Hi everyone. I am it professional with 20 years of experience as a retail architect. Today we are looking at our site reliability engineering. Our SRE helps scale a driven retail logistics platforms with rising customer expectations. Retail needs, smart, reliable systems. SRE brings the tools and mindset to keep these platforms resilient, scalable, and fast, making a powered omnichannel experiences possible. Let's explore how reliability powers the future of retail. Thank you. Let's look at the evolution of retail logistics. We started with the traditional model. Others took days processed through centralized warehouses. Then came the omnichannel era, blending online offline channels, cutting ment time down to 24 hours. Next, AI powered micro fulfillment centers arrived. Using predictive algorithms to pull bill orders within hours. And now we are stepping into the next generation where predictive systems start processing even before the customer finishes their order. Retail logistics is getting faster, smarter, and more proactive than ever. Now let's talk about the core SRE principles in retail logistics. First, we have error budgets. They help quantify how much risk we can accept without impacting the operations next service level objectives. Our SLOs, these align technical performance with what really matters to the customer experience. Then there is automation key to reducing manual work, especially with continuous deployment pipelines like CICD pipelines. And finally observability, giving us deep visibility into complex distributed logistics systems so we can detect and fix issues fast. Together these SRE principles. Keep retail logistics reliable, efficient, and customer focused. Let's talk about building resell and micro fulfillment center, MFC infrastructure, A key piece of modern retail logistics. First, the business volume. We are aiming for 98.7% order accuracy with one hour delivery windows. That's the kind of precision and speed today's customer expects. At the core is the A intelligence layer. It powers predictive inventory placement. And accurate demand forecasting enduring products are where they need to be before the customer even clicks. By supporting that distributed systems architecture built with far tolerant microservices and regional. Failover. So even if one part of the system goes down, the operations keeps running smoothly. All of this runs a strong infrastructure foundation, Kubernetes orchestrated container that can be at scale to meet changing demand in real time. The lead approach keeps MFCs fast, reliable, and ready for peak retail perform. Let's explore our advanced observability solutions, the backbone of reliable high performing retail logistics, starting with tropic monitoring our enterprise grade distributor pricing system, and 10 x tropic spikes during sp peak shopping times without any performance drop paid with the smartly detection algorithm. Our systems alert engineers to potential issues before customer even notice. And the performance analysis side, we use custom metrics to precisely track fulfillment velocity across all our regional MFC networks. Real time dashboards offer instant. Comparison of actual versus expected performance for every geographic zone. And when it comes to business insights, we connect the dots between technical metrics and business outcomes. Our SL word tracking doesn't just say. In the engineering VLM, it feeds into initiative executive dashboards clearly showing how infrastructure health impacts customer satisfaction. In short, our observability is then just about keeping system running. It's about keeping customers happy and the business growing. Let's talk about a powered route optimization at scale. A game changer for last mile delivery. It start with position analysis. We use realtime GPS data from our delivery fleet to know exactly where every vehicle is at any moment. Then we layer on traffic prediction. Our machine learning models forecast congestion patterns before they happen, allowing the system to stay ahead of delay. Finally, the root competition dynamically the fast, most efficient farts through complex urban environments. Adapting in real time as conditions change the result faster deliveries, lower cost, and happier customers. Let's walk through our service level objectives framework, which align technology, customer experience, and business outcomes. Starting with technical vos, we target 99 point 99% system availability. A PA responses under a hundred milliseconds and other processing latency under 50 seconds. These ensure our backend stays lightening fast and dependable. Next, our customer experience SVOs. We aim for delivery time accuracy within plus or minus five minutes. Other accuracy about 99.5% and app transaction completion rates over 98%, all focused on smooth, reliable customer journey. Finally, our business customer outcomes SVOs connect performance to impact. We keep. Cart a abandonment under 15%. Push for a repeat purchase rate above 65% and optimized delivery efficiency to x, exceed 12 orders for hour. This framework keep everyone from engineers to execute. Two focused on what truly let's drive into our incident management framework. Designed to respond quickly and efficiently when things go wrong. Detection. Start with automated alerts through pager duty, customer feedback, monitoring and synthetic transaction. Canaries proactively catching issues before they affect the users for response. We have a structured incident command. With cross-functional teams, communication channels are pre dependent, ensuring everyone knows their role and process to Palo. When it comes to remediation, we use playbook driven procedures, including automated rollbacks to restore service quickly. We also focus on customer impact mitigation. Minimizing disruption for the end user. Finally, in the learning phase, we conduct blameless postmortems to understand what went wrong without pointing fingers. We track systematic improvements and update our knowledge base to prevent future incidents. This framework ensures. We respond fast, learn continuously, and keep our services reliable. Let's talk about chavos engineering in practice, how you proactively test and improve the resilience of our systems. It starts with. Hypothesis formation. We begin by formulating precise hypothesis about the systems, study steps and predicting how the system will behave when disruptions happen. Next, we conduct controlled experiments. Our engineers intentionally introduce calibrated failures in production environments. To test system boundaries and assess recovery mechanisms, essentially pushing our systems to their limits. Then we measure impact. We correlate technical metrics with customer experience indicators to understand the real world impact of system degradations. This helps us quantify how failures affect the customer. Finally, the insights we gain help us improve resilience we use. That's what we have learned to develop automated recovery system, self ailing infrastructure, and a detailed incident. S. All aimed at making our system more robust and responsive in the future. Chavos engineering isn't about causing harm, it's about making our system stronger and more resell. Let's talk about our lift shift security approach, which integrates security at every stage of the development lifecycle. Starting with development, we focus on real time vulnerability detection directly in the rd, sorry, ID using automated security linking and code quantity quality gates to catch issues as early as possible in the development process. In continuous integration, we apply. Comprehensive static application security testing automatically scan for CV vulnerabilities and validate third party dependencies to ensure they don't introduce risks. When we move to deployment, we implement container image scanning, runtime application, self production, and ensure automated regularity. Compliance verification to keep everything secure during the deployment page. Finally, in production, we imply advanced treat intelligence monitoring, leveraging a powered ity detection and well defined incident response protocols. To stay high head up, potential traits. This approach ensures security is embedded through preventing risk before they make it into production. To wrap up, here are the key takeaways for building reliable retail logistics. Define clear lvo. It's critical to balance technical metrics with customer experience indicators. This ensures we meet business goals while keeping customers happy. Invest in observability. Build comprehensive visibility across all distributed systems so you can detect and address issues in real time. Embrace automation, reduce manual effort by implementing infrastructure as code. Enabling faster, more efficient operations, faster. A real a resilience culture. Promote blameless problem solving and continuous learning to improve systems and processes. No matter the challenges. These principles will guide you. Towards a more reliable, efficient, and customer centric retail logistics operations. Thank you.