دانشگاه سمنان

 آمار

تعداد نشریات21
تعداد شماره‌ها610
تعداد مقالات9,029
تعداد مشاهده مقاله67,082,929
تعداد دریافت فایل اصل مقاله7,656,387
khoshkhoy Nilash, Ehsan Allah, Esmaeilpour, Mansour, Bayat, Behrooz, Isfandyari Moghaddam, Alireza, Hassannayebi, Erfan. (1402). Discovery and Evaluation of Fixed Capital Facility Processes Based on Process Mining Approach: A Case Study of the Bank Loan Acceptance Process in Iran. نشریه هنرهای کاربردی, 3(4), 31-41. doi: 10.22075/mseee.2025.35345.1177
Ehsan Allah khoshkhoy Nilash; Mansour Esmaeilpour; Behrooz Bayat; Alireza Isfandyari Moghaddam; Erfan Hassannayebi. "Discovery and Evaluation of Fixed Capital Facility Processes Based on Process Mining Approach: A Case Study of the Bank Loan Acceptance Process in Iran". نشریه هنرهای کاربردی, 3, 4, 1402, 31-41. doi: 10.22075/mseee.2025.35345.1177
khoshkhoy Nilash, Ehsan Allah, Esmaeilpour, Mansour, Bayat, Behrooz, Isfandyari Moghaddam, Alireza, Hassannayebi, Erfan. (1402). 'Discovery and Evaluation of Fixed Capital Facility Processes Based on Process Mining Approach: A Case Study of the Bank Loan Acceptance Process in Iran', نشریه هنرهای کاربردی, 3(4), pp. 31-41. doi: 10.22075/mseee.2025.35345.1177
khoshkhoy Nilash, Ehsan Allah, Esmaeilpour, Mansour, Bayat, Behrooz, Isfandyari Moghaddam, Alireza, Hassannayebi, Erfan. Discovery and Evaluation of Fixed Capital Facility Processes Based on Process Mining Approach: A Case Study of the Bank Loan Acceptance Process in Iran. نشریه هنرهای کاربردی, 1402; 3(4): 31-41. doi: 10.22075/mseee.2025.35345.1177

Discovery and Evaluation of Fixed Capital Facility Processes Based on Process Mining Approach: A Case Study of the Bank Loan Acceptance Process in Iran

Modeling and Simulation in Electrical and Electronics Engineering
دوره 3، شماره 4 - شماره پیاپی 14، اردیبهشت 2024، صفحه 31-41    اصل مقاله (609.21 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/mseee.2025.35345.1177
نویسندگان
Ehsan Allah khoshkhoy Nilash1؛ Mansour Esmaeilpour* 2؛ Behrooz Bayat3؛ Alireza Isfandyari Moghaddam3؛ Erfan Hassannayebi4
1Department of Management, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
2Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
3Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
4Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
تاریخ دریافت: 27 شهریور 1403،  تاریخ بازنگری: 28 آبان 1403،  تاریخ پذیرش: 16 بهمن 1403 
چکیده
Fixed capital facility processes have many steps, control points, and approvals with long durations. In this regard, banks with more awareness and knowledge by analyzing and evaluating their processes can do better than their competitors in improving them and providing customer service. To tackle this challenge, process mining is one of the effective and efficient methods for analyzing processes, i.e., discovering and evaluating their quality. This paper aims to find and evaluate the model of the fixed capital facilities acceptance process based on the mentioned method. The proposed six-step method includes event logs preparation, process model discovery, evaluation and compliance checking, results analysis, analysis based on the fuzzy method, and comparison of results. The discovered process model is evaluated based on the quality dimensions of the process model, namely precision, fitness, simplicity, and generalization. Also, the results obtained from different methods are compared with each other. In addition to the discovery of the process model, one of the results was the heuristic algorithm having the best performance in terms of the mentioned criteria, with a value of 0.833. Particularly, it excelled in precision with a value of 0.656. The genetic algorithm, with a value of 0.946, exhibited the best fitness performance. Another result is the superior performance of the fuzzy technique compared to other methods. Furthermore, bottlenecks, activities with the highest repetition in a case, and branches and users with the most significant role in the process were identified.
کلیدواژه‌ها
Fixed capital facilities؛ discovery؛ process mining؛ fuzzy method؛ process model quality
مراجع
  1. Dakic, D., et al., BUSINESS PROCESS MINING APPLICATION: A LITERATURE REVIEW. Annals of DAAAM & Proceedings, 2018. 29.
  2. Khoshkhoy Nilash, E. A., Tamjid Yamechlo, A., & Rad, R. (2021). Performance Analysis and Improvement of Bank of Industry and Mine Working Capital Facility Processes Based on Process Mining Approach. Business Intelligence Management Studies, 9(36), 37-71. doi: 10.22054/ims.2021.58106.1896.
  3. Yazici, I.E. and O. Engin. Use of process mining in bank real estate transactions and visualization with fuzzy models. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019. 2020. Springer.
  4. Schoknecht, A., et al., Similarity of business process models—a state-of-the-art analysis. ACM Computing Surveys (CSUR), 2017. 50(4): p. 1-33.
  5. Kahloun, F., and S.A. Channouchi, Quality criteria and metrics for business process models in higher education domain: case of a tracking of curriculum offers process. Procedia Computer Science, 2016. 100: p. 1016-1023.
  6. Khlif, W., et al. Quality metrics for business process modeling. In Proceedings of the 9th WSEAS international conference on Applied computer science. 2009.
  7. Vanderfeesten, I., et al., Quality metrics for business process models. BPM and Workflow handbook, 2007. 144(2007): p. 179-190.
  8. Zellner, G., A structured evaluation of business process improvement approaches. Business Process Management Journal, 2011. 17(2): p. 203-237.
  9. Van Der Aalst, W. and W. van der Aalst, Data science in action. 2016: Springer.
  10. De Weerdt, J., et al., A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems, 2012. 37(7): p. 654-676.
  11. dos Santos Garcia, C., et al., Process mining techniques and applications–A systematic mapping study. Expert Systems with Applications, 2019. 133: p. 260-295.
  12. Van Dongen, B., J. Carmona, and T. Chatain, Alignment-based metrics in conformance checking (summary). Fachgruppentreffen der GI-Fachgruppe Entwicklungsmethoden für Informationssysteme und deren Anwendung, 2016: p. 87-90.
  13. Yegani, K. and M. Safari, a review of the concept of fixed investment, in the 7th Conference on Economic Studies and Management in the Islamic World. 2023
  14. Van der Aalst, W.M., Process discovery: Capturing the invisible. IEEE Computational Intelligence Magazine, 2010. 5(1): p. 28-41.
  15. Stefanini, A., et al., A process mining methodology for modeling unstructured processes. Knowledge and Process Management, 2020. 27(4): p. 294-310.
  16. Rozinat, A. and W.M. Van der Aalst, Conformance checking of processes based on monitoring real behavior. Information Systems, 2008. 33(1): p. 64-95.
  17. Buijs, J.C., B.F. van Dongen, and W.M. van der Aalst, Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity. International Journal of Cooperative Information Systems, 2014. 23(01): p. 1440001.
  18. Blum, F.R., Metrics in process discovery. Tech. Rep. Technical Report TR/DCC-2015–6, Computer Science Dept., University of Chile, 2015.
  19. Dila, R.A., M. Lubis, and L. Ramadani, Verification and validation of business processes in business architecture using the formal method with V-Model concept. Procedia Computer Science, 2024. 234: p. 718-724.
  20. Delias, P., N. Mittas, and G. Florou, A doubly robust approach for impact evaluation of interventions for business process improvement based on event logs. Decision Analytics Journal, 2023. 8: p. 100291.
  21. Kady, C., et al., Addressing Business Process Deviations through the Evaluation of Alternative Pattern-Based Models. Applied Sciences, 2023. 13(13): p. 7722.
  22. Gallego-Fontenla, V., J.C. Vidal, and M. Lama, Gradual Drift Detection in Process Models Using Conformance Metrics. arXiv preprint arXiv:2207.11007, 2022.
  23. Urrea-Contreras, S.J., et al., Applying Process Mining: The Reality of a Software Development SME. Applied Sciences, 2024. 14(4): p. 1402.
  24. EL KODSSI, I. and H. Sbai, Applying Process Mining to Generate Business Process Models from Smart Environments. International Journal of Computing and Digital Systems, 2024. 16(1): p. 705-717.
  25. Rashed, A.-H.M., et al., Analysis the patients’ careflows using process mining. Plos one, 2023. 18(2): p. e0281836.
  26. Erdogan, T.G. and A.K. Tarhan, Multi-perspective process mining for emergency process. Health Informatics Journal, 2022. 28(1): p. 14604582221077195.
  27. Reißner, D., A. Armas-Cervantes, and M. La Rosa, Generalization in automated process discovery: A framework based on event log patterns. arXiv preprint arXiv:2203.14079, 2022.
  28. Burke, A.T., et al., A chance for models to show their quality: Stochastic process model-log dimensions. Information Systems, 2024. 124: p. 102382.
  29. Sungkono, K.R., et al., Enhancing model quality and scalability for mining business processes with invisible tasks in non-free choice. Journal of King Saud University-Computer and Information Sciences, 2023. 35(9): p. 101741.
  30. Gerji et al., Reengineering organizational structure with process mining techniques; A case study in Mazandaran education. Journal of Process Engineering, 2021. 9(15): p. 1-18.
  31. Adriansyah, A., et al. Alignment based precision checking. In Business Process Management Workshops: BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers 10. 2013. Springer.
  32. Van Dongen, B., J. Carmona, and T. Chatain. Alignment-based Quality Metrics in Conformance Checking. in EMISA'16-7th International Workshop on Enterprise Modelling and Information Systems Architectures. 2016.
  33. Dharmawan, Y.S. and P. Amelia. MSMEs business process evaluation using business process management lifecycle approach in gresik. In 23rd Asian Forum of Business Education (AFBE 2019). 2020. Atlantis Press.
  34. Van Der Aalst, W.M. Process mining: discovering and improving Spaghetti and Lasagna processes. In the 2011 IEEE symposium on computational intelligence and data mining (CIDM). 2011. IEEE.
  35. Van der Aalst, W.M., A.A. De Medeiros, and A.J. Weijters. Genetic process mining. In Applications and Theory of Petri Nets 2005: 26th International Conference, ICATPN 2005, Miami, USA, June 20-25, 2005. Proceedings 26. 2005. Springer.
  36. Van Der Aalst, W.M. and B.F. Van Dongen. Discovering Petri nets from event logs. In Transactions on Petri nets and other models of concurrency vii. 2013. Springer.
  37. Weijters, A.J., W.M. van Der Aalst, and A.A. De Medeiros, Process mining with the HeuristicsMiner algorithm. 2006.
  38. Günther, C.W. and W.M. Van Der Aalst. Fuzzy mining–adaptive process simplification based on multi-perspective metrics. In International conference on business process management. 2007. Springer.
آمار
تعداد مشاهده مقاله: 43
تعداد دریافت فایل اصل مقاله: 51


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب