Determining a Fuzzy Model of Time Buffer Size in Critical Chain Project Management

• Autorzy: Marek-Kołodziej, Katarzyna , Łapuńka, Iwona
• Języki publikacji: EN

Abstrakty

EN

Elaborating and applying a new model for estimating the time buffer size of a project programme, which shall guarantee a 90 % probability of timely project execution. The research included source text analysis to provide information on a research gap and the identification of the research problem. The research problem was identified: the time buffer size in a critical path programme does not guarantee a 90 % probability of timely project execution. A new model was then elaborated to estimate the buffer size; it was applied in a technical production preparation project. An additional comparative analysis was performed using the following methods to verify the model more accurately: half of the time total of a path, the sum of squares (SSQ), and the root square error method (RSEM). The application of the fuzzy model to estimate the buffer size in a critical chain programme offers can shorten the total planned project duration. It has a higher probability of timely project execution than other methods for estimating the buffer size. It guarantees a 90 % probability of timely project execution, keeping aggressive task times, which eliminates unwanted situations such as student syndrome, Parkinson’s law, overestimating task duration, and multitasking. Project programming is an inherent part of the project planning stage in project management. Recently, project management has been increasingly developing, which has been confirmed by the article’s source literature analysis. The analysis revealed a research gap in models estimating project buffer size, which might guarantee a 90 % probability of timely project execution. Thus, a fuzzy model for estimating time buffer size in a critical chain was developed, constituting added value to the science of management and quality of production engineering (currently, mechanical engineering). The fuzzy model for estimating time buffer size was applied in one Polish enterprise in a project for a new product’s technical production preparation. The fuzzy model for estimating time buffer size permits the shortening of the duration of tasks to aggressive times, guaranteeing a 90 % probability of project timely execution. The elaborated model for estimating time buffer size may be applied further in practice in projects programmed using the critical chain method.

Słowa kluczowe

PL

zarządzanie projektami; harmonogramowanie projektu; zarządzanie projektem łańcucha krytycznego; bufor czasu; rozmiar bufora; zbiory rozmyte

EN

project management; project scheduling; critical chain project management; time buffer; buffer size; fuzzy sets

• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2024
• Tom: Vol. 16, no. 3
• Strony: 41--55
• Opis fizyczny: Bibliogr. 57 poz., tab., wykr.

Twórcy

autor

Marek-Kołodziej, Katarzyna

• Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland,

autor

Łapuńka, Iwona

• Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland,

Bibliografia

• Abbasbandy, S., Viranloo, T. A., López-Pouso, Ó., & Nieto, J. J. (2004). Numerical Methods for Fuzzy Differential Inclusions. Computer and Mathematics with Applications, 48, 1633-1641. doi: 10.1016/j. camwa.2004.03.009
• Altarazi, F., & Bao, H. (2015). Investigating the Impact of Buffer Size in Critical Chain Management. Flexible Automation and Intelligent Manufacturing (FAIM2015), 1-8.
• Ashtiani, B., Jalali, G. R., Aryanezhad, M. B., & Makui, A. (2007). A new approach for buffer sizing in critical chain scheduling. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 2–4 December 2007, 1037-1041.
• Atkinson, R. (1999). Project Management: Cost, Time and Quality, Two Best Guesses and a Phenomenon, It’s Time to Accept Other Success Criteria. International Journal of Project Management, 17, 337-342. doi: 10.1016/S0263-7863(98)00069-6
• Cserháti, G., & Szabó, L. (2014). The relationship between success criteria and success factors in organizational event projects. International Journal of Project Management, 32, 613-624. doi: 10.1016/j.ijproman.2013.08.008
• Fallah, M., Ashitiani, B., & Aryanezhad, B. (2010). Critical chain project scheduling: utilizing uncertainty for buffer sizing. International Journal of Research and Reviews in Applied Sciences, 3(3), 280-289.
• Ghoddousi, P., Ansari, R., & Makui, A. (2017). A risk-oriented buffer allocation model based on critical chain project management. KSCE Journal of Civil Engineering, 21, 1536-1548. doi: 10.1007/s12205-016-0039-y
• Goldratt, E. M. (1997). Critical Chain. Great Barrington: The North River Press Publishing Corporation.
• Hass, K. B. (2010). Managing complex projects that are too large, too long and too costly. Retrieved from https:// www.projecttimes.com/articles/managing-complexprojects-that-are-too-large-too-long-and-too-costly. html
• Hellendoorn, H., & Thomas, C. (1993). Defuzzification in fuzzy controllers. Journal of Intelligent and Fuzzy Systems, 1(2), 109-123. doi: 10.3233/ifs-1993-1202
• Herroelen, W., & Leus, R. (2004). The construction of stable project baseline schedule. European Journal of Operational Research, 156, 550-565. doi: 10.1016/S0377- 2217(03)00130-9
• Iranmanesh, H., Mansourian, F., & Kouchaki, S. (2015). Critical chain scheduling: a new approach for feeding buffer sizing. International Journal of Operational Research, 25(1), 114-130. doi: 10.1504/ IJOR.2016.073254
• Izmailov, A., Korneva, D., & Kozhemiakim, A. (2016). Project management using the buffers of time and resources. Procedia-Social and Behavioral Sciences, 235, 189-197. doi: 10.1016/j.sbspro.2016.11.014
• Jugdev, K., & Mller, R. (2005). A retrospective look at our evolving understanding of project success. Project Management Journal, 36(4), 9-31. doi: 10.1177/875697280503600403
• Kuchta, D. (2014). A new concept of project robust schedule – use of buffers. Information Technology and Quantitative Management (ITQM 2014), Procedia Computer Science, 31, 957-965. doi: 10.1016/j. procs.2014.05.348
• Leach, L. P. (1999). Critical chain project management improves project performance. Project Management Journal, 30(2), 39-51. doi: 10.1177/875697289903000207
• Leach, L. P. (2003). Schedule and cost buffer sizing: How to account for performance and your model. Project Management Journal, 34(2), 34-47. doi: 10.1177/875697280303400205
• Leach, L. P. (2005). Lean Project Management: Eight Principles for Success. Boise Idaho: Advanced Projects Inc.
• Leach, L. P. (2014). Critical Chain Project Management (Third Edition). Boston: Artech House.
• Li, H., Cao, Y., Lin, Q., & Zhu, H. (2022). Data-driven project buffer sizing in critical chains. Automation in Construction, 135. doi: 10.1016/j.autcon.2022.104134
• Liu, J., & Whangbo, T.-K. (2012). A study on the buffer sizing method of CCPM technique using statistical analysis. In: Lee G., Howard D., Ślęzak D., Hong Y.S. (Eds.), Convergence and Hybrid Information Technology. ICHIT 2012. Communications in Computer and Information Science, 310. Springer, Berlin, Heidelberg.
• Martens, C. D. P., Machado, F. J., Martens, M. L., Oliveira e Silva, T. Q. P., & de Freitas, H. M. R. (2018). Linking entrepreneurial orientation to project. Journal of Project Management, 36(2), 255-266. doi: 10.1016/j. ijproman.2017.10.005
• Min, Z., & Rongqiu, C. (2008). Buffer sized technique in critical chain management: A fuzzy approach. In Wireless Communications. Networking and Mobile Computing, 2008. WiCOM ‘08. 4th International Conference, 12–14 October, 1-4.
• Molinari, F. (2016). A new criterion of choice between generalized triangular fuzzy numbers. Fuzzy Sets and Systems, 296, 51-69. doi: 10.1016/j.fss.2015.11.022
• Moussa, D. A., El-Korany, T. M., Etman, E. E., & Taher, S. F. (2016). Evaluation of critical chain buffer sizing techniques. AICSGE, Egypt, 1-10.
• Mller, R., & Turner, R. (2007). The influence of project managers on project success criteria and project success by type of project. European Management Journal, 25(4), 298-309. doi: 10.1016/j.emj.2007.06.003
• Nafkha, R. (2016). The PERT method in estimating project duration. Information Systems in Management, 5(4), 542-550.
• Newbold, R. (1998). Project Management in the Fast Lane – Applying the Theory of Constraints. Boca Raton: The St. Lucie Press.
• Pedrycz, W. (1993). Fuzzy Control and Fuzzy Systems (Second Extended Edition). England: Research Studies Press.
• Poshdar, M., González, V., Raftery, G., Orozco, F., Romeo, J., & Forcael, E. (2016). A probabilistic-based method to determine optimum size of project buffer in construction schedules. Journal of Construction Engineering and Management, 142(10). doi: 10.1061/ (ASCE)CO.1943-7862.0001158
• Project Management Institute. (2013). A Guide to the Project Management Body of Knowledge (5th edition). Newtown Square, USA: PMI.
• Ravalji, J. M., & Deshpande, V. A. (2014). Comparative study of alternatives for 50% rule in critical chain project management. In International Conference on Design, Manufacturing and Mechatronics (pp. 1–10). KJEI’s Trinity College of Engineering and Research.
• Raz, T., Barnes, R., & Dvir, D. (2003). A critical look at critical chain project management. Project Management Journal, 34(4), 24-32. doi: 10.1177/875697280303400404
• Roghanian, E., Alipour, M., & Rezaei, M. (2018). An improved fuzzy critical chain approach in order to face uncertainty in project scheduling. International Journal of Construction Management, 18(1), 1-13. doi: 10.1080/15623599.2016.1225327
• Roychowdhury, S., & Wang, B.-H. (1996). Cooperative neighbors in defuzzification. Fuzzy Sets and Systems, 78(1), 37-49. doi: 10.1016/0165-0114(95)00077-1
• Saade, J. J. M., & Diab, H. B. (2004). Defuzzification methods and new techniques for fuzzy controllers. Iranian Journal of Electrical and Computer Engineering, 3(2), 161-174.
• Sebestyen, Z. (2017). Further considerations in project success. Procedia Engineering, 196, 571-577. doi: 10.1016/j.proeng.2017.08.032
• Serrador, P., & Turner, R. (2014). The relationship between project success and project efficiency. Procedia-Social and Behavioral Sciences, 119, 75-84. doi: 10.1016/j. sbspro.2014.03.011
• She, B., Chen, B., & Hall, N. G. (2021). Buffer sizing in critical chain project management by network decomposition. Omega, 102. doi: 10.1016/j.omega.2020.102382
• Shenhar, A. J., Levy, O., Dvir, D., & Maltz, A. C. (2001). Project success: a multidimensional strategic concept. Long Range Planning, 34(6), 699-725. doi: 10.1016/S0024-6301(01)00097-8
• Shi, Q., & Gong, T. (2010). An improved project buffer sizing approach to critical chain management under resources constraints and fuzzy uncertainty. Artificial Intelligence and Computational Intelligence, IEEE. AICI ‘09. International Conference on. doi: 10.1109/ AICI.2009.192
• Slusarczyk, A., Kuchta, D., Verhulst, P., Huyghe, W., Lauryssen, K., & Debal, T. (2013). A comparison of buffer sizing techniques in the critical chain method. Journal of Automation Mobile Robotics and Intelligent Systems, 7(3), 43-56.
• Spalek, S. (2014). Success factors in project management: Literature review. Proceedings of 8th International Technology Education and Development Conference INTED2014, Valencia, Spain (pp. 4828–4835).
• Taher, S. F., & El-Korany, T. M. (2016). Critical chain project management – a critique. 1st International Conference Sustainable Construction and Project Management, Egypt.
• Tenera, A. B. (2008). Critical chain buffer sizing: a comparative study. Paper presented at PMI® Research Conference: Defining the Future of Project Management, Warsaw: Newtown Square, PA: Project Management Institute.
• The Standish Group (2013). The Chaos Manifesto. Think Big. Act Small.
• Tukel, O. I., Rom, W. O., & Eksioglu, S. D. (2006). An investigation of buffer sizing techniques in criti cal chain scheduling. European Journal of Operational Research, 172(2), 401-416. doi: 10.1016/j. ejor.2004.10.019
• Turner, R., & Zolin, R. (2012). Forecasting success on large projects: Developing reliable scales to predict multiple perspectives by multiple stakeholders over multiple time frames. Project Management Journal, 43(5), 87-99. doi: 10.1002/pmj.21289
• Urbański, M., Haque, A., & Oino, I. (2019). The moderating role of risk management in project planning and project success: Evidence from construction businesses of Pakistan and the UK. Engineering Management in Production and Services, 11(1), 23-35. doi: 10.2478/emj-2019-0002
• Van de Vonder, S., Demeulemesser, E., Herroelen, W., & Leus, R. (2005). The use of buffers in project management: The trade-off between stability and makespan. International Journal of Production Economics, 97, 227-240. doi: 10.1016/j.ijpe.2004.08.004
• Wang, Y.-J. (2015). Ranking triangle and trapezoidal fuzzy numbers based on the relative preference relations. Applied Mathematical Modelling, 39(2), 586-599. doi: 10.1016/j.apm.2014.06.011
• Young, R., & Poon, S. (2013). Top management support— almost always necessary and sometimes sufficient for success: Findings from a fuzzy set analysis. International Journal of Project Management, 31(7), 943-957. doi: 10.1016/j.ijproman.2012.11.013
• Zarghami, S. A., Gunawan, I., Corral de Zubielqui, G., & Baroudi, B. (2020). Incorporation of resource reliability into critical chain project management buffer sizing. International Journal of Production Research, 58(20), 6130-6144. doi: 10.1080/00207543.2019.1667041
• Zhang, J., Song, X., & Díaz, E. (2014). Buffer sizing of critical chain based on attribute optimization. Concurrent Engineering, 22(3), 253-264. doi: 10.1177/1063293X14541286
• Zhang, J., Song, X., & Díaz, E. (2016). Project buffer sizing of a critical chain based on comprehensive resource tightness. European Journal of Operational Research, 248(1), 174-182. doi: 10.1016/j.ejor.2015.07.009
• Zohrehvandi, S., & Khalilzadeh, M. (2019). APRT-FMEA buffer sizing method in scheduling of a wind farm construction project. Engineering, Construction and Architectural Management, 26(6), 1129-1150. doi: 10.1108/ECAM-04-2018-0161
• Zohrehvandi, S., & Soltani, R. (2022). Project scheduling and buffer management: A comprehensive review and future directions. Journal of Project Management, 7(2), 121-132. doi: 10.5267/j.jpm.2021.9.002

Identyfikatory

DOI

10.2478/emj-2024-0023