A Model-Driven Software Development Framework for Scalable Customer Relationship Management Platforms in Multi-Enterprise Business Environments
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I1P107Keywords:
Customer Relationship Management (CRM), Model-Driven Architecture (MDA), Model-Driven Engineering (MDE), Cloud Computing, Enterprise Integration, Workflow AutomationAbstract
Customer Relationship Management (CRM) systems have become fundamental enterprise platforms for managing customer interactions, improving business communication, and supporting organizational decision-making processes. In a multi-enterprise context, however, the traditional CRM approach may pose limitations concerning scalability, interoperability, maintenance and adaptation to the fast-changing business needs. This study suggests a scalable and flexible model-driven software development framework that provides support for CRM platforms that are configured in distributed enterprise ecosystems. The proposed framework is based on the use of Model-Driven Architecture (MDA), cloud-native deployment models, service-oriented computing, and microservices-based design principles, which enhance the modularity and maintainability of the system and its operation. By using UML-based system modeling and automated code generation mechanisms, software development is simplified, and the deployment process is accelerated and its complexity is decreased. The framework also includes the features such as workflow automation, API-based enterprise integration, authentication services, analytics repositories, and a distributed database management to enable seamless customer interactions and enterprise collaboration. Performance evaluation proves that the proposed framework maintains stable response times, efficient usage of resources and scalable multi-tenant operations under different workloads in the enterprise. The architecture is scalable and will work well with more users and distributed business operations without significant performance degradation, according to the scalability analysis. When comparing to traditional monolithic CRM systems and the early cloud-based CRM systems, it shows enhancements in development efficiency, runtime flexibility, and adaptability to multi-enterprise. The results indicate that model-driven engineering and scalable cloud infrastructure can serve as a solid base for the creation of future CRM systems to serve dynamic and collaborative enterprise business environments.
References
1. Li, S., Xu, L., Wang, X., & Wang, J. (2012). Integration of hybrid wireless networks in cloud services oriented enterprise information systems. Enterprise Information Systems, 6(2), 165-187.
2. Chattopadhyay, S., Mo, J. P., & Chan, D. S. (2010). Business model for virtual manufacturing: a human-centred and eco-friendly approach. International Journal of Enterprise Network Management, 4(1), 39-58.
3. Kale, V. (2018). Enterprise Process Management Systems: Engineering Process-Centric Enterprise Systems using BPMN 2.0. CRC Press.
4. Fayoumi, A. (2013). Enterprise modelling framework for dynamic and complex business environment: socio-technical systems perspective (Doctoral dissertation, Loughborough University).
5. Buttle, F., & Maklan, S. (2019). Customer relationship management: concepts and technologies. Routledge.
6. Karunanithy, M., & Kajendra, K. (2012, July). An evolution of customer relationship management: a conceptual approach. In Proceedings of Jaffna University International Research Conference (JUICE-2012) (Vol. 49).
7. Messner, W. (2005). Customer relationship management technology: A commodity or distinguishing factor? Business Information Review, 22(4), 253–262. https://doi.org/10.1177/0266382105060604
8. Xu, Y., Duan, Q., & Yang, H. (2005, September). Web-service-oriented customer relationship management system evolution. In 13th IEEE International Workshop on Software Technology and Engineering Practice (STEP'05) (pp. 39-48). IEEE.
9. Saarijärvi, H., Karjaluoto, H., & Kuusela, H. (2013). Customer relationship management: the evolving role of customer data. Marketing intelligence & planning, 31(6), 584-600.
10. Ang, L., & Buttle, F. (2006). CRM software applications and business performance. Journal of Database Marketing & Customer Strategy Management, 14(1), 4-16.
11. Mohamed, D. A., & Darwish, N. R. (2019). Extracting CRM requirements–Waterfall or Agile: A comparative study. International Research Journal of Advanced Engineering and Science, 4(3), 1-5.
12. Chalmeta, R. (2006). Methodology for customer relationship management. Journal of systems and software, 79(7), 1015-1024.
13. Chorafas, D. N. (2001). Integrating ERP, CRM, supply chain management, and smart materials. Auerbach Publications.
14. Zheng, S., Yen, D. C., & Tarn, J. M. (2000). The new spectrum of the cross-enterprise solution: the integration of supply chain management and enterprise resources planning systems. Journal of Computer Information Systems, 41(1), 84-93.
15. Shafiei, F., Sundaram, D., & Piramuthu, S. (2012). Multi-enterprise collaborative decision support system. Expert Systems with Applications, 39(9), 7637-7651.
16. Di Ruscio, D., Pierantonio, A., & Tisi, M. (2019). Model-driven engineering and low-code development: Perspectives and challenges. Proceedings of the 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 659–663. https://doi.org/10.1109/MODELS-C.2019.00101
17. Dubey, M. (2019). The Salesforce Ecosystem Building a Scalable and Resilient CRM on Hybrid Infrastructure.
18. Chen, I. J., & Popovich, K. (2003). Understanding customer relationship management (CRM) People, process and technology. Business process management journal, 9(5), 672-688.
19. Cai, H., Gu, Y., Vasilakos, A. V., Xu, B., & Zhou, J. (2016). Model-driven development patterns for mobile services in cloud of things. IEEE Transactions on Cloud Computing, 6(3), 771-784.
20. Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
