Purpose: This study addresses the critical challenge of maintaining operational resilience and high-quality service delivery within complex, large-scale retail ecosystems. Traditional operations models often fail to scale with the demands of omnichannel commerce, necessitating the adoption of specialized frameworks. The primary objective is to develop and validate a Site Reliability Engineering (SRE) framework specifically optimized for the unique, transaction-heavy environment of modern retail.

Design/Methodology/Approach: We propose the Retail-SRE (R-SRE) model, a five-pillar conceptual framework encompassing Monitoring, Automation, Risk Management, Team Alignment, and Security (MARTS). The methodology involved defining novel, retail-specific Service Level Indicators (SLIs) and Service Level Objectives (SLOs), such as Transaction Latency and Inventory Sync Accuracy. The study incorporates a simulated financial impact analysis of the Error Budget mechanism, quantifying the technical-business trade-off in the retail context. Advanced monitoring techniques, including deep learning for multivariate anomaly detection, were integrated to enhance predictive capability.

Findings: The R-SRE model provides a clear, actionable pathway for large-scale retail enterprises to transition to a proactive, engineering-driven operations culture. Implementation results, discussed through a detailed analysis of operational toil, indicate a substantial reduction in manual labor from 55% to 18%, reallocating resources to strategic engineering. Crucially, the quantitative financial analysis demonstrates a direct association between strict SLO adherence and minimized revenue loss. Furthermore, the integration of predictive monitoring successfully achieved an 83% Zero-Downtime Resolution Rate on identified pre-failure states.

Originality/Value: This research offers one of the first comprehensive SRE models explicitly tailored for the nuances of retail. It closes critical research gaps by formally linking SRE metrics to financial outcomes and integrating advanced security and predictive monitoring practices, establishing reliability as a core competitive metric.