Mitigating operational risks in critical infrastructure through integrated ERP-BPMS: a multi-case study

Authors

DOI:

https://doi.org/10.15587/2706-5448.2025.330660

Keywords:

ERP, BPMS, integration, risk, management, infrastructure, data, governance, case, study

Abstract

Operational risks in critical infrastructure sectors, from housing services and specialized construction to water technology and energy utilities, have significant socioeconomic implications. This study investigates how an integrated enterprise resource planning-business process management system (ERP-BPMS) supported by a dedicated information administrator (IA) can systematically mitigate these risks. Using a quasi-experimental, multi-case design, four anonymized organizations contributed baseline data (3–9 months) and post-implementation data (6–8 months). Six indicators were tracked: integral qualification score (IQS), cost prediction accuracy (CPA), system stability index (SSI), preventive maintenance ratio (PMR), sourcing score, and information utilization rate (IUR).

Results reveal that IQS increased substantially in all cases (e. g., from 9.5 to 42.1), while CPA values commonly exceeded 0.85. Preventive maintenance ratios increased by 1520 percentage points, indicating a notable shift from reactive to proactive strategies. In the energy utility case, the SSI improved from 1.04 to 1.31, showing enhanced service reliability. The IA’s oversight proved instrumental in ensuring consistent data governance, standardizing metrics, and streamlining cross-departmental coordination. These improvements translated into measurable resource savings that significantly outweighed the costs of maintaining the IA role. Cross-case analysis suggests that a staged implementation, beginning with pilot phases for core modules, can reveal data inconsistencies early and inform tailored training programs. Managers in sectors where cost accuracy and project timelines are critical may benefit substantially from such phased rollout. Collectively, these findings highlight that a unified ERP-BPMS platform reinforced by structured human governance can significantly bolster risk management in mission-critical contexts. This research contributes to both information systems and project management fields by offering a tested framework for enhancing resilience and operational stability in high-stakes environments.

Author Biographies

Yuri Chernenko, Higher Education Institution "International University of Business and Law"

PhD

 

Dmytro Bedrii, State Enterprise "Ukrainian Scientific Research Institute of Radio and Television"

Doctor of Technical Sciences, Associate Professor, Senior Researcher, Acting Director

Oksana Haidaienko, Admiral Makarov National University of Shipbuilding

PhD, Associate Professor

Department of Information Management Systems and Technologies

Oleh Meliksetov, Admiral Makarov National University of Shipbuilding

PhD Student

Department of Information Management Systems and Technologies

References

  1. Gorenstein, A., Kalech, M. (2022). Predictive maintenance for critical infrastructure. Expert Systems with Applications, 210, 118413. https://doi.org/10.1016/j.eswa.2022.118413
  2. Pestana, G., Sofou, S. (2024). Data Governance to Counter Hybrid Threats against Critical Infrastructures. Smart Cities, 7 (4), 1857–1877. https://doi.org/10.3390/smartcities7040072
  3. Chernenko, Y., Danchenko, O., Melenchuk, V. (2022). Conceptual model of risk management in development projects of providers of housing and utility services. Management of Development of Complex Systems, 51, 41–48. https://doi.org/10.32347/2412-9933.2022.51.41-48
  4. Maglaras, L., Kantzavelou, I., Ferrag, M. A. (2021). Digital Transformation and Cybersecurity of Critical Infrastructures. Applied Sciences, 11 (18), 8357. https://doi.org/10.3390/app11188357
  5. Moussa, A., Ezzeldin, M., El-Dakhakhni, W. (2024). Predicting and managing risk interactions and systemic risks in infrastructure projects using machine learning. Automation in Construction, 168, 105836. https://doi.org/10.1016/j.autcon.2024.105836
  6. Bernardo, B. M. V., Mamede, H. S., Barroso, J. M. P., dos Santos, V. M. P. D. (2024). Data governance & quality management – Innovation and breakthroughs across different fields. Journal of Innovation & Knowledge, 9 (4), 100598. https://doi.org/10.1016/j.jik.2024.100598
  7. Rahi, K., Bourgault, M., Preece, C. (2022). Risk and vulnerability management, project agility and resilience: a comparative analysis. International Journal of Information Systems and Project Management, 9 (4), 5–21. https://doi.org/10.12821/ijispm090401
  8. Varajão, J., Trigo, A., Pereira, J. L., Moura, I. (2022). Information systems project management success. International Journal of Information Systems and Project Management, 9 (4), 62–74. https://doi.org/10.12821/ijispm090404
  9. Loggen, T., Ravesteyn, P. (2023). How does BPM maturity affect process performance? Communications of the IIMA. https://doi.org/10.58729/1941-6687.1437
  10. Gavali, A., Halder, S. (2019). Identifying critical success factors of ERP in the construction industry. Asian Journal of Civil Engineering, 21 (2), 311–329. https://doi.org/10.1007/s42107-019-00192-4
  11. Hewavitharana, T., Nanayakkara, S., Perera, A., Perera, J. (2019). Impact of enterprise resource planning (ERP) systems to the construction industry. International Journal of Research in Electronics and Computer Engineering, 7 (2), 887–893. Available at: https://figshare.com/articles/journal_contribution/Impact_of_Enterprise_Resource_Planning_ERP_Systems_to_the_Construction_Industry/8868392?file=16257764
  12. Jayamaha, B. H. V. H., Perera, B. A. K. S., Gimhani, K. D. M., Rodrigo, M. N. N. (2023). Adaptability of enterprise resource planning (ERP) systems for cost management of building construction projects in Sri Lanka. Construction Innovation, 24 (5), 1255–1279. https://doi.org/10.1108/ci-05-2022-0108
  13. Pontoh, G. T., Indrijawati, A., Handayanto, A. B., Tahang, R. A., Supardi, T. S. (2024). Transforming public sector operations with enterprise resource planning: Opportunities, challenges, and best practices. Corporate Law and Governance Review, 6 (2), 8–24. https://doi.org/10.22495/clgrv6i2p1
  14. Szelągowski, M., Berniak-Woźny, J., Lupeikiene, A., Senkus, P. (2023). Paving the way for tomorrow: the evolution of erp and bpms systems. Scientific Papers of Silesian University of Technology. Organization and Management Series, 2023 (185), 481–510. https://doi.org/10.29119/1641-3466.2023.185.27
  15. Szelągowski, M., Lupeikiene, A., Berniak-Woźny, J. (2022). Drivers and Evolution Paths of BPMS: State-of-the-Art and Future Research Directions. Informatica, 33 (2), 399–420. https://doi.org/10.15388/22-infor487
  16. Martín-Navarro, A., Lechuga Sancho, M. P., Medina-Garrido, J. A. (2023). Determinants of BPMS use for knowledge management. Journal of Knowledge Management, 27 (11), 279–309. https://doi.org/10.1108/jkm-07-2022-0537
  17. Szelągowski, M., Berniak-Woźny, J., Lupeikiene, A. (2022). The Direction of the Future Development of ERP and BPMS: Towards a Single Unified Class? Digital Business and Intelligent Systems, 111–124. https://doi.org/10.1007/978-3-031-09850-5_8
  18. Szelągowski, M., Berniak-Woźny, J., Lupeikiene, A. (2022). The Future Development of ERP: Towards Process ERP Systems? Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum, 326–341. https://doi.org/10.1007/978-3-031-16168-1_21
  19. Ochoa Pacheco, P., Coello-Montecel, D., Tello, M., Lasio, V., Armijos, A. (2023). How do project managers’ competencies impact project success? A systematic literature review. PLOS ONE, 18 (12), e0295417. https://doi.org/10.1371/journal.pone.0295417
  20. ISO 31000:2018 Risk management – Guidelines (2018). International Organization for Standardization. Available at: https://www.iso.org/standard/65694.html
  21. Zubair, M. U., Farid, O., Hassan, M. U., Aziz, T., Ud-Din, S. (2024). Framework for Strategic Selection of Maintenance Contractors. Sustainability, 16 (6), 2488. https://doi.org/10.3390/su16062488
  22. Joshi, D., Pratik, S., Rao, M. P. (2021). Data governance in data mesh infrastructures: The Saxo Bank case study. Proceedings of the ICEB 2021 Conference. Available at: https://aisel.aisnet.org/iceb2021/52
  23. Papamichael, M., Dimopoulos, C., Boustras, G. (2024). Performing risk assessment for critical infrastructure protection: an investigation of transnational challenges and human decision-making considerations. Sustainable and Resilient Infrastructure, 9 (4), 367–385. https://doi.org/10.1080/23789689.2024.2340368
  24. Janssens, G., Kusters, R., Martin, H. (2021). Expecting the unexpected during ERP implementations: a complexity view. International Journal of Information Systems and Project Management, 8 (4), 68–82. https://doi.org/10.12821/ijispm080404
  25. Schuh, G., Jussen, P., Optehostert, F. (2019). Iterative Cost Assessment of Maintenance Services. Procedia CIRP, 80, 488–493. https://doi.org/10.1016/j.procir.2019.01.067
  26. Alexander, A., Li, Y., Plante, R. (2023). Comparative study of two menus of contracts for outsourcing the maintenance function of a process having a linear failure rate. IISE Transactions, 55 (12), 1230–1241. https://doi.org/10.1080/24725854.2023.2175939
  27. Stenström, C., Norrbin, P., Parida, A., Kumar, U. (2015). Preventive and corrective maintenance – cost comparison and cost–benefit analysis. Structure and Infrastructure Engineering, 12 (5), 603–617. https://doi.org/10.1080/15732479.2015.1032983
  28. Bouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., Bennadji, B. (2021). Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach. Sensors, 21 (4), 1044. https://doi.org/10.3390/s21041044
  29. Rousso, B. Z., Do, N. C., Gao, L., Monks, I., Wu, W., Stewart, R. A. et al. (2024). Transitioning practices of water utilities from reactive to proactive: Leveraging Australian best practices in digital technologies and data analytics. Journal of Hydrology, 641, 131808. https://doi.org/10.1016/j.jhydrol.2024.131808
  30. Summers, D. J., Visser, J. K. (2021). Factors that influence the decision to outsource maintenance in the processing industry. South African Journal of Industrial Engineering, 32 (1), 10–20. https://doi.org/10.7166/32-1-2127
  31. Mostofi, F., Tokdemir, O. B., Bahadır, Ü., Toğan, V. (2024). Performance‐driven contractor recommendation system using a weighted activity-contractor network. Computer-Aided Civil and Infrastructure Engineering, 40 (3), 409–424. Portico. https://doi.org/10.1111/mice.13332
  32. Bilir, C., Yafez, E. (2022). Project success/failure rates in Turkey. International Journal of Information Systems and Project Management, 9 (4), 24–40. https://doi.org/10.12821/ijispm090402
  33. Parra, N., Giraldo, S., la Rotta, D., Gómez, B., Mejía, Y., Morales, L. et al. (2024). A step towards digital transformation within a utility company through process automation for a sustainable energy transition. The Journal of Engineering, 2024 (12). https://doi.org/10.1049/tje2.70036
  34. Saeed, S., Gull, H., Aldossary, M. M., Altamimi, A. F., Alshahrani, M. S., Saqib, M. et al. (2024). Digital Transformation in Energy Sector: Cybersecurity Challenges and Implications. Information, 15 (12), 764. https://doi.org/10.3390/info15120764
  35. Shaheen, B. W., Németh, I. (2022). Integration of Maintenance Management System Functions with Industry 4.0 Technologies and Features – A Review. Processes, 10 (11), 2173. https://doi.org/10.3390/pr10112173
  36. Pan, Y., Zhang, L. (2021). A BIM-data mining integrated digital twin framework for advanced project management. Automation in Construction, 124, 103564. https://doi.org/10.1016/j.autcon.2021.103564
  37. Chen, C., Fu, H., Zheng, Y., Tao, F., Liu, Y. (2023). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581–594. https://doi.org/10.1016/j.jmsy.2023.10.010
  38. van Dinter, R., Tekinerdogan, B., Catal, C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008. https://doi.org/10.1016/j.infsof.2022.107008
  39. Mohammed, A., Al Busaeedi, N., Mohamed, A. A., Saud, S. (2022). Smart Project Management System (SPMS) – An Integrated and Predictive Solution for Proactively Managing Oil & Gas client Projects. ADIPEC. https://doi.org/10.2118/210877-ms
  40. van Besouw, J., Bond-Barnard, T. (2021). Smart project management information systems (SPMIS) for engineering projects – Project performance monitoring & reporting. International Journal of Information Systems and Project Management, 9 (1), 78–97. https://revistas.uminho.pt/index.php/ijispm/article/view/4452
Mitigating operational risks in critical infrastructure through integrated ERP-BPMS: a multi-case study

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Published

2025-05-30

How to Cite

Chernenko, Y., Bedrii, D., Haidaienko, O., & Meliksetov, O. (2025). Mitigating operational risks in critical infrastructure through integrated ERP-BPMS: a multi-case study. Technology Audit and Production Reserves, 3(4(83), 53–63. https://doi.org/10.15587/2706-5448.2025.330660