Realistic planning of research and development projects

    Dorota Kuchta Affiliation


Purpose – the purpose of the article is to propose a more flexible approach to the planning of research and development projects, especially for the needs of project calls. In those calls, financial means are often refused to projects with a high level of uncertainty. The proposal should support a positive assessment of the most pathbreaking projects and a flexible reaction to the failure or partial failure of such projects.

Research methodology – In the proposal type 1 and type 2 fuzzy sets are applied. The proposal will is using case studies.

Findings – the results will modify the way research and development project are planned and controlled.

Research limitations – the proposal has not been verified in practice, for which many more case studies and cooperation with financing institutions would be necessary. Also, it does not use up all the modelling possibilities of uncertainty and dependencies between various uncertain elements of the project plan.

Practical implications – the results might be used in the design of forms used by various financing institutions (e.g. European Commission or national research funding institutions) in project calls. Originality/Value – the proposal presents an entirely different way research and development projects should be planned and described. Type 2 fuzzy sets are used for the description, where various elements of the project plan (e.g. objectives, methods, tasks) are assigned a possibility degree (of attainment, of usage etc.).

Keyword : research project, R&D project, project plan, project uncertainty, fuzzy set

How to Cite
Kuchta, D. (2019). Realistic planning of research and development projects. Business, Management and Education, 17(2), 309-326.
Published in Issue
Dec 31, 2019
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Cropper, P., & Cook, R. (2000). Developments: activity-based costing in universities – five years on. Public Money & Management, 20(2), 61-68.

Courtot, H. (1998), La gestion de risques dans les projets. Economica, Paris.

Dowling, P. (2014). Successfully transitioning a research project to a commercial spin-out using an agile software process. Journal of Software: Evolution and Process, 26(5), 468-475.

Hanss, M. (2010). Applied fuzzy arithmetic: An introduction with engineering applications. Springer.

Holzmüller-Laue, S., & Göde, B. (2011). Agile business process management in research projects of life sciences. Lecture Notes in Business Information Processing, 90, 336-344.

Klaus-Rosińska, A. (2019). Success of reserach and reserach and development projects. Wrocław: Publishig House of Wroclaw University of Technology (in press). (in Polish).

Korol, T. (2012). Fuzzy logic in financial management. In E. P. Dadios (Ed.), Fuzzy logic emerging technologies and applications, (pp. 259-286). Rijeka, Chorwacja: InTechOPEN.

Kuchta, D. (2019a). Application of type 2 fuzzy sets to project management. In M. Popławski, S. Stanek. Decisions in situation of endangerment. Wrocław, Poland: Academy of Land Forces.

Kuchta, D. (2019b, May 9-10). Research and development projects fuzzy definition in the university context. In J. Stankevičienė, et al. (Eds.), International Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering 2019”, (CIBMEE-2019), Vilnius, Lithuania (pp. 564-571). Vilnius: VGTU Press.

Kuchta, D. (2014). Planning and realization control of research projects. Zarządzanie Publiczne, 2, 217-228.

Kuchta, D., Betta, J., Jastrzębska, J., Frączkowski, K., Gładysz, B., Prałat, E., Ptaszyńska, E., Rola, P., Walecka-Jankowska, K., Ropuszyńska-Surma, E., Skomra, A., Ryńca, R., Klaus-Rosińska, A., & Mrzygłocka-Chojnacka, J. (2017a, April 26-27). Success and failure factors of R&D projects at universities in Poland and France. In A. Nowak, Z. Wilimowska (Eds.), Business risk in changing dynamics of global village: Proceedings of 1st International Conference on Business Risk in Changing Dynamics of Global Village, (pp. 265-278). Nysa: Publishing Office University of Applied Sciences.

Kuchta, D., Gładysz, B., Skowron, D., & Betta, J. (2015). R&D projects in the science sector. R&D Management, 47(1), 88-110.

Kuchta, D., L’Ebraly, P., & Marchwicka, E. (2017b). Agile-similar approach based on project crashing to manage research projects. Lecture Notes in Business Information Processing, 291, 225-241.

Kuchta, D., & Skowron, D. (2016). Classification of R&D projects and selection of R&D project management concept. R&D Management, 46(5), 831-841.

Kuchta, D., & Skowron, D. (2017). Traditional versus agile scheduling and implementation of R&D projects: a case study, In J. Vopava, et al. (Eds.), Proceedings of AC 2017, MAC Prague (pp. 622-630). Mendel, J. M. (2015). Type-2 fuzzy sets and systems: A retrospective. Informatik Spektrum, 38(6), 523532.

Mendel, J. M., & John, R. I. B. (2014). Type-2 fuzzy sets made simple. IEEE Transactions on Fuzzy Systems, 10, 117-127.

Project Management Institute. (2018). Project Management Body of Knowledge. Project Management Institute.

Shenhar, A. J. (2001). One size doesn’t fit all projects: Exploring classical contingency domains. Management Science, 47(3), 394-414.

Solak, S., Clarke J.-P. B., Johnson, E. L., & Barnes, E. R. (2010). Optimization of R&D project portfolios under endogenous uncertainty. European Journal of Operational Research, 207(1), 420-433.

Song, Y.-I., Lee, D.-H., Lee, Y.-G., & Chung, Y.-Ch. (2007). Managing uncertainty and ambiguity in frontier R&D projects: A Korean case study. Journal of Engineering and Technology Management, 24(3), 231-250.

Wysocki, R. K., Kaikini, S., & Sneed, R. (2014). Effective project management: Traditional, adaptive, extreme. Indianapolis, Ind: Wiley Pub.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.