project performance: The effects of cultural values, age and gender on the perceived importance of project success/failure factors. International Journal of Operations & Production Management, 34(3), 364-389. doi:10.1108/IJOPM-04-2012-0156.
dc.relation.isbasedonColin, J., Vanhoucke, M. (2016). Empirical Perspective on Activity Durations for Project-Management Simulation Studies. Journal of Construction Engineering and Management, 142(1). doi:10.1061/(ASCE)CO.1943-7862.0001022.
dc.relation.isbasedonCooper, D. et al. (2014). Project Risk Management Guidelines: Managing Risk with ISO 31000 and IEC 62198. Wiley.
dc.relation.isbasedonCummings, S., Bridgman, T., Brown, K. G. (2016). Unfreezing change as three steps: Rethinking Kurt Lewin’s legacy for change management. Human Relations, 69(1), 33-60. doi:10.1177/0018726715577707.
dc.relation.isbasedonDavis, K. (2014). Different stakeholder groups and their perceptions of project success. International Journal of Project Management, 32(2), 189-201. doi:10.1016/j.ijproman.2013.02.006.
dc.relation.isbasedonde Carvalho, M. M., Patah, L. A., Bido, D. de S. (2015). Project management and its effects on project success: Cross-country and cross-industry comparisons. International Journal of Project Management. 33(7), 1509-1522. doi:10.1016/j.ijproman.2015.04.004.
dc.relation.isbasedonDoskočil, R. (2015). Fuzzy logic: An instrument for the evaluation of project status. Revista de Metodos Cuantitativos para la Economia y la Empresa. 19(1), 5-23.
dc.relation.isbasedonDoskočil, R., & Doubravský, K. (2013). Critical Path Method based on Fuzzy Numbers: Comparison with Monte Carlo Method. In Creating Global Competitive Economies (pp. 1402-1411). Rome: International Business Information Management Association.
dc.relation.isbasedonDostál, P. (2011). Advanced Decision Making in Business and Public Services. Brno: CERM.
dc.relation.isbasedonDudzevičiūtė, G., & Tvaronavičienė, M. (2011). Measurement framework of innovation activity: theoretical approaches’ analysis. Journal of Security and Sustainability Issues, 1(1), 63-75.
dc.relation.isbasedonEarned Value Management Terms and Formulas for Project Managers. Retrieved March 31, 2016, from http://www.dummies.com/how-to/content/earned-value-management-terms-and-formulas-for-pro.html.
dc.relation.isbasedonGrossmann, V. (2009). Entrepreneurial innovation and economic growth. Journal of Macroeconomics, 31(4), 602-613. doi:10.1016/j.jmacro.2008.12.008.
dc.relation.isbasedonJaradat, A. R., & Andreica, C. M. H. (2010). The Cost-Benefit Analysis in the Management of the Investment Projects. Metalurgia International, 15, 173-175.
dc.relation.isbasedonJoslin, R., & Muller, R. (2015). Relationships between a project management methodology and project success in different project governance contexts. International Journal of Project Management. 33(6), 1377-1392. doi:10.1016/j.ijproman.2015.03.005.
dc.relation.isbasedonKemmeter, S. (2014). By Controlling for Project Success - Partnership Strategies for Controller and Manager. Betriebswirtschaftliche Forschung und Praxis, 66(5), 579-580.
dc.relation.isbasedonKhan, A. S., & Rasheed, F. (2015). Human resource management practices and project success, a moderating role of Islamic Work Ethics in Pakistani project-based organizations. International Journal of Project Management, 33(2), 435-445. doi:10.1016/j.ijproman.2014.08.006.
dc.relation.isbasedonKlir, G. J., & Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic, Theory and Applications. New Jersey: Prentice Hall.
dc.relation.isbasedonKloppenborg, T. J. et al. (2014). Project Success and Executive Sponsor Behaviors: Empirical Life Cycle Stage Investigations. Project Management Journal, 45(1), 9-20. doi:10.1002/pmj.21396.
dc.relation.isbasedonKuchta, D. (2001). Use of Fuzzy numbers in project risk (criticality) assessment. International Journal of Project Management, 19(5), 305-310.
dc.relation.isbasedonKuo, Y.-C., & Lu, S.-T. (2013). Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management, 31(4), 602-614. doi:10.1016/j.ijproman.2012.10.003.
dc.relation.isbasedonLacko, B. (2015). RIPRAN Metoda pro hodnocení projektových rizik. Retrieved March 19, 2015, from http://ripran.cz/licence.html.
dc.relation.isbasedonLiu, Z. C., & Ye, Y. (2015). Models for comprehensive evaluating modeling of investment project risk with trapezoid fuzzy linguistic information. Journal of Intelligent & Fuzzy Systems, 28(1), 151-156. doi:10.3233/IFS-141284.
dc.relation.isbasedonMazur, A. et al. (2014). Rating defence major project success: The role of personal attributes and stakeholder relationships. International Journal of Project Management, 32(6), 944-957. doi:10.1016/j.ijproman.2013.10.018.
dc.relation.isbasedonMcKay, D. S., & Ellis, T. J. (2014). Tracking the Flow of Knowledge in IT organizations; The Impact of Organizational Learning Factors and Project Learning Practices on Project Success. Proceedings of the 2014 47th Hawaii International Conference on System Sciences (pp. 5185-5194). Washington, D.C.: IEEE Computer Society.
dc.relation.isbasedonNaeni, L. M., & Salehipour, A. (2011). Evaluating fuzzy earned value indices and estimates by applying alpha cuts. Expert Systems with Applications, 38(7), 8193-8198. doi:10.1016/j.eswa.2010.12.165.
dc.relation.isbasedonNaeni, L. M., Shadrokh, S., Salehipour. A. (2011). A fuzzy approach for the earned value management. International Journal of Project Management, 29(6), 764-772. doi:10.1016/j.ijproman.2013.02.002.
dc.relation.isbasedonNasirzadeh, F. et al. (2014). Dynamic modeling of the quantitative risk allocation in construction projects. International Journal of Project Management, 32(3), 442-451. doi:10.1016/j.ijproman.2013.06.002.
dc.relation.isbasedonNieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2), 220-231. doi:10.1016/j.ijproman.2010.02.002.
dc.relation.isbasedonOliveros, A. V. O., & Fayek, A. R. (2005). Fuzzy Logic Approach for Activity Delay Analysis and Schedule Updating. Journal of Construction Engineering and Management, 131(1), 42-51. doi:.
dc.relation.isbasedonRelich, M. (2015). A computational intelligence approach to predicting new product success. In Proceedings of the 11th International Conference on Strategic Management and its Support by Information Systems (pp. 142-150).
dc.relation.isbasedonRelich, M., & Muszyński, W. (2014). The use of intelligent systems for planning and scheduling of product development projects. Procedia computer science, 35, 1586-1595. doi:10.1016/j.procs.2014.08.242.
dc.relation.isbasedonRodriguez, A., Ortega, F., & Concepcion, R. (2016). A method for the evaluation of risk in IT projects. Expert Systems with Applications, 45(March), 273-285. doi:10.1016/j.eswa.2015.09.056.
dc.relation.isbasedonRudnik, K., & Deptula, A. M. (2015). System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects. Expert Systems with Applications, 42(17), 6365-6379. doi:10.1016/j.eswa.2015.04.025.
dc.relation.isbasedonSamset, K. (1998). Project management in a high-uncertainty situation: Uncertainty, risk and project management in international development projects (Doctoral dissertation). Trondheim: Norwegian University of Science and Technology, Faculty of Civil and Environmental Engineering, Department of Building and Construction Engineering.
dc.relation.isbasedonSpolečnost pro projektové řízení Česká republika. (2016). Retrieved March 31, 2016, from http://cspr.cz/.
dc.relation.isbasedonSchibi, O. (2013). Managing Stakeholder Expectations for Project Success. A Knowledge Integration Framework and Value Focused Approach. Plantation: J. Ross Publishing.
dc.relation.isbasedonSchwable, K. (2011). Řízení projektů v IT. Kompletní průvodce. Brno: Computer Press.
dc.relation.isbasedonSerra, C. E. M., & Kunc, M. (2015). Benefits Realisation Management and its influence on project success and on the execution of business strategies. International Journal of Project Management, 33(1), 53-56. doi:10.1016/j.ijproman.2014.03.011.
dc.relation.isbasedonTodorovic, M. L., Petrovic, D. C., Mihic, M. M., Obradovic, V. L., & Bushuyev, S. D. (2015). Project success analysis framework: A knowledge-based approach in project management. International Journal of Project Management, 33(4), 772-783. doi:10.1016/j.ijproman.2014.10.009.
dc.relation.isbasedonÚřad pro technickou normalizaci, metrologii a státní zkušebnictví. (2005). ČSN EN ISO 9000 (010300) Systémy managementu kvality – Základní principy a slovník. Praha: Úřad pro technickou normalizaci, metrologii a státní zkušebnictví.
dc.relation.isbasedonÚřad pro technickou normalizaci, metrologii a státní zkušebnictví. (2004). ČSN ISO 10006 (010333) Systémy managementu jakosti – Směrnice pro management jakosti projektů. Praha: Úřad pro technickou normalizaci, metrologii a státní zkušebnictví.
dc.relation.isbasedonVanhoucke, M. (2014). Earned Value Management. In Integrated Project Management and Control, Management for Professionals (pp. 17-31). Springer International Publishing. doi:10.1007/978-3-319-04331-9_2.
dc.relation.isbasedonWauters, M., Vanhoucke, M. (2016). A comparative study of Artificial Intelligence methods for project duration forecasting. Expert Systems with Applications, 46(March), 249-261. doi:10.1016/j.eswa.2015.10.008.
dc.relation.isbasedonWimelius, M. E., Engberg, J. (2015). Crisis Management through Network Coordination: Experiences of Swedish Civil Defence Directors. Journal of Contingencies and Crisis Management. 23(3), 129-137. doi:10.1111/1468-5973.12048.
dc.relation.isbasedonYang, L.-R., Huang, C.-F., Wu, K.-S. (2011). The association among project manager’s leadership style, teamwork and project success. International Journal of Project Management. 29(3), 258-267. doi:10.1016/j.ijproman.2010.03.006.
dc.relation.isbasedonYin-xiang, L. (2013). The rules and Models to Estimate IRR Conveniently and Effectively. New York: IEEE.
dc.relation.isbasedonZadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/S0019-9958(65)90241-X.
dc.relation.isbasedonZadeh, L. A. (2007). Fuzzy logic as the logic of natural languages. In P. Melin, O. Castillo, E. G. Ramirez, J. Kacprzyk, & W. Pedrycz. (Eds.), Analysis and Design of Intelligent Systems Using Soft Computing Techniques (pp. 1-2). Berlin: Springer-Verlag.
dc.relation.isbasedonZhang, S. X., & Li, X. L. (2011). Construction Project Risk Assessment Model. In 4th International Conference on Engineering and Risk Management (ERM) (pp. 42-45). Toronto.
dc.relation.isbasedonZimmermann, H. J. (2001). Fuzzy Set Theory – and its Applications (4th ed.). London: Kluwer Academic Publishers.
dc.relation.isbasedonZwikael, O. et al. (2014). The moderating effect of risk on the relationship between planning and success. International Journal of Project Management, 32(3), 435-441. doi:10.1016/j.ijproman.2013.07.002.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectproject managementen
dc.subjectproject successen
dc.subjectevaluation modelen
dc.subjectfuzzy logicen
dc.subjectdecision-makingen
dc.subject.classificationC44
dc.subject.classificationM11
dc.subject.classificationM21
dc.titleSuccess evaluation model for project managementen
dc.typeArticleen
local.accessopen
local.citation.epage185
local.citation.spage167
local.facultyFaculty of Economics
local.fulltextyes
local.relation.abbreviationE&Men
local.relation.abbreviationE+Mcs
local.relation.issue4
local.relation.volume19
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