Uloga EFQM modela i poslovnih modela u unapređenju performansi kvaliteta: pristup zasnovan na mašinskom učenju
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
http://orcid.org/0000-0001-5563-8982
Aleksandar Đorđević
http://orcid.org/0000-0003-2856-6578
Aleksandar Aleksić
http://orcid.org/0000-0002-7990-9123
Marija Savković
http://orcid.org/0000-0002-3620-7762
Nikola Komatina
http://orcid.org/0000-0001-6964-5673
Apstrakt
EFQM model ima ključnu ulogu u praćenju performansi malih i srednjih preduzeća (MSP), pružajući sveobuhvatan okvir za procenu organizacione izvrsnosti. Podjednako važnu ulogu imaju i poslovni modeli, koji oblikuju strateški pravac i operativnu efikasnost ovih preduzeća. Ova studija ispituje uticaj usklađivanja EFQM modela sa poslovnim modelima na performanse kvaliteta u malim i srednjim preduzećima (MSP) u Srbiji. Studija slučaja je izvršena na realnim podacima iz 20 preduzeća koja su aplicirala za Oskar Kvaliteta, a za analizu složenih međuzavisnosti između poslovne izvrsnosti, inovacija poslovnih modela i performansi kvaliteta primenjene su veštačke neuronske mreže (VNM). Rezultati pokazuju da povećanje integracije EFQM modela sa poslovnim modelom od 5% značajno poboljšava ključne pokazatelje performansi kvaliteta. Uzimajući u obzir neizvesnost poslovanja i ograničene resurse preduzeća iz sektora MSP poboljšanja stagniraju nakon povećanja usaglašenosti od 20%, što sugeriše optimalni prag za stratešku implementaciju. Ovi rezultati naglašavaju vrednost strukturiranih okvira za poslovnu izvrsnost u poboljšanju operativnih performansi, dok istovremeno pokazuju efikasnost primene mašinskog učenja u optimizaciji procesa donošenja odluka. Studija pruža korisne uvide za MSP koji žele da poboljšaju kvalitet performansi kroz ciljano usklađivanje EFQM modela i sopstvenog poslovnog modela.
##plugins.themes.bootstrap3.article.details##
Reference
[2] Para-González L, Jiménez-Jiménez D, Martínez-Lorente Á-R. Does EFQM enhance learn-ing and innovation? Total Qual Manag Bus Excell 2022;33:727–51. https://doi.org/10.1080/14783363.2021.1890016.
[3] Petrović T, Paunović V, Komatina N. EFQM and business model relation effect on perfor-mance of manufacturing enterprises. Int Rev 2023:39–47. https://doi.org/10.5937/intrev2302050P.
[4] Mitchell TM. Artificial neural networks. Mach Learn 1997;45:127.
[5] Liu Y-L, Ko P. F. A modified EFQM Excellence Model for effective evaluation in the hotel industry. Total Qual Manag Bus Excell 2018;29:1580–93. https://doi.org/10.1080/14783363.2017.1279011.
[6] Aydin S, Kahraman C, Kaya İ. A new fuzzy multicriteria decision making approach: An application for European Quality Award assessment. Knowl-Based Syst 2012;32:37–46. https://doi.org/10.1016/j.knosys.2011.08.022.
[7] Nestić S, Aleksić A, Lafuente JG, Cvetić T, Đurić G. The Allocation of Business Model Components under Presence of Uncertainties by the Branch-and-Bound Method. Math Probl Eng 2022;2022:1–12. https://doi.org/10.1155/2022/2958519.
[8] Walczak S, editor. Encyclopedia of Information Science and Technology, Fourth Edition: IGI Global; 2018. https://doi.org/10.4018/978-1-5225-2255-3.
[9] Dongare AD, Kharde RR, Kachare AD. Introduction to artificial neural network. Int J Eng Innov Technol IJEIT 2012;2:189–94.
[10] Abiodun OI, Kiru MU, Jantan A, Omolara AE, Dada KV, Umar AM, et al. Comprehen-sive Review of Artificial Neural Network Applications to Pattern Recognition. IEEE Access 2019;7:158820–46. https://doi.org/10.1109/ACCESS.2019.2945545.
[11] Sharma V, Rai S, Dev A. A comprehensive study of artificial neural networks 2013;2:278–84.
[12] Tkáč M, Verner R. Artificial neural networks in business: Two decades of research. Appl Soft Comput 2016;38:788–804. https://doi.org/10.1016/j.asoc.2015.09.040.
[13] Nenadál J. The New EFQM Model: What is Really New and Could Be Considered as a Suit-able Tool with Respect to Quality 4.0 Concept? Qual Innov Prosper 2020;24:17–28. https://doi.org /10.12776/qip.v24i1.1415.
[14] Ghasemi A, Asgharizadeh E. Presenting a hybrid ANN-MADM method to define excellence level of Iranian petrochemical companies. J Inf Technol Manag 2014;6. https://doi.org/10.22059 /jitm.2014.50864.
[15] Mishra N, Rane SB. Business excellence initiative success prediction model based on lo-gistic regression and artificial neural network. Int J Qual Innov 2019;4:132. https://doi.org/10.1504/IJQI.2019.105749.
[16] Milošević I, Ruso J, Glogovac M, Arsić S, Rakić A. An integrated SEM-ANN approach for predicting QMS achievements in Industry 4.0. Total Qual Manag Bus Excell 2022;33:1896–912. https://doi.org/10.1080/14783363.2021.2011194.
[17] Petrović T. Uticaj usaglašenosti modela kvaliteta i poslovnog modela na performanse malih i srednjih preduzeća. Doctoral dissertation. University of Kragujevac, Faculty of Engineering, 2023.
[18] Nestić S, Gojković R, Petrović T, Tadić D, Mimović P. Quality Performance Indicators Evaluation and Ranking by Using TOPSIS with the Interval-Intuitionistic Fuzzy Sets in Pro-ject-Oriented Manufacturing Companies. Mathematics 2022;10:4174. https://doi.org/10.3390/math10224174.