Abstract
In today’s competitive landscape, optimizing business processes is crucial for maintaining efficiency and customer satisfaction, particularly in the manufacturing sector. This paper presents SentiProMo, a self-developed tool that integrates sentiment analysis of collaborative comments with summarization features to enhance the design phase of business processes. Leveraging the capabilities of large language models (LLMs) such as Chat-GPT, this tool empowers managers with insightful reports for informed decision-making. To demonstrate the effectiveness of SentiProMo, a case study was conducted focusing on the check-in process at airports, a critical aspect of the airline industry. Real-world data from collaborative comments during the design phase of the check-in process were analyzed using sentiment analysis techniques. Additionally, summarization features were employed to generate concise and informative reports for management stakeholders. The results of the case study showcase the potential of LLM-powered tools like SentiProMo in streamlining business processes. By harnessing sentiment analysis, organizations can gain valuable insights into employee perceptions and identify areas for improvement. Moreover, the summarization capabilities facilitate the efficient communication of findings to management, enabling them to make informed decisions promptly. This research not only underscores the power of LLMs in enhancing business process design but also highlights the promising avenues for future applications. With its wide array of potential applications across various industries, SentiProMo represents a significant advancement in process optimization and management.
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References
Allweyer, T.: BPMN 2.0: Introduction to the Standard for Business Process Modeling. Books on Demand (2009)
Chen, H., Zimbra, D.: AI and opinion mining. IEEE Intell. Syst. 25(3), 74–76 (2010). https://doi.org/10.1109/MIS.2010.75
Choudhury, P., Sharma, K., Verma, A., Singh, V., Bhatia, P.: Utilizing natural language processing for business process improvement: a systematic literature review. J. Bus. Res. 122, 877–888 (2020)
Davenport, T.H., Short, J.E.: The new industrial engineering: information technology and business process redesign. Sloan Manage. Rev. 31(4), 11–27 (1990). http://sloanreview.mit.edu/smr/issue/1990/summer/1/
Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56509-4
Hagenau, C., Helmes, K., Liebmann, M.: Automated business process improvement: a comprehensive survey. Expert Syst. Appl. 147, 113191 (2020)
Hrastnik, J., Cardoso, J., Kappe, F.: The business process knowledge framework, pp. 517–520, January 2007
Hrastnik, J., Cardoso, J.S., Kappe, F.: The business process knowledge framework. In: International Conference on Enterprise Information Systems (2007). https://api.semanticscholar.org/CorpusID:26394879
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012). https://doi.org/10.2200/s00416ed1v01y201204hlt016. http://dx.doi.org/10.2200/S00416ED1V01Y201204HLT016
Lüftenegger, E., Softic, S.: SentiProMo: a sentiment analysis-enabled social business process modeling tool. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 83–89. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66498-5_7
Muehlen, M., Recker, J.: How much language is enough? Theoretical and practical use of the business process modeling notation. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 465–479. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69534-9_35
Radford, A., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901. Curran Associates, Inc. (2020)
Recker, J., Indulska, M., Rosemann, M., Green, P.: How good is BPMN really? Insights from theory and practice. In: Proceedings of the 14th European Conference on Information Systems, ECIS 2006 (2006)
Schmidt, R., Nurcan, S.: BPM and social software. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 649–658. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_65
Softic, S., Lüftenegger, E.: Towards empowering business process redesign with sentiment analysis. In: Yang, X.-S., Sherratt, S., Dey, N., Joshi, A. (eds.) Proceedings of Sixth International Congress on Information and Communication Technology. LNNS, vol. 236, pp. 119–126. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2380-6_10
Softic, S., Lüftenegger, E., Turcin, I.: Tracking and analyzing processes in smart production. In: Al-Turjman, F. (ed.) Trends in Cloud-based IoT. EICC, pp. 37–50. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40037-8_3
Wynn, D.C., Clarkson, P.J.: Process models in design and development. Res. Eng. Des. 29(2), 161–202 (2018). https://doi.org/10.1007/s00163-017-0262-7
Zor, S., Schumm, D., Leymann, F.: A proposal of BPMN extensions for the manufacturing domain. In: Proceedings of the 44th CIRP International Conference on Manufacturing Systems (2011)
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Softic, S., Lüftenegger, E., Resanovic, D., Softic, S., Popan, A. (2024). Leveraging Sentiment Analysis and Reporting for Re-designing Business Processes Using Large Language Models: A SentiProMo Case Study in Airline Check-In Processes. In: Thürer, M., Riedel, R., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. APMS 2024. IFIP Advances in Information and Communication Technology, vol 731. Springer, Cham. https://doi.org/10.1007/978-3-031-71633-1_1
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