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Enterprises with extensive application portfolios face significant challenges in cloud migration beyond simple rehosting. While cloud providers offer migration solutions, most focus on lift-and-shift strategies, which move workloads to the cloud but provide minimal business value. This often leads to low prioritization by business units and increased long-term technical debt. A more structured and scalable approach is necessary to ensure that cloud migration aligns with enterprise-wide standardization, governance, and operational excellence. This paper presents a Cloud Migration Factory Model, a structured methodology designed to streamline large-scale application migrations while integrating enterprise-wide best practices such as single sign-on (SSO), observability, centralized caching, and security compliance. The model organizes migration efforts into specialized Product-Oriented Delivery teams. dedicated to different phases: discovery, analysis, migration, testing, deployment, and training. A key aspect of this approach is incorporating automation-driven discovery tools, which perform static code analysis and rule-based assessments to generate migration roadmaps, reducing errors and manual effort. By leveraging parallel workflows for discovery and training, the factory model accelerates migrations while ensuring that development teams receive the necessary knowledge transfer to maintain cloud-native applications post-migration. We validate this approach through a case study of a large financial enterprise, demonstrating a 30% improvement in migration velocity and significant reductions in operational friction and cloud onboarding time. The paper concludes with an analysis of key challenges, the role of automation in migration planning, and future directions, including AI-driven predictive analysis for enterprise cloud adoption.
Kesharwani et al. (Sat,) studied this question.