Received 29.06.2022, Revised 15.09.2022, Accepted 10.10.2022
The article summarizes the issue of increasing the efficiency, speed and correctness of the enterprise's business processes. The main purpose of the article was to combine the possibility of increasing the efficiency of the enterprise's business processes with the use of artificial intelligence, its methods and tools. New approaches to forecasting the business processes of the enterprise (Predictive Process Monitoring (PPM)), which enables the analysis of the event and the current execution of the algorithm, with the aim of predicting the future behavior of the programmed business process algorithm, are considered. PPM provides the possibility of advanced intelligent analysis of business processes with forecasting functionality . A thorough analysis of literary sources confirmed the relevance of the study and revealed a focus on the use of artificial intelligence in business process management. The widespread use of information technologies in enterprises will allow to move to a higher level of development and accelerate the recovery of the country's micro- and macroeconomic environment. The process of mergers and acquisitions of enterprises will characteristic of reconstruction after the end of the war, that is why this methodology was proposed by the authors. The methodological tools of the study were the following methods: scientific abstraction, analysis and synthesis, structural-logical method for the formation tools for improving the company's business processes, monitoring them for the purpose of adapting and correcting the production process. Business processes of the enterprise were chosen as the object of research, and artificial intelligence and its methods were chosen as the subject. An interdisciplinary approach made it possible to offer artificial intelligence solutions to the enterprise's business processes. Machine learning has been identified as a tool for increasing operational efficiency through the implementation of automated solutions, creating cognitive business technology frameworks that actually think like humans, which will positively affect productivity and reduce human labor. It has been proven that predictive modeling will improve the financial results of enterprises and increase income. The results of the study can be used by Ukrainian enterprises to form a strategic goal of their development
business process; enterprise; artificial intelligence; forecasting; intellectual analysis; development
[1] Amouzgar, F., Beheshti, A., Ghodratnama, S., Benatallah, B., Yang, J., & Sheng, Q.Z. (2018). Isheets: A spreadsheet-based machine learning development platform for data-driven process analytics. In X. Liu et al. (Eds.), Service-oriented computing – ICSOC 2018 workshops. ICSOC 2018. Lecture notes in computer science, 11434. Cham: Springer. doi: 10.1007/978-3-030-17642-6_43.
[2] Arias, M., Saavedra, R., Marques, M., Munoz-Gama, J., & Sepálveda, M. (2018). Human resource allocation in business process management and process mining. A systematic mapping study. Management Decision, 56(2), 376-405. doi: 10.1108/MD-05-2017-0476.
[3] Arias, M., Munoz-Gama, J., & Sepúlveda, M. (2017). Towards a taxonomy of human resource allocation criteria. In E. Teniente & M. Weidlich, M. (Eds.), Business process management workshops. BPM 2017. Lecture notes in business information processing, 308. Cham: Springer. doi: 10.1007/978-3-319-74030-0_37.
[4] Beheshti, A., Benatallah B., & Motahari-Nezhad, H. (2018a). Processatlas: A scalable and extensible platform for business process analytics. Software, Practice & Expierence, 48(4), 842-866. doi: 10.1002/spe.2558.
[5] Beheshti, A., Schiliro, F., Ghodratnama, S., Amouzgar, F., Benatallah, B., & Yang, J. (2018b). Iprocess: Enabling iot platforms in data-driven knowledge-intensive processes. In M. Weske, M. Montali, I. Weber & J. vom Brocke. (Eds.), Business process management workshops. BPM 2018. Lecture notes in business information processing, 329, 109-126. Cham: Springer. doi: 10.1007/978-3-319-98651-7_7.
[6] Beheshti, S., Benatallah, B., Nezhad, H., & Sakr, S. (2011). A query language for analyzing business processes execution. In S. Rinderle-Ma, F. Toumani & K. Wolf (Eds.), Business process management. BPM 2011. Lecture notes in computer science, 6896. Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-23059-2_22.
[7] Beheshti, S., Benatallah, B., Sakr, S., Grigori, D., Motahari-Nezhad, H., Barukh, M., Gater, A., & Ryu, S. (2016). Process analytics: Concepts and techniques for querying and analyzing process data. Cham: Springer.
[8] Brunk, J., Stottmeister, J., Weinzierl, S., Matzner, M., & Becker, J. (2020). Exploring the effect of context information on deep learning business process predictions. Journal of Decision Systems, 29(1), 328-343. doi: 10.1080/12460125.2020.1790183.
[9] Buijs, J., Van Dongen, B., & Van der Aalst, W. (2012). Towards cross-organizational process mining in collections of process modelsand their executions. In F. Daniel, K. Barkaoui & Dustdar, S. (Eds.), Business process management workshops. BPM 2011. Lecture notes in business information processing, 100. Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-28115-0_2.
[10] Gralla, P. (2007). How the internet works. (8th ed.). Indianapolis: Que Publishing.
[11] Hansen, L., & Nissenbaum, H. (2009). Digital disaster, cyber security, and the copenhagen school. International Studies Quarterly, 53(4), 1155-1175.
[12] Harrington, H.J. (1991a). Business process improvement - the breakthrough strategy for total quality, productivity, and competitiveness. Journal of Public Health Management and Practice JPHMP, 16(2), 104-109. doi: 10.1097/PHH.0b013e3181c65534.
[13] Harris, J.G., & Davenport, T.H. (2017). Competing on analytics, updated, with a new introduction: The new science of winning. Harvard Business School Press Books.
[14] Lupton, D., & Michael, M. (2015). Toward a manifesto for the public understanding of Big Data. Public Understanding of Science, 25, 1-13.
[15] Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A revolution that will transform how we live, work and think. Boston. New York: An Eamon Dolan B ook.
[16] Pika, A., Leyer, M., Wyn, M., Fidge, H.A., & Aalst, W. (2017). Mining resource profiles from event logs. ACM Transactions on Management Information Systems (TMIS), 8(1), 1-30. doi: 10.1145/3041218.
[17] Pika, A.,& Wynn, M. (2021). Machine learning based approach for recommending unfamiliar process activities. IEEEAcces, 9, 104969-104979. doi: 10.1109/ACCESS.2021.3096513.
[18] Polpinij, J., Ghose, A., & Dam, H. (2010). Business rules discoveryfrom process design repositories. IEEE Congress on Services, 614-620. doi: 10.1109/SERVICES.2010.73.
[19] Schiliro, F., Beheshti, A., Ghodratnama, S., Amouzgar, F., Benatallah, B., & Yang, J. (2018). Icop: Lot-enabled policing processes. In X. Liu et al. (Ed.), Service-oriented computing – ICSOC 2018 workshops. ICSOC 2018. Lecture notes in computer science, 11434. Cham: Springer. doi: 10.1007/978-3-030-17642-6_42.
[20] Sindhgatta, R., Ghose, A., & Dam, H. (2016). Context-aware recommendation of task allocations in service systems. In Q. Sheng, E. Stroulia, S. Tata & S. Bhiri (Eds.), Service-oriented computing – ICSOC 2016 workshops. ICSOC 2016. Lecture notes in computer science, 9936. Cham: Springer. doi: 10.1007/978-3-319-46295-0_25.
[21] Van Der Aalst, W. (2016). Data science in action in process mining. Berlin, Heidelberg: Springer.
[22] Van Der Aalst, W., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 60(4), 269-272. doi: 10.1007/s12599-018-0542-4.
[23] Wolf, F., Brunk, J., & Becker, J.(2021). A framework of business process monitoring and prediction techniques. In F. Ahlemann, R. Schütte & S. Stieglitz (Eds.), Innovation through information systems. WI 2021. Lecture notes in information systems and organisation, 47. Cham: Springer. doi: 10.1007/978-3-030-86797-3_47.
[24] Zhao, X. (2011). A business process driven approach for automatic gen-eration of business applications. (PhD thesis, Queen’s University, Canada).
[25] Zhao, X., Zou, Y., Hawkins, J., & Madapusi, B. (2007). A business-process-driven approach for generating e-commerce user interfaces. In G. Engels, B. Opdyke, D.C. Schmidt & F. Weil (Eds.), Model driven engineering languages and systems. MODELS 2007. Lecture notes in computer science, 4735. Berlin, Heidelberg: Springer. doi: 10.1007/978-3-540-75209-7_18.
[26] Frolova, L.V., & Goloborodko, A.Yu. (2013). Mergers and acquisitions of enterprises. Kyiv: Kondor.
