MESTO I ZNAČAJ PROCESNOG RUDARENJA U UPRAVLJANJU POSLOVNIM PROCESIMA – PREGLED LITERATURE

Univerzitet u Istočnom Sarajevu, Fakultet poslovne ekonomije Bijeljina, Republika Srpska, BiH
Bosna i Hercegovina


Apstrakt

Procesna orijentacija postaje sve zastupljenija u savremenim tržišnim uslovima bez obzira na veličinu poslovnog subjekta. Njena prednost se ogleda kroz direktnu odgovornost i jasno merljive rezultate procesa, što omogućava praćenje i kontrolu poslovanja. Kako bi se osigurala efikasnost navedene orijentacije, dolazi do razvoja upravljanja poslovnim procesima koje podrazumeva njihovo identifikovanje, realizaciju, analizu i konstantno unapređenje. Navedena disciplina, pored menadžmenta, postaje predmet interesovanja i nauke o informacionim tehnologijama. Zahvaljujući njoj, u vidu alata za podršku, razvija se tehnika procesnog rudarenja zasnovana na izdvajanju upotrebljivih informacija iz zapisa događaja. Cilj rada je putem pregleda literature prikazati mesto i značaj procesnog rudarenja u upravljanju poslovnim procesima. Opisani su rezultati dobijeni analizom i sintezom pojedinačnih radova koji ukazuju na mesto procesnog rudarenja u životnom ciklusu upravljanja poslovnim procesima, kao i prednosti koje se stiču njegovom primenom.

Ključne reči



Cijeli članak:


Reference


Adriansyah, A., Van Dongen, B. F., & Van Der Aalst, W. M. P. (2011). Conformance checking using cost-based fitness analysis. Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOC, 55–64.

Agostinelli, S., Maggi, F. M., Marrella, A., & Milani, F. (2019). A user evaluation of process discovery algorithms in a software engineering company. Proceedings - 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference, EDOC 2019, 142–150.

Aguirre, S., Parra, C., & Alvarado, J. (2013). Combination of Process Mining and Simulation Techniques for Business Process Redesign: A Methodological Approach.

Becker, M., & Buchkremer, R. (2019). A practical process mining approach for compliance management. Journal of Financial Regulation and Compliance,

Bolt, A., de Leoni, M., van der Aalst, W. M. P., & Gorissen, P. (2017). Business process reporting using process mining, analytic workflows and process cubes: A case study in education. Lecture Notes in Business Information Processing, 28–53.

Buijs, J. C. A. M., Rosa, M. La, Reijers, H. A., Dongen, B. F. Van, & Aalst, W. M. P. Van Der. (2013). Improving Business Process Models Using Observed Behavior.

Camargo, M., Dumas, M., & González-Rojas, O. (2020). Automated discovery of business process simulation models from event logs. Decision Support Systems

Chernia, J., Martinho, R., & Ghannouchi, S. A. (2019). Towards Improving Business Processes based on preconfigured KPI target values, Process Mining and Redesign Patterns. Procedia Computer Science, 164, 279–284.

Dixit, P. M. (2018). ProDiGy : Human-in-the-loop Process Discovery. i.

Djedović, A., Žunić, E., & Karabegović, A. (2017). A combined process mining for improving business process. Proceedings of International Conference on Smart Systems and Technologies 2017, SST 2017, 2017-Decem(October), 143–148.

Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2012). Fundamentals of Business Process Management. Springer Science.

Dybå, T., & Dingsøyr, T. (2008). Empirical studies of agile software development: A systematic review. In Information and Software Technology (Vol. 50).

Erdogan, T. G., & Tarhan, A. (2018). A goal-driven evaluation method based on process mining for healthcare processes. Applied Sciences (Switzerland), 8(6).

Ferreira, D. R., Szimanski, F., & Ralha, C. G. (2014). Improving process models by mining mappings of low-level events to high-level activities. Journal of Intelligent Information Systems, 43(2), 379–407.

Firouzian, I., Zahedi, M., & Hassanpour, H. (2019). Investigation of the effect of concept drift on data-aware remaining time prediction of business processes. International Journal of Nonlinear Analysis and Applications, 10(2), 153–166.

Galdo Seara, L., & Medeiros de Carvalho, R. (2019). An Approach for Workflow Improvement based on Outcome and Time Remaining Prediction. Modelsward,

Greyling, B. T., & Jooste, W. (2017). The application of business process mining to improving a physical asset management process: A case study. South African Journal of Industrial Engineering, 28(2), 120–132.

Hompes, B. F. A., Buijs, J. C. A. M., & van der Aalst, W. M. P. (2016). A generic framework for context-aware process performance analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10033 LNCS, 300–317.

Huang, Z., Lu, X., & Duan, H. (2012). Resource behavior measure and application in business process management. Expert Systems with Applications, 6458–6468.

Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering - A systematic literature review. Information and Software Technology, 51(1), 7–15.

Lamghari, Z., Radgui, M., Saidi, R., & Rahmani, M. D. (2018). A set of indicators for BPM life cycle improvement. 2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018, 2018-May(December 2019), 1–8.

Li, C., Ge, J., Huang, L., Hu, H., Wu, B., Hu, H., & Luo, B. (2017). Software cybernetics in BPM: Modeling software behavior as feedback for evolution by a novel discovery method based on augmented event logs. Journal of Systems and Software, 124, 260–273.

Liu, J., Liu, P., Liu, S., Ma, Y., & Yang, W. (2013). Handover optimization in business processes via prediction. Kybernetes, 42(7), 1101–1127.

Liu, T., Cheng, Y., & Ni, Z. (2012). Mining event logs to support workflow resource allocation. Knowledge-Based Systems, 35, 320–331.

Liu, Y., Zhang, H., Li, C., & Jiao, R. J. (2012). Workflow simulation for operational decision support using event graph through process mining. Decision Support Systems, 52(3), 685–697.

Mahendrawathi, E. R., Astuti, H. M., & Nastiti, A. (2015). Analysis of Customer Fulfilment with Process Mining: A Case Study in a Telecommunication Company. Procedia Computer Science, 72, 588–596.

Márquez-Chamorro, A. E., Resinas, M., Ruiz-Cortés, A., & Toro, M. (2017). Run-time prediction of business process indicators using evolutionary decision rules. Expert Systems with Applications, 87, 1–14.

Marrella, A. (2019). Automated Planning for Business Process Management. Journal on Data Semantics, 8(2), 79–98.

McCormack, K., & Johnson, B. (2001). Business process orientation, supply chain management, and the e-corporation. IIE Solutions, 33(10), 33.

Park, G., & Song, M. (2020). Predicting performances in business processes using deep neural networks. Decision Support Systems, 129(November 2019), 113191.

R’bigui, H., & Cho, C. (2017). Customer Oder Fulfillment Process Analysis with Process Mining: An Industrial Application in a Heavy Manufacturing Company. ACM International Conference Proceeding Series, 247–252.

Radosavljević, M. (2016). Upravljanje poslovnim procesima primenom modela zrelosti.

Saraeian, S., & Shirazi, B. (2020). Process mining-based anomaly detection of additive manufacturing process activities using a game theory modeling approach. Computers and Industrial Engineering, 146(January), 106584.

Savickas, T., & Vasilecas, O. (2018). Belief network discovery from event logs for business process analysis. Computers in Industry, 100(April), 258–266.

Sebu, M. L., & Ciocarlie, H. (2015). Business activity monitoring solution to detect deviations in business process execution. SACI 2015 - 10th Jubilee IEEE International Symposium on Applied Computational Intelligence and Informatics.

Suriadi, S., Wynn, M. T., Xu, J., van der Aalst, W. M. P., & ter Hofstede, A. H. M. (2017). Discovering work prioritisation patterns from event logs. Decision Support Systems, 100, 77–92.

Tax, N., Sidorova, N., & van der Aalst, W. M. P. (2019). Discovering more precise process models from event logs by filtering out chaotic activities. Journal of Intelligent Information Systems, 52(1), 107–139.

Thabet, D., Ghannouchi, S. A., & Ben Ghezala, H. H. (2015). Business process model extension with cost perspective based on process mining - Cost data description and analysis. Proceedings of the 26th International Business Information Management Association Conference - Innovation Management and Sustainable Economic Competitive Advantage: From Regional Development to Global Growth, IBIMA 2015, October 2018, 44–58.

Thabet, D., Ghannouchi, S. A., & Ghezala, H. H. Ben. (2016). Towards a general solution for business process model extension with cost perspective based on Process Mining. Proceedings of the 28th International Business Information Management Association Conference, October 2018, 208–220.

Van der Aalst, W. (2016). Process mining: Data science in action. In Process Mining: Data Science in Action.

van Eck, M., Lu, X., Leemans, S., & van der Aalst, W. (2015). PM2: A Process Mining Project Methodology. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9097, 520–521.

Vázquez-Barreiros, B., van Zelst, S., Buijs, J. C. A. M., & Lama, M. (2016). Repairing Alignments: Striking the Right Nerve. Lecture Notes in Business Information Processing, 248(June), V–VI.

Weske, M. (2012). Business Process Management - Concepts, Languages, Architectures. Springer Science.

Wu, Q., He, Z., Wang, H., Wen, L., & Yu, T. (2019). A business process analysis methodology based on process mining for complaint handling service processes. Applied Sciences (Switzerland), 9(16).

Zheng, W., Du, Y., Wang, S., & Qi, L. (2019). Repair Process Models Containing Non-Free-Choice Structures Based on Logic Petri Nets. IEEE Access.