Kök Neden Analizi için Süreç Madenciliği: Sistematik Literatür İncelemesi

Yıl 2025, Cilt: 15 Sayı: 3, 765 - 777, 01.09.2025

Tugba Gurgen Erdogan

https://doi.org/10.21597/jist.1594801

Öz

Mevcut süreç madenciliği tekniklerinin çoğu süreç keşfi, uygunluk kontrolü ve iş süreci performansına odaklanır. Ancak, süreç madenciliğinde performans ve uyumluluk sorunlarının kök nedenlerini analiz etme ve belirleme konusunda nispeten az araştırma yapılmıştır. Bu makalede, iş süreçlerinde kök neden analizine (RCA) süreç madenciliğini uygulamanın kullanımını, faydalarını ve zorluklarını deneysel bir bakış açısıyla incelemek için sistematik bir literatür incelemesi (SLR) sunulmaktadır. Bu çalışma, RCA'da süreç madenciliğinin en son ve pratik uygulamalarına genel bir bakış sunarak, bu alandaki zorlukları ve pratik sınırlamaları ele alma fırsatlarını vurgulamaktadır. Bulgular, yeni RCA tekniklerinin geliştirilmesini desteklemeyi ve çeşitli ihtiyaçlara göre uyarlanmış, özel özelliklere sahip ve uygun RCA yöntemlerinin seçimine rehberlik etmeyi amaçlamaktadır.

Anahtar Kelimeler

Süreç Madenciliği , Kök Neden Analizi , Sistematik Literatür Tarama

Process Mining for Root Cause Analysis: A Systematic Literature Review

Öz

Process mining techniques mostly focus on process discovery, conformance checking, and business process performance. However, relatively little research has been done on analyzing and identifying the root causes of compliance and performance issues in process mining. In this paper, we present a systematic literature review (SLR) that explores the use, stages, approaches, challenges of applying process mining to root cause analysis (RCA) in business processes from an empirical perspective. This SLR offers an overview of the state-of-the-art and applications of process mining in RCA, highlighting opportunities to address challenges and practical limitations in this domain. The findings aim to support the implement of novel RCA techniques and guide the selection of appropriate RCA methods with specific features tailored to various needs.

Anahtar Kelimeler

Process Mining , Root Cause Analysis , Systematic Literature Review

Kaynakça

• Adams, J. N., van Zelst, S. J., Quack, L., Hausmann, K., van der Aalst, W. M., & Rose, T. (2021). A framework for explainable concept drift detection in process mining. International Conference on Business Process Management, 400–416.
• Akhramovich, K., Serral, E., & Cetina, C. (2024). A systematic literature review on the application of process mining to industry 4.0. Knowledge and Information Systems, 66 (5), 2699–2746.
• Andersen, B., & Fagerhaug, T. (2006). Root cause analysis: Simplified tools and techniques. Quality Press.
• Bautista, A. D., Wangikar, L., & Akbar, S. M. K. (2012). Process mining-driven optimization of a consumer loan approvals process. BPI Challenge.
• Berti, A., Schuster, D., & van der Aalst, W. M. (2023). Abstractions, scenarios, and prompt definitions for process mining with llms: A case study. International Conference on Business Process Management, 427–439.
• Bozorgi, Z. D., Teinemaa, I., Dumas, M., La Rosa, M., & Polyvyanyy, A. (2020). Process mining meets causal machine learning: Discovering causal rules from event logs. 2020 2nd International Conference on Process Mining (ICPM), 129–136.
• Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and Regression Trees. CRC Press.
• Buijs, J. (2014). Receipt phase of an environmental permit application process (‘wabo’), coselog project. Eindhoven University of Technology.
• Cai, Z., Li, W., Zhu, W., Liu, L., & Yang, B. (2019). A real-time trace-level root- cause diagnosis system in alibaba datacenters. IEEE Access, 7, 142692–142702.
• Chiang, L. H., & Braatz, R. D. (2003). Process monitoring using causal map and multivariate statistics: Fault detection and identification. Chemometrics and intelligent laboratory systems, 65 (2), 159–178.
• Corallo, A., Lazoi, M., & Striani, F. (2020). Process mining and industrial applications: A systematic literature review. Knowledge and Process Management, 27 (3), 225–233.
• De Leoni, M., van der Aalst, W. M., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235–257.
• Dogan, O., & Areta Hiziroglu, O. (2024). Empowering manufacturing environments with process mining-based statistical process control. Machines, 12 (6), 411.
• dos Santos Garcia, C., Meincheim, A., Junior, E. R. F., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., Santos, E. A. P., & Scalabrin, E. E. (2019). Process mining techniques and applications–a systematic mapping study. Expert Systems with Applications, 133, 260–295.
• Erdogan, T. G., & Tarhan, A. (2018). Systematic mapping of process mining studies in healthcare. IEEE Access, 6, 24543–24567.
• Erdogan, T. G., & Tarhan, A. K. (2022). Multi-perspective process mining for emergency process. Health Informatics Journal, 28 (1), 14604582221077195.
• Ferreira, D. R., & Vasilyev, E. (2015). Using logical decision trees to discover the cause of process delays from event logs. Computers in Industry, 70, 194–207.
• Ghazal, M. A., Ibrahim, O., & Salama, M. A. (2017). Educational process mining: A systematic literature review. 2017 European Conference on Electrical Engineering and Computer Science (EECS), 198–203.
• Goel, K., Leemans, S., Wynn, M. T., ter Hofstede, A., & Barnes, J. (2021). Improving phd student journeys with process mining: Insights from a higher education institution. Proceedings of the Industry Forum (BPMIF 2021) co-located with 19th International Conference on Business Pro-cess Management (BPM 2021), 39–49.
• Gunther, C. W., & Van Der Aalst, W. M. (2007). Fuzzy mining–adaptive process simplification based on multi-perspective metrics. International conference on business process management, 328–343.
• Hompes, B. F., Buijs, J. C., & van der Aalst, W. M. (2016). A generic framework for context-aware process performance analysis. OTM Confederated International Conferences” On the Move to Meaningful Internet Systems”, 300–317.
• Hompes, B. F., Maaradji, A., La Rosa, M., Dumas, M., Buijs, J. C., & van der Aalst, W. M. (2017). Discovering causal factors explaining business process performance variation. International Conference on Advanced Information Systems Engineering, 177–192.
• Jagadeesh Chandra Bose, R., Gupta, A., Chander, D., Ramanath, A., & Dasgupta, K. (2015). Opportunities for process improvement: A cross-clientele analysis of event data using process mining. International Conference on Service-Oriented Computing, 444–460.
• Khakpour, R., Ebrahimi, A., & Seyed-Hosseini, S.-M. (2024). Lean process mining: Adopting process mining in lean manufacturing for dynamic process mapping and avoiding waste occurrence in real time. International Journal of Lean Six Sigma.
• Kitchenham, B., Brereton, O. P., 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.
• Knoll, D., Reinhart, G., & Pr¨uglmeier, M. (2019). Enabling value stream mapping for internal logistics using multidimensional process mining. Expert Systems with Applications, 124, 130–142.
• Leemans, S. J., Fahland, D., & Van Der Aalst, W. M. (2014). Process and deviation exploration with inductive visual miner. BPM (demos), 1295 (8).
• Leemans, S. J., & Tax, N. (2022). Causal reasoning over control-flow decisions in process models. International Conference on Advanced Information Systems Engineering, 183–200.
• Lehto, T., Hinkka, M., Hollm´en, J., et al. (2017). Focusing business process lead time improvements using influence analysis. SIMPDA, 54–67.
• Li, C.-Y., Shinde, T., He, W., Lau, S. S. F., Hiew, M. X. B., Tam, N. T., Joshi, A., & van der Aalst, W. M. (2023). Unveiling bottlenecks in logistics: A case study on process mining for root cause identification and diagnostics in an air cargo terminal. International Conference on Service-Oriented Computing, 291–307.
• Mahendrawathi, E., Zayin, S. O., & Pamungkas, F. J. (2017). Erp post implementation review with process mining: A case of procurement process. Procedia Computer Science, 124, 216–223.
• Mans, R. S., Schonenberg, M., Song, M., van der Aalst, W. M., & Bakker, P. J. (2008). Application of process mining in healthcare–a case study in a dutch hospital. International joint conference on biomedical engineering systems and technologies, 425–438.
• Nadim, K., Ragab, A., & Ouali, M.-S. (2022). Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining. Journal of Intelligent Manufacturing, 1–27.
• Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.
• Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008). Systematic mapping studies in software engineering. 12th International Conference on Evaluation and Assessment in Software Engineering (EASE) 12, 1–10.
• Qafari, M. S., & Aalst, W. v. d. (2019). Fairness-aware process mining. OTM Confederated International Conferences” On the Move to Meaningful Internet Systems”, 182–192.
• Qafari, M. S., & van der Aalst, W. (2020). Root cause analysis in process mining using structural equation models. International Conference on Business Process Management, 155–167.
• Qafari, M. S., & van der Aalst, W. (2021). Feature recommendation for structural equation model discovery in process mining. arXiv preprint arXiv:2108.07795.
• Qafari, M. S., & van der Aalst, W. M. (2021). Case level counterfactual reasoning in process mining. International Conference on Advanced Information Systems Engineering, 55–63.
• Ratzer, A. V., Wells, L., Lassen, H. M., Laursen, M., Qvortrup, J. F., Stissing, M. S., Westergaard, M., Christensen, S., & Jensen, K. (2003). Cpn tools for editing, simulating, and analysing coloured petri nets. International conference on application and theory of petri nets, 450–462.
• Senderovich, A., Weidlich, M., Yedidsion, L., Gal, A., Mandelbaum, A., Kadish, S., & Bunnell, C. A. (2016). Conformance checking and performance improvement in scheduled processes: A queueing-network perspective. Information Systems, 62, 185–206.
• Southier, L. F. P., Krugger, E. M. R., Garcia, C. D. S., & Scalabrin, E. E. (2023). Process mining and root cause analysis for detecting inefficiencies in business processes: An applied case in a brazilian telecommunications provider. 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 53–58.
• Stefanini, A., Aloini, D., Benevento, E., Dulmin, R., & Mininno, V. (2018). Performance analysis in emergency departments: A data-driven approach. Measuring Business Excellence.
• Suriadi, S., Ouyang, C., van der Aalst, W. M., & ter Hofstede, A. H. (2012). Root cause analysis with enriched process logs. International Conference on Business Process Management, 174–186.
• Suriadi, S., Ouyang, C., van der Aalst, W. M., & ter Hofstede, A. H. (2013). Root cause analysis with enriched process logs. Business Process Management Workshops: BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers 10, 174–186.
• Suriadi, S., Wynn, M. T., Ouyang, C., ter Hofstede, A. H., & van Dijk, N. J. (2013). Understanding process behaviours in a large insurance company in australia: A case study. Advanced Information Systems Engineering: 25th International Conference, CAiSE 2013, Valencia, Spain, June 17-21, 2013. Proceedings 25, 449–464.
• Van Der Aalst, W. M., & Dustdar, S. (2012). Process mining put into context. IEEE Internet Computing, 16 (1), 82–86. van der Aalst, W. M. (2019). Object-centric process mining: Dealing with divergence and convergence in event data. International Conference on Software Engineering and Formal Methods, 3–25.
• van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action. Springer.
• Van der Aalst, W. M. (2022). Process mining: A 360 degree overview. In Process mining handbook (pp. 3–34). Springer.
• Van Houdt, G., Depaire, B., & Martin, N. (2022). Root cause analysis in process mining with probabilistic temporal logic. International Conference on Process Mining, 73–84.
• Van Houdt, G., Martin, N., & Depaire, B. (2023). Aitia-pm: Discovering the true causes of events in a process mining context. Engineering Applications of Artificial Intelligence, 126, 107145.
• Van Zelst, S. J., Mannhardt, F., de Leoni, M., & Koschmider, A. (2021). Event abstraction in process mining: Literature review and taxonomy. Granular Computing, 6, 719–736.
• Vasilyev, E., Ferreira, D. R., & Iijima, J. (2013). Using inductive reasoning to find the cause of process delays. 2013 IEEE 15th Conference on Business Informatics, 242–249.
• Verboven, S., & Martin, N. (2022). Combining the clinical and operational perspectives in heterogeneous treatment effect inference in healthcare processes. International Conference on Process Mining, 327–339.
• Weijters, A., & Ribeiro, J. T. S. (2011). Flexible heuristics miner (fhm). 2011 IEEE symposium on computational intelligence and data mining (CIDM), 310–317.
• Wieringa, R., Maiden, N., Mead, N., & Rolland, C. (2006). Requirements engineering paper classification and evaluation criteria: A proposal and a discussion. Requirements engineering, 11 (1), 102–107.
• Xu, X., Zhu, L., Weber, I., Bass, L., & Sun, D. (2014). Pod-diagnosis: Error diagnosis of sporadic operations on cloud applications. 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 252–263.