Digital Transformation in Behshahr Industrial Development Holding Corporation: Leveraging the BPLLM Framework for Process Optimization and Business Growth

Authors

  • Mohammad Bagherian * Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran.
  • Behnam Bagherian Department of Railway Transportation Engineering, Iran University of Science and Technology, Tehran, Iran.
  • Nasim Nahavandi Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran. https://orcid.org/0000-0002-1445-6557

https://doi.org/10.48314/apem.v2i2.32

Abstract

Process-aware Decision Support Systems (DSSs) have been traditionally enhanced by incorporating Artificial Intelligence (AI) functionalities to facilitate accelerated and informed decision-making. To advance its digital transformation and capitalize on AI, Behshahr Industrial Development Holding Corp. can tap into the innovative Business Process Large Language Model (BPLLM) framework to optimize business decisions and processes. The framework boosts flexibility, autonomy, and accuracy in supporting process-aware decision-making by blending advanced Natural Language Processing (NLP) and Business Process Management (BPM) capabilities. Key characteristics of BPLLM include process-specific informational Retrieval-Augmented Generation (RAG), fine-tuning models to meet organizational needs, and process-aware chunking. Evaluating this framework in various scenarios has revealed the high ability of BPLLM to identify activities and sequence flows and offer precise and relevant answers. By leveraging this framework, Behshahr Industrial Development Holding Corp can enhance supply chain management, production efficiency, and market and customer analysis, facilitating sustainable and intelligent growth.

Keywords:

Business process large language model framework, Large language model, Business process management, Decision support system, Digital trans formation

References

  1. [1] Dumas, M., Fournier, F., Limonad, L., Marrella, A., Montali, M., Rehse, J. R., … ., & Weber, I. (2023). AI-augmented business process management systems: A research manifesto. Association for computing machinery transactions on management information systems, 14(1), 1–19. https://doi.org/10.1145/3576047

  2. [2] Agarwal, P., Gao, B., Huo, S., Reddy, P., Dechu, S., Obeidi, Y., … ., & Carbajales, S. (2022). A process-aware decision support system for business processes. Proceedings of the 28th ACM sigkdd conference on knowledge discovery and data mining (pp. 2673–2681). Association for computing machinery. https://doi.org/10.1145/3534678.3539088

  3. [3] Estrada-Torres, B., Del-Río-Ortega, A., & Resinas, M. (2024). Mapping the landscape: Exploring large language model applications in business process management. Enterprise, business-process and information systems modeling (pp. 22–31). Cham: Springer, Cham. https://doi.org/10.1007/978-3-031-61007-3_3

  4. [4] Fahland, D., Fournier, F., Limonad, L., Skarbovsky, I., & Swevels, A. J. E. (2024). How well can large language models explain business processes? https://doi.org/10.48550/arXiv.2401.12846

  5. [5] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. A., Lacroix, T., … ., & Lample, G. (2023). LLaMA: Open and efficient foundation language models. https://doi.org/10.48550/ARXIV.2302.13971

  6. [6] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., … ., & Scialom, T. (2023). Llama 2: Open foundation and fine-tuned chat models. https://doi.org/10.48550/arXiv.2307.09288

  7. [7] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., … ., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in neural information processing systems, 2020-Decem, 9459–9474. http://dx.doi.org/10.48550/arXiv.2005.11401

  8. [8] Dumas, M., La Rosa, M., Mendling, J., Reijers, H. A. (2018). Fundamentals of business process management. Springer. https://doi.org/10.1007/978-3-662-56509-4

  9. [9] Lohrmann, M., & Reichert, M. (2016). Effective application of process improvement patterns to business processes. Software & systems modeling, 15(2), 353–375. https://doi.org/10.1007/s10270-014-0443-z

  10. [10] Agostinelli, S., De Luzi, F., Canito, U., Ferraro, J., Marrella, A., & Mecella, M. (2022). A data-centric approach to design resilient-aware process models in BPMN. Business process management forum (pp. 38–54). Springer, Cham. http://dx.doi.org/10.1007/978-3-031-16171-1_3

Published

2025-05-07

How to Cite

Bagherian, M. ., Bagherian, B. ., & Nahavandi, N. . (2025). Digital Transformation in Behshahr Industrial Development Holding Corporation: Leveraging the BPLLM Framework for Process Optimization and Business Growth. Annals of Process Engineering and Management, 2(2), 91-100. https://doi.org/10.48314/apem.v2i2.32