Redesigning Business Processes: A Method Based on Simulation and Process Mining Techniques
Corporations have always prioritized efforts to examine and improve processes. Various metrics, such as the cost and time required to implement the process and can be specified in this regard. Process improvement can be defined as an improvement of these indicators. This is accomplished by looking at prospective adjustments to the current executive process model or the resources allotted to it. Research has been conducted in this paper to the improve the procurement process and aims to explore assessment prospects in the project using a combination of process mining and simulation (benefiting from Play-In and Play-Out methodologies). To run the simulation, we will need to complete the control flow diagram, institution settings, resource settings, and activity settings. The process of mining event logs yields the process control flow. However, both the entry of institutions and the distribution of resources must be modeled. The rate of admission of institutions and the distribution of time for the implementation of activities will be determined in the next step.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 333
 J. F. Chang, “Business Process Management Systems: Strategy and Implementation,” Bus. Process Manag. Syst., Apr. 2016, doi: 10.1201/9781420031362.
 R. K. L. Ko, S. S. G. Lee, and E. W. Lee, “Business process management (BPM) standards: a survey,” Bus. Process Manag. J., vol. 15, no. 5, pp. 744–791, Sep. 2009, doi: 10.1108/14637150910987937.
 M. Dumas, M. La Rosa, J. Mendling, and H. A. Reijers, Fundamentals of business process management: Second Edition. Springer Berlin Heidelberg, 2018.
 W. Van der Aalst, “Process mining: Data science in action,” Process Min. Data Sci. Action, pp. 1–467, Jan. 2016, doi: 10.1007/978-3-662-49851-4.
 W. van der Aalst and W. van der Aalst, “Data Science in Action,” in Process Mining, Springer Berlin Heidelberg, 2016, pp. 3–23.
 A. Rozinat, R. S. Mans, M. Song, and W. M. P. van der Aalst, “Discovering colored Petri nets from event logs,” Int. J. Softw. Tools Technol. Transf., vol. 10, no. 1, pp. 57–74, Jan. 2008, doi: 10.1007/S10009-007-0051-0.
 W. M. P. van der Aalst, “Object-Centric Process Mining: Dealing with Divergence and Convergence in Event Data,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Sep. 2019, vol. 11724 LNCS, pp. 3–25, doi: 10.1007/978-3-030-30446-1_1.
 C. dos S. Garcia et al., “Process mining techniques and applications – A systematic mapping study,” Expert Systems with Applications, vol. 133. Elsevier Ltd, pp. 260–295, Nov. 01, 2019, doi: 10.1016/j.eswa.2019.05.003.
 S. J. van Zelst, F. Mannhardt, M. de Leoni, and A. Koschmider, “Event abstraction in process mining: literature review and taxonomy,” Granul. Comput. 2020 63, vol. 6, no. 3, pp. 719–736, May 2020, doi: 10.1007/S41066-020-00226-2.
 A. Corallo, M. Lazoi, and F. Striani, “Process mining and industrial applications: A systematic literature review,” Knowl. Process Manag., vol. 27, no. 3, pp. 225–233, Jul. 2020, doi: 10.1002/KPM.1630.
 X. Yi-wu, L. Xiao-wan, and C. Yan, “The Research on the Usage of Business Process Mining in the Implementation of BPR,” in Network and Parallel Computing Workshops, IFIP International Conference on, Apr. 2008, pp. 995–1000, doi: 10.1109/npc.2007.124.
 L. Mǎruşter and N. R. T. P. Van Beest, “Redesigning business processes: A methodology based on simulation and process mining techniques,” Knowl. Inf. Syst., vol. 21, no. 3, pp. 267–297, Dec. 2009, doi: 10.1007/s10115-009-0224-0.
 T. Becker, M. Lütjen, and R. Porzel, “Process Maintenance of Heterogeneous Logistic Systems—A Process Mining Approach,” Springer, Cham, 2017, pp. 77–86.
 A. Sahraeidolatkhaneh and K. H. Han, “Integrated Framework of Process Mining and Simulation Approaches for the Efficient Diagnosis and Design of Business Process,” J. Korea Contents Assoc., vol. 17, no. 5, pp. 221–233, 2017.
 M. Cho, M. Song, M. Comuzzi, and S. Yoo, “Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques,” Decis. Support Syst., vol. 104, pp. 92–103, Dec. 2017, doi: 10.1016/j.dss.2017.10.004.
 E. Ruschel, E. A. P. Santos, and E. de F. R. Loures, “Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing,” J. Intell. Manuf., vol. 31, no. 1, pp. 53–72, Jan. 2020, doi: 10.1007/S10845-018-1434-7/EMAIL/CORRESPONDENT/C1/NEW.
 “Process mining for self-regulated learning assessment in e-learning | SpringerLink.” https://link.springer.com/article/10.1007/s12528-019-09225-y (accessed Sep. 14, 2021).
 J. Eggers, A. Hein, M. Böhm, and H. Krcmar, “No Longer Out of Sight, No Longer Out of Mind? How Organizations Engage with Process Mining-Induced Transparency to Achieve Increased Process Awareness,” Bus. Inf. Syst. Eng. 2021, pp. 1–20, Sep. 2021, doi: 10.1007/S12599-021-00715-X.
 J. Kamburowski, “Normally Distributed Activity Durations in PERT Networks,” J. Oper. Res. Soc., vol. 36, no. 11, p. 1051, Nov. 1985, doi: 10.2307/2582437.
 Mamoudan, M. M., Mohammad Nazari, Z., Ostadi, A., & Esfahbodi, A. (2022). Food products pricing theory with application of machine learning and game theory approach. International Journal of Production Research, 1-21.