Statisztikai folyamatszabályozó-kártyák alkalmazása az üzleti folyamatok kontrollálásában
DOI:
https://doi.org/10.14267/VEZTUD.2025.09.03Kulcsszavak:
üzleti folyamatok, szabályozókártyák, eloszlásfüggetlenségAbsztrakt
A statisztikai folyamatszabályozás (Statistical Process Control, SPC) és az ellenőrző kártyák hatékony eszközök a gyártási folyamatok nyomon követésére és ellenőrzésére. Az SPC-t nemcsak a gyártási folyamatokban, hanem más területeken is alkalmazzák egy-egy művelet eredményének ellenőrzésére, például az egészségügyben, a pénzügyekben, a szoftverfejlesztésben vagy az oktatásban. Bár az üzleti folyamatok a gazdaság minden területén megjelennek, a szerzők tudomása szerint e folyamatok nyomon követésére és ellenőrzésére még nem alkalmaztak statisztikai folyamatszabályozási kártyákat. Ez a tanulmány megmutatja, hogy a termelésmenedzsmentben a műveletekre használt szabályozási logikát hogyan lehet átültetni a teljes üzleti folyamatok szabályozásának területére. A javasolt módszert egy valós vállalati példán mutatják be. A szabályozókártyák teljesítményét normalitás nem-teljesülése esetén is vizsgálják, valamint különböző eloszlás független szabályozókártya-statisztikákat hasonlítanak össze, hogy kiválasszák a legjobb szabályozó kártyákat. A gyakorlati vonatkozások tekintetében ajánlásokat fogalmaznak meg a döntéshozók számára a lehetséges szabályozókártyák kiválasztására vonatkozóan.
Downloads
Hivatkozások
Aagesen, G., & Krogstie, J. (2015). BPMN 2.0 for Modeling Business Processes. In J. vom Brocke & M. Rosemann (Eds.), Handbook on Business Process Management 1: Introduction, Methods, and Information Systems (pp. 219-250). Springer. https://doi.org/10.1007/978-3-642-45100-3_10
Abid, M., Nazir, H.Z., Riaz, M., & Lin, Z.Y. (2017). An efficient nonparametric EWMA Wilcoxon Signed-Rank chart for monitoring location. Quality and Reliability Engineering International, 33(3), 669-685. https://doi.org/10.1002/qre.2048
Ahad, N.A., Yin, T.S., Othman, A.R., & Yaacob, C.R. (2011). Sensitivity of normality tests to non-normal data. Sains Malaysiana, 40(6), 637-641. http://journalarticle.ukm.my/2511/1/15_NorAishah.pdf
Albers, W., Kallenberg, W.C., & Nurdiati, S. (2004). Parametric control charts. Journal of Statistical Planning and Inference, 124(1), 159–184. https://doi.org/10.1016/S0378-3758(03)00200-3
Amjad, A., Azam, F., Anwar, M.W., Butt, W.H., & Rashid, M. (2018). Event-driven process chain for modeling and verification of business requirements – a systematic literature review. Ieee Access, 6, 9027–9048. https://doi.org/10.1109/ACCESS.2018.2791666
Bakir, S.T. (2004). A distribution-free Shewhart quality control chart based on signed-ranks. Quality Engineering, 16(4), 613–623. https://doi.org/10.1081/QEN-120038022
Bencsik, A. (2021). Vezetői felkészültség felmérése a digitális kor kihívásaira – Nemzetközi összehasonlítás. Vezetéstudomány, 52(4), 93-108. https://doi.org/10.14267/VEZTUD.2021.04.08
Benedict, T., Lederer Antonucci, Y., Bariff, M., Champlin, B., Downing, B.D., Franzen, J., Madison, D.J., Lusk, S., Spanyi, A., & Treat, M. (2019). Guide to the Business Process Management Common Body Of Knowledge (BPM CBOK Version 4.0). ABPMP International.
Benneyan, J.C. (2008). The design, selection, and performance of statistical control charts for healthcare process improvement. International Journal of Six Sigma and Competitive Advantage, 4(3), 209–239. https://doi.org/10.1504/IJSSCA.2008.021837
Bi, H.H. (2022). Applying statistical process control to teaching quality assurance at higher education institutions. Quality Management Journal, 29(2), 145–157. https://doi.org/10.1080/10686967.2022.2035288
Caivano, D. (2005). Continuous software process improvement through statistical process control. In Ninth European Conference on Software Maintenance and Reengineering (pp. 288-293). IEEE. https://doi.org/10.1109/CSMR.2005.20
Capizzi, G., & Masarotto, G. (2018). Phase I distribution- free analysis with the R package dfphase1. In Frontiers in Statistical Quality Control 12 (pp. 3-19). Springer. https://doi.org/10.1007/978-3-319-75295-2_1
Castagliola, P., & Figueiredo, F.O. (2013). The median chart with estimated parameters. European Journal of Industrial Engineering, 7(5), 594–614. https://doi.org/10.1504/EJIE.2013.057382
Chakraborti, S., & Graham, M.A. (2019). Nonparametric (distribution-free) control charts: An updated overview and some results. Quality Engineering, 31(4), 523–544. https://doi.org/10.1080/08982112.2018.1549330
Chakraborti, S., Van der Laan, P., & Bakir, S. (2001). Nonparametric control charts: an overview and some results. Journal of Quality Technology, 33(3), 304–315. https://doi.org/10.1080/00224065.2001.11980081
Colin, J., & Vanhoucke, M. (2015). Developing a framework for statistical process control approaches in project management. International Journal of Project Management, 33(6), 1289–1300. https://doi.org/10.1016/j.ijproman.2015.03.014
Das, K.R., & Imon, A. (2016). A brief review of tests for normality. American Journal of Theoretical and Applied Statistics, 5(1), 5–12. https://doi.org/10.11648/j.ajtas.20160501.12
Davis, R.B., & Woodall, W.H. (1988). Performance of the control chart trend rule under linear shift. Journal of Quality Technology, 20(4), 260–262. https://doi.org/10.1080/00224065.1988.11979118
Goedhart, R., Schoonhoven, M., & Does, R.J. (2017). Guaranteed in-control performance for the Shewhart X and X control charts. Journal of Quality Technology, 49(2), 155–171. https://doi.org/10.1080/00224065.2018.1404876
Hajej, Z., Nyoungue, A.C., Abubakar, A.S., & Kammoun, M.A. (2021). An integrated model of production, maintenance, and quality control with statistical process control chart of a supply chain. Applied Sciences, 11(9), 4192. Available at: https://doi.org/10.3390/app11094192
Harris, K., Triantafyllopoulos, K., Stillman, E., & McLeay, T. (2016). A Multivariate Control Chart for Autocorrelated Tool Wear Processes. Quality and Reliability Engineering International, 32(6), 2093-2106. https://doi.org/10.1002/qre.2032
Hillier, F.S. (1967). Small sample probability limits for the range chart. Journal of the American Statistical Association, 62(320), 1488–1493. https://doi.org/10.2307/2283792
Jayathavaj, V., & Pongpullponsak, A. (2014). A simulation study on the performance of the sign test, Mann-Whitney test, Hodges-Lehmann estimator and control charts for Normal and Weibull data. International Journal of Industrial Engineering Computations, 5(4), 561–574. https://doi.org/10.5267/j.ijiec.2014.7.004
Jones-Farmer, L.A., Jordan, V., & Champ, C.W. (2009). Distribution- free phase I control charts for subgroup location. Journal of Quality Technology, 41(3), 304–316. https://doi.org/10.1080/00224065.2009.11917784
Kaplan, R.S., & Norton, D.P. (1996). Using the Balanced Scorecard as a Strategic Management System. Harvard Business Review, January – February, 75-85. http://jackson.com.np/home/documents/MBA4/Management_accounting/BSCHarvardBusinessReview.pdf
Kemény, S., Pusztai, É., Lakné Komka, K., Deák, A., Mihalovits, M., & Bodnár-Kemény, K. (2021). A 6 szigma statisztikai eszközei. Typotex. https://interkonyv.hu/konyvek/kemeny-sandor-a-6-szigma-statisztikai-eszkozei/
Kim, J., & Lim, C. (2021). Customer complaints monitoring with customer review data analytics: An integrated method of sentiment and statistical process control analyses. Advanced Engineering Informatics, 49, 101304. https://doi.org/10.1016/j.aei.2021.101304
Kim, S.H. (2015). A distribution-free CUSUM chart for monitoring variability of autocorrelated processes. Quality and Reliability Engineering International, 31(6), 923-934. Available at: https://doi.org/10.1002/qre.1649.
Klein, M. (2000). Two alternatives to the Shewhart X control chart. Journal of Quality Technology, 32(4), 427–431. https://doi.org/10.1080/00224065.2000.11980028
Kosztyán, Z.T., & Katona, A.I. (2016). Risk-based multivariate control chart. Expert Systems with Applications, 62, 250–262. https://doi.org/10.1016/j.eswa.2016.06.019
Kosztyán, Z.T., & Katona, A.I. (2018). Risk-Based X-bar chart with variable sample size and sampling interval. Computers & Industrial Engineering, 120, 308–319. https://doi.org/10.1016/j.cie.2018.04.052
Koutras, M., Bersimis, S., & Maravelakis, P. (2007). Statistical process control using Shewhart control charts with supplementary runs rules. Methodology and Computing in Applied Probability, 9, 207–224. https://doi.org/10.1007/s11009-007-9016-8
Kumar, S., Lim, W.M., Sureka, R., Jabbour, C.J.C., & Bamel, U. (2024). Balanced scorecard: trends, developments, and future directions. Review of Managerial Science, 18, 2397–2439. https://doi.org/10.1007/s11846-023-00700-6
Lasa, I.S., Laburu, C.O., & de Castro Vila, R. (2008). An evaluation of the value stream mapping tool. Business Process Management Journal, 14(1), 39–52. https://doi.org/10.1108/14637150810849391
Li, Q., & Chen, Y.L. (2009). Data flow diagram. In Modeling and Analysis of Enterprise and Information Systems (pp. 85-97). Springer. https://doi.org/10.1007/978-3-540-89556-5_4
Li, Y., Pei, D., & Wu, Z. (2020). A multivariate non-parametric control chart based on run test. Computers & Industrial Engineering, 149, 106839. https://doi.org/10.1016/j.cie.2020.106839
Mason, B., & Antony, J. (2000). Statistical process control: an essential ingredient for improving service and manufacuring quality. Managing Service Quality: An International Journal, 10(4), 233–238. https://doi.org/10.1108/09604520010341618
Montgomery, D.C. (2024). The 100th anniversary of the control chart. Journal of Quality Technology, 56(1), 2–4. https://doi.org/10.1080/00224065.2023.2282926
Mukherjee, A., & Chakraborti, S. (2012). A distribution- free control chart for the joint monitoring of location and scale. Quality and Reliability Engineering International, 28(3), 335–352. https://doi.org/10.1002/qre.1249
Mukherjee, A., & Sen, R. (2015). Comparisons of Shewhart- type rank based control charts for monitoring location parameters of univariate processes. International Journal of Production Research, 53(14), 4414– 4445. https://doi.org/10.1080/00207543.2015.1012605
Oakland, J., & Oakland, R.J. (2018). Statistical process control. Routledge. Object Management Group. (2011) Business Process Model and Notation (BPMN). Version 2.0. http://www.omg.org/spec/BPMN/2.0
Perdikis, T., Celano, G., & Chakraborti, S. (2024). Distribution- free control charts for monitoring scale in finite horizon productions. European Journal of Operational Research, 314(3), 1040–1051. https://doi.org/10.1016/j.ejor.2023.11.048
Reisinger, D., Reisinger, V., & Nagy, J. (2022). A mesterséges intelligencia és a digitalizáció hatása a logisztikai munkakörökre – Veszélyben vannak-e a munkahelyek? Vezetéstudomány, 53(8-9), 103-114. https://doi.org/10.14267/VEZTUD.2022.08-09.08
Scheer, A.W., Jost, W., Kindermann, H., & Kruppke, H. (Eds.) (2006). AGILITY by ARIS Business Process Management. Springer Verlag.
Schilling, E.G., & Nelson, P.R. (1976). The effect of non-normality on the control limits of X charts. Journal of Quality Technology, 8(4), 183–188. https://doi.org/10.1080/00224065.1976.11980743
Severin, T., & Schmid, W. (1998). Statistical process control and its application in finance. In Risk Measurement, Econometrics and Neural Networks: Selected Articles of the 6th Econometric-Workshop in Karlsruhe, Germany (pp. 83-104). Springer. https://doi.org/10.1007/978-3-642-58272-1_7
Siau, K., & Cao, Q. (2001). Unified modeling language: A complexity analysis. Journal of Database Management (JDM), 12(1), 26–34. https://doi.org/10.4018/jdm.2001010103
Song, S., Wang, Y., Wang, X., Lin, C., & Hu, K. (2024). A deep learning-based approach to similarity calculation for UML use case models. Expert Systems with Applications, 251, 123927. https://doi.org/10.1016/j.eswa.2024.123927
Thor, J., Lundberg, J., Ask, J., Olsson, J., Carli, C., Härenstam, K.P., & Brommels, M. (2007). Application of statistical process control in healthcare improvement: systematic review. BMJ Quality & Safety, 16(5), 387– 399. http://dx.doi.org/10.1136/qshc.2006.022194
Weiß, C.H., & Atzmüller, M. (2010). EWMA control charts for monitoring binary processes with applications to medical diagnosis data. Quality and Reliability Engineering International, 26(8), 795-805. https://doi.org/10.1002/qre.1098
Wu, C., Zhao, Y., & Wang, Z. (2002). The median absolute deviations and their applications to Shewhart control charts. Communications in Statistics-Simulation and Computation, 31(3), 425–442. https://doi.org/10.1081/SAC-120003850
Letöltések
Megjelent
Folyóirat szám
Rovat
License
Copyright (c) 2025 Corvinus University of Budapest, publisher of Vezetéstudomány / Budapest Management Review
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors assign copyright to Vezetéstudomány / Budapest Management Review. Authors are responsible for permission to reproduce copyright material from other sources.
Hogyan kell idézni
