The R-SRE Model: A Prescriptive Framework for Operationalizing Resilient Service Delivery in Complex Retail Technology Stacks
DOI:
https://doi.org/10.55640/Keywords:
Site Reliability Engineering, Retail Technology, Service Level Objectives, Error Budget, Operational Resilience, Observability, Automated Incident ResponseAbstract
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.
References
Bhola, Abhishek, Arpit Jain, Bhavani D. Lakshmi, Tulasi M. Lakshmi, and Chandana D. Hari. "A wide area network design and architecture using Cisco packet tracer." In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I)
Chakravarty, A., Jain, A., & Saxena, A. K. (2022, December). Disease Detection of Plants using Deep Learning Approach—A Review. In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)
Devi, T. Aswini, and Arpit Jain. "Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud Computing Environments." In 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT)
Sagar Kesarpu. (2025). Contract Testing with PACT: Ensuring Reliable API Interactions in Distributed Systems. The American Journal of Engineering and Technology, 7(06), 14–23. https://doi.org/10.37547/tajet/Volume07Issue06-03
Jain, Arpit, Nageswara Rao Moparthi, A. Swathi, Yogesh Kumar Sharma, Nitin Mittal, Ahmed Alhussen, Zamil S. Alzamil, and MohdAnul Haq. "Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture." Computer Systems Science & Engineering 48, no. 2
Rao, S. Madhusudhana, and Arpit Jain. "Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review." International Journal of Safety & Security Engineering 14, no. 1
Sen, C., Singh, P., Gupta, K., Jain, A. K., Jain, A., & Jain, A. (2024, March). UAV Based YOLOV-8 Optimization Technique to Detect the Small Size and High Speed Drone in Different Light Conditions. In 2024 2nd International Conference on Disruptive Technologies (ICDT)
Rajgopal, P. R., & Karanam, L. (2025). MDR service design: Building profitable 24/7 threat coverage for SMBs. International Journal of Applied Mathematics, 38(2s). https://doi.org/10.12732/ijam.v38i2s.711
Kumar Tiwari, S., Sooraj Ramachandran, Paras Patel, & Vamshi Krishna Jakkula. (2025). The Role of Chaos Engineering in Enhancing System Resilience and Reliability in Modern Distributed Architectures. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3885
Singh, Pranita, Keshav Gupta, Amit Kumar Jain, Abhishek Jain, and Arpit Jain. "Vision-based UAV Detection in Complex Backgrounds and Rainy Conditions." In 2024 2nd International Conference on Disruptive Technologies (ICDT)
Walker, J., & Green, S. (2022). Ease of Use and Learning Curve in Data Warehousing Solutions: Snowflake’s Advantage. Software Usability Research Journal, 11(2)
Vikram Singh, 2025, Policy Optimization for Anti-Money Laundering (AML) Compliance using AI Techniques: A Machine Learning Approach to Enhance Banking Regulatory Compliance, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 14, Issue 04 (April 2025)
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Copyright (c) 2025 Ahmad Fikri Suteja (Author)
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