Analyzing Process Execution Time for Evidence-Based Policy Making in Information Systems using Process Mining

Mohammadi, Mohsen

doi:10.22034/jipm.2024.711526

Analyzing Process Execution Time for Evidence-Based Policy Making in Information Systems using Process Mining

نوع مقاله : مقاله پژوهشی

نویسنده

Mohsen Mohammadi

Esfarayen University of Technology; Esfarayen, Iran

10.22034/jipm.2024.711526

چکیده

Enterprises employ information systems to carry out their day-to-day business operations. Organizations implement business policies to enhance their competitive edge through efficient process management. This paper aims to propose a method that combines two approaches: evidence-based policymaking and process mining, to facilitate process reengineering. While numerous evidence-based approaches utilizing process mining techniques have been employed to assess process performance through measurements, these methods often focus on individual process instances. This is in contrast to Business Process Redesign (BPR) assessments, which encompass more comprehensive performance measurements, including overall process performance. This study proposes a method for analyzing process execution time, which includes Cycle time, Lead time, and Activity time. The aim is to support evidence-based policymaking in information systems through the use of process mining. Several key performance indicators (KPIs) have been defined for evidence-based management of business processes to identify process bottlenecks. The results of this paper demonstrate the application of process mining in analyzing the execution time of business processes. Using a real-world dataset, the study identified time-consuming activities and provided key performance indicators (KPIs) to guide process optimization. These findings demonstrate the effectiveness of process mining in identifying bottlenecks and inefficiencies within operational processes, ultimately leading to improved process performance and efficiency.

کلیدواژه‌ها

evidence-based approach

process mining

process performance

Key Performance Indicators

execution time

موضوعات

فناوری و اطلاعات

عنوان مقاله English

Analyzing Process Execution Time for Evidence-Based Policy Making in Information Systems using Process Mining

نویسنده English

Mohsen Mohammadi

Esfarayen University of Technology; Esfarayen, Iran

چکیده English

Enterprises employ information systems to execute their day-to-day business operations. To enhance their competitive edge through efficient process management, organizations implement business policies. This paper aims to propose a method that combines two approaches: evidence-based policymaking and process mining, to facilitate process reengineering. Many evidence-based approaches based on process mining techniques have been conducted to evaluate process performance through measurements. However, these approaches mainly concentrate on individual process instances, whereas assessments of Business Process Redesign (BPR) primarily employ comprehensive performance measurements, such as overall process performance. This study suggests a method for analyzing process execution time, including Cycle time, Lead time, and Activity time, for evidence-based policymaking in information systems using process mining. Several Key Performance Indicators (KPIs) have been defined for the evidence-based management of business processes to pinpoint process bottlenecks. The results demonstrate that evidence from process mining can be utilized to validate the effectiveness of existing policies.

کلیدواژه‌ها English

evidence-based approach

process mining

process performance

Key Performance Indicators

execution time

References

Cho, M., Song, M., Yoo, S., & Reijers, H. A. (2019). An evidence-based decision support framework for clinician medical scheduling. IEEE Access, 7, 15239-15249.

Cho, M., Song, M., Comuzzi, M., & Yoo, S. (2017). Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques. Decision Support Systems, 104, 92-103.

del-Río-Ortega, A., Resinas, M., Cabanillas, C., & Ruiz-Cortés, A. (2013). On the definition and design-time analysis of process performance indicators. Information Systems, 38(4), 470-490.

De Lima, E. P., da Costa, S. E. G., Angelis, J. J., & Munik, J. (2013). Performance measurement systems: A consensual analysis of their roles. International Journal of Production Economics, 146(2), 524-542.

https://data.4tu.nl/articles/dataset/BPI_Challenge_2017/12696884

Delias, P., & Nguyen, G. T. (2021). Prototyping a business process improvement plan. An evidence-based approach. Information Systems, 101, 101812.

Li, Chiao-Yun, Sebastiaan J. van Zelst, and Wil MP van der Aalst. "A generic approach for process performance analysis using bipartite graph matching." In Business Process Management Workshops: BPM 2019 International Workshops, Vienna, Austria, September 1–6, 2019, Revised Selected Papers 17, pp. 199-211. Springer International Publishing, 2019.

Li, C. Y., van Zelst, S. J., & van der Aalst, W. M. (2019). A generic approach for process performance analysis using bipartite graph matching. In Business Process Management Workshops: BPM 2019 International Workshops, Vienna, Austria, September 1–6, 2019, Revised Selected Papers 17 (pp. 199-211). Springer International Publishing.

Kueng, P. (2000). Process performance measurement system: a tool to support process-based organizations. Total quality management, 11(1), 67-85..

Kueng, P., & Krahn, A. J. (1999). Building a process performance measurement system: some early experiences.

Mohammadi, M., & Mukhtar, M. B. (2012). Service process modeling for demand-driven supply chain based on SOA. International Journal of Digital Content Technology and its Applications, 6(22), 21.

Mohammadi, M. (2017). Combination of modeling techniques for business process modeling. Int. J. Adv. Sci. Eng. Inf. Technol., 7(3), 1038-1048.

Peltier, T. R. (2004). Information security policies and procedures: a practitioner's reference. CRC Press.

PM4PY, https://pm4py.fit.fraunhofer.de

Popova, V., & Sharpanskykh, A. (2010). Modeling organizational performance indicators. Information systems, 35(4), 505-527.

Schuh, G., Guetzlaff, A., Schmitz, S., Schopen, M., & Obladen, A. (2022, December). Performance-based Decision Support for Business Process Analysis and Design. In 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1376-1380). IEEE.

Strecker, S., Frank, U., Heise, D., & Kattenstroth, H. (2012). MetricM: a modeling method in support of the reflective design and use of performance measurement systems. Information Systems and e-Business Management, 10, 241-276.

Van der Aalst, W. M. (2013). Business process management: a comprehensive survey. International Scholarly Research Notices, 2013.

Van der Aalst W. (2016) Data science in action. In: van der Aalst W, editor. Process mining: Data science in action. Berlin, Heidelberg: Springer Berlin Heidelberg. p. 3-23

Van Der Aalst, Wil MP, Hajo A. Reijers, Anton JMM Weijters, Boudewijn F. van Dongen, AK Alves De Medeiros, Minseok Song, and H. M. W. Verbeek. (2007) "Business process mining: An industrial application." Information systems 32, no. 5: 713-732.

Wang, H. J., Zhao, J. L., & Zhang, L. J. (2009). Policy-Driven Process Mapping (PDPM): Discovering process models from business policies. Decision Support Systems, 48(1), 267-281.

Wetzstein, B., Ma, Z., & Leymann, F. (2008). Towards measuring key performance indicators of semantic business processes. In Business Information Systems: 11th International Conference, BIS 2008, Innsbruck, Austria, May 5-7, 2008. Proceedings 11 (pp. 227-238). Springer Berlin Heidelberg.