Vol. 2 No. 1 (2022): African Journal of Artificial Intelligence and Sustainable Development
Articles

A Data-Centric Approach to Business Process Optimization: Integrating AI with Process Mining for Performance Benchmarking

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  • Amish Doshi
Amish Doshi
Lead Consultant, Excelon Solutions, USA
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Published 07-11-2022

Keywords

  • artificial intelligence,
  • process mining
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Amish Doshi, “A Data-Centric Approach to Business Process Optimization: Integrating AI with Process Mining for Performance Benchmarking”, African J. of Artificial Int. and Sust. Dev., vol. 2, no. 1, pp. 143–153, Nov. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/200

Abstract

In the rapidly evolving landscape of business process optimization, the integration of artificial intelligence (AI) with process mining presents a transformative approach for achieving enhanced performance benchmarking and continuous process improvement. This paper explores the synergies between AI and process mining, emphasizing their collective potential in the creation of data-driven performance benchmarks for organizations across sectors such as finance and e-commerce. By leveraging AI models and algorithms, process mining techniques can uncover process inefficiencies, monitor key performance indicators (KPIs) in real-time, and facilitate data-driven decision-making. The application of AI enables the automatic identification of patterns, anomalies, and bottlenecks within complex business workflows, thus allowing organizations to optimize their operations dynamically. Furthermore, the use of AI in conjunction with process mining enhances predictive analytics, offering insights into future process behaviors and performance outcomes. The study highlights the implications of this integration for developing a robust framework for continuous process optimization, wherein AI-driven insights drive the ongoing refinement of business processes. This research contributes to the growing body of knowledge on the use of AI for process management, offering a comprehensive approach to performance benchmarking that ensures adaptability and sustained operational excellence.

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References

  1. A. van der Aalst, "Process mining: Data science in action," Springer, 2016.
  2. W. M. P. van der Aalst, "Process mining: Overview and opportunities," ACM Transactions on Management Information Systems, vol. 3, no. 2, pp. 1-17, 2012.
  3. X. Liu, P. Y. Yang, and Y. M. Zhu, "Integrating AI and process mining for business process optimization: A survey," Artificial Intelligence Review, vol. 57, pp. 219-241, 2020.
  4. M. J. Rojas, D. A. M. Lima, and T. A. Almeida, "AI-based process mining for performance benchmarking in manufacturing," Journal of Manufacturing Systems, vol. 53, pp. 96-112, 2019.
  5. A. Calders, "Mining process models using artificial intelligence techniques," IEEE Transactions on Automation Science and Engineering, vol. 9, no. 1, pp. 1-13, Jan. 2012.
  6. G. Zhan, Y. Yu, and L. Zhang, "Data-driven process mining: Current trends and future directions," International Journal of Advanced Computer Science and Applications, vol. 11, no. 1, pp. 29-37, 2020.
  7. A. B. Bandyopadhyay and S. K. Gupta, "The integration of process mining and machine learning for process improvement," Business Process Management Journal, vol. 25, no. 2, pp. 323-342, 2019.
  8. F. R. Saavedra and A. V. L. Zambon, "Application of AI and process mining to customer service process optimization in e-commerce," Computers in Industry, vol. 108, pp. 12-27, 2019.
  9. S. L. Lee, J. P. Yu, and S. C. Lee, "Exploring AI for business process optimization," IEEE Access, vol. 10, pp. 25836-25845, 2022.
  10. M. J. S. Kim and J. B. Lee, "Artificial intelligence in process mining for business process optimization in the finance sector," Journal of Financial Technology, vol. 1, no. 2, pp. 45-58, 2021.
  11. G. A. Nunes and M. K. T. Schuster, "AI and process mining integration for fraud detection in financial systems," Procedia Computer Science, vol. 172, pp. 124-131, 2020.
  12. F. S. Dijkstra, A. H. T. Smit, and H. M. S. Maarten, "Process mining and artificial intelligence in supply chain optimization," International Journal of Production Economics, vol. 210, pp. 147-158, 2020.
  13. M. G. Ross, J. B. Voigt, and M. K. Hausenblas, "Enhancing customer experience in e-commerce through AI and process mining," Computers in Human Behavior, vol. 123, no. 7, pp. 347-359, 2021.
  14. S. D. Stefanov, "AI-driven process mining for performance benchmarking in healthcare," Journal of Healthcare Engineering, vol. 7, no. 3, pp. 89-99, 2022.
  15. P. R. V. Vargas and F. N. Ruiz, "Integrating AI and process mining for predictive analytics in business performance," International Journal of AI & Decision Support, vol. 34, pp. 123-134, 2022.
  16. M. C. Brinton, D. V. Zhang, and M. A. Novoselov, "AI-enhanced process mining for agile process optimization," IEEE Transactions on Engineering Management, vol. 68, no. 5, pp. 432-444, 2021.
  17. V. O. Pereira, "Leveraging AI and process mining for performance benchmarking in retail operations," Journal of Retailing and Consumer Services, vol. 52, pp. 27-35, 2020.
  18. S. G. Yousaf, M. B. Ullah, and A. A. Mirza, "AI for process mining in e-commerce: A case study," Journal of Business Research, vol. 123, pp. 321-332, 2021.
  19. J. R. Li, R. K. Patel, and H. D. Rojas, "Challenges in AI-process mining integration for large-scale enterprises," Business Information Systems Engineering, vol. 64, no. 4, pp. 85-101, 2020.
  20. H. R. Kaczmarek and M. T. Herlihy, "AI-driven process mining for KPI benchmarking in financial sectors," Journal of Artificial Intelligence in Finance, vol. 3, pp. 245-258, 2022.

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