Operational Efficiency and Risk Mitigation in Optical Fibre Networks: A Business Management Framework
Abstract
Purpose: These factors have necessitated the fact that optimal fibre networks have become the backbone of the new communication infrastructure, with the increase in the number of data-intensive applications and the worldwide connectivity needs that exist today. Nevertheless, there exist multiple challenges in attaining efficiency in these complex systems with the lowest risk.
Methodology: The paper presents a holistic business management structure to improve efficiency and minimise risks in optical fibre networks. Based on the general principles of systems thinking, business process management (BPM), and risk governance, we develop a systematic framework for assessing and enhancing network operations.
Findings: The framework combines key performance indicators (KPIs), predictive maintenance, cybersecurity protocols, and adaptive project management. Practical Implications: Case studies highlight the framework's usefulness for telecommunications companies.
Originality/Value: The study will also contribute to existing bodies of knowledge in telecommunications and business management by connecting the technical aspects of infrastructure with strategic business management practices.
Keywords: Optical fibre networks, Business process management, Risk governance models, Cyber security protocols, Adaptive project management.
References
- Ahmed, S., Noor, A., & Desai, R. (2020). Integrated performance and risk monitoring in optical networks using business intelligence frameworks. IEEE Access, 8, 112563–112573. https://doi.org/10.1109/ACCESS. 2020.3002986
- Dey, P., & Hassan, M. (2020). Standardisation issues in performance and risk metrics of optical network systems: A review. Journal of Optical Communications, 41(2), 155–163. https://doi.org/10.1515/joc-2020-0098
- El-Mahdy, S. M., Farag, W., & Khamis, A. (2020). Deep learning for proactive fault detection in optical fibre infrastructure. Opto-Electronics Review, 28(3), 213–221. https://doi.org/10.1016/j.opelre.2020.03.007
- Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71–79. https://steinbeis-bi.de/images/artikel/hbr_1992.pdf
- Kumar, D., Kumar, R., & Sharma, N. (2019, October). A risk reduction approach in optical backbone network. In 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 206–211). IEEE. https://doi.org/10.1109/ISPCC48220.2019.8988530
- Lee, J., Park, H., & Kim, S. (2015). Software-defined networking for dynamic resource allocation in optical backbone networks. Optical Switching and Networking, 17, 29–41. https://doi.org/10.1016/j.osn.2015.05.002
- Patel, R., Mehta, K., & Bose, A. (2021). Bayesian inference model for optical fibre risk assessment under environmental and operational constraints. Telecommunication Systems, 78, 55–72. https://doi.org/10.1007/s11235-021-00839-4
- Sharma, A., Gupta, M., & Arora, S. (2019). Predictive fault detection in optical fibre networks using machine learning. International Journal of Optical Network Technologies, 10(3), 145–153. https://doi.org/10.1016/j.ijont.2019.03.004
- Sarmacharjee, S., Ghosh, D., Ahmad, I., Ahmad, F., Shinde, R. M., & Ola, M. O. (2024). Exploring the role of artificial intelligence in supply chain management for SMEs: Critical success factors and the impact of environmental uncertainty. Journal of Informatics Education and Research, 4(3), 3642–3654.
- Singh, R., Bhatt, N., & Kaul, M. (2022). Lifecycle network management aligned with strategic telecom business goals. Telecommunications Policy, 46(10), 102358. https://doi.org/10.1016/j.telpol.2022.102358
- Wang, D., Zhang, C., Chen, W., Yang, H., Zhang, M., & Lau, A. P. T. (2022). A review of machine learning-based failure management in optical networks. Science China Information Sciences, 65, 211302. https://doi.org/10.1007/s11432-022-3557-9
- Zhang, T., & Wu, Q. (2015). Business-centric optimization in optical network operations: A decision support approach. Journal of Network and Systems Management, 23(4), 890–905. https://doi.org/10.1007/s10922-015-9334-1