Optimizing Reliability and Evidence Integration in Large-Scale System Management: A Multidimensional Analysis of Error Budgeting and Implementation Strategies

Abstract

In the contemporary landscape of large-scale system operations, reliability and evidence-based management emerge as pivotal constructs for sustainable performance. This study examines the integration of site reliability engineering (SRE) principles with evidence-based operational strategies to optimize error budget management and enhance system resilience. Drawing from recent research and established theoretical frameworks, the study investigates how systematic error tracking, probabilistic risk assessment, and implementation of best-practice guidelines contribute to operational stability in complex systems. Furthermore, it addresses organizational barriers, cognitive biases, and contextual factors influencing adoption of evidence-based interventions. Through a comprehensive review of scholarly literature and a critical interpretive analysis, this research underscores the multifaceted implications of combining SRE practices with evidence-based management approaches. Key findings indicate that structured error budget frameworks, when coupled with continuous feedback loops and managerial knowledge brokering, significantly reduce system failures while fostering adaptive capacity among operational teams. The discussion elaborates on the intersection between theoretical modeling, practical constraints, and organizational learning mechanisms, offering nuanced insights into scalability, policy design, and workforce competency development. Limitations related to cross-sector generalizability and technological heterogeneity are highlighted, and future research trajectories are proposed to address integration with emerging AI-driven monitoring systems. The study contributes to both theoretical and applied discourse on reliability optimization, presenting a synthesis that bridges engineering practices and evidence-informed management in the governance of high-complexity infrastructures.