How to Improve System Reliability

My maintenance reliability transformation process from start to finish in 7 steps: 1. Assessment and Gap Analysis - Compare current practices against best practices in planning/scheduling, storeroom, PM optimization, and root cause analysis 2. Develop Strategic Roadmap - Create a project plan with ~200-250 line items that map your reliability journey in manageable chunks 3. Leadership Alignment - Meet with plant leadership to prioritize initiatives based on impact and resources, focusing on quick wins first 4. Education and Competency Development - Implement training for planners, reliability engineers, storeroom personnel, and maintenance managers through courses and certification 5. Process Implementation - Execute targeted improvements in highest-impact areas (typically planning/scheduling, PM optimization, storeroom management) 6. Coaching and Reinforcement - Work side-by-side with your team to embed new practices and overcome resistance to change 7. Continuous Improvement - Implement review cycles and feedback loops to identify and address new opportunities That's my process. What's yours? PS: I've seen this approach reduce reactive maintenance from 78% to 22%, improve schedule compliance from near-zero to 78%, and increase uptime from 88% to 96%. #Reliability #MaintenanceExcellence #ReliabilityEngineering

Google has some of the world's best Site Reliability Engineers & Production services, keeping their & millions of businesses kicking on the web. Last week, I read Google’s official SRE best practices to find what makes them so effective, here’s what I learned: 1. Fail Sanely    - Sanitize and validate inputs to prevent errors.    - If bad input occurs, continue with the previous state until valid input is confirmed.    - Example: Google's DNS outage was prevented by adding sanity checks to avoid empty or invalid configurations. 2. Progressive Rollouts    - Rollout changes in stages, starting with small percentages of traffic to mitigate risk.    - Monitor rollouts closely, and roll back immediately if issues are detected. 3. Define SLOs from User's Perspective    - Measure availability and performance based on what users experience.    - Example: Gmail’s improved user experience after adjusting SLOs based on client-side error rates. 4. Error Budgets    - Define an acceptable failure rate and freeze new launches when error budgets are exceeded.    - Balances reliability and the pace of innovation. 5. Monitoring    - Alerts should be actionable: trigger pages for immediate action, or tickets for later.    - Avoid reliance on emails for important alerts, as they will be ignored over time. 6. Postmortems    - Blameless, focusing on system and process failures, not individuals.    - Improve systems to avoid future incidents. 7. Capacity Planning    - Plan for simultaneous planned and unplanned outages.    - Validate forecasts with real-world data and use load testing to ensure capacity meets demand. 8. Overloads and Failure    - Systems should degrade gracefully under load.    - Implement techniques like load shedding, queuing, and exponential backoff to avoid cascading failures. 9. SRE Teams    - Limit SREs to 50% operational work; include product developers in on-call rotations to share responsibility.    - Regular production meetings between SRE and development teams help improve system design. 10. Incident Handling Practice    - Routinely practice handling outages to prevent long incidents due to team inexperience in rare failures.

Maintenance and Reliability Best Practice (If you really want to improve) 1) Set Clear Goals and Expectations (not just talk) 2) Simplify Processes 3) Optimize Strategies 4) Minimize Downtime 5) Use Technology Expanded below 1) Set Clear Goals and Expectations (PDCA - Not Just Talk) Set goals to boost EBITDA and Capacity (e.g., cost reduction, asset uptime). Track (MTBF, MTTR, OEE) to measure financial and capacity impacts. Engage (leadership, operators, maintainers, customers) to align on priorities. Apply PDCA cycles to refine strategies for profitability and output. 2) Simplify Processes Use RCM to prioritize critical assets and eliminate non-value-adding tasks. Apply FMEA to reduce design-related risks impacting EBITDA. Streamline workflows with Value Stream Mapping to cut waste. Standardize and Simplify components to lower costs and support capacity. 3) Optimize Strategies Implement operator-based maintenance to align with maintenance goals and enhanced capacity. Adjust maintenance schedules using data to maximize uptime and minimize costs. Optimize spare parts inventory to balance availability and financial efficiency. Train operators and technicians to support defect elimination and reliability. 4) Minimize Downtime Use RCA to identify and eliminate defects threatening capacity and profitability. Manage work orders with CMMS to ensure high asset availability. Pre-kit materials to speed up maintenance tasks. Create clear SOPs for consistent operator and maintenance execution. 5) Use Technology Monitor assets with condition-based systems to maintain high capacity. Predict and prevent failures using analytics to protect EBITDA. Automate CMMS workflows for efficient defect tracking and resolution. Explore digital twins or robotics to optimize inspections and operations. ReliabilityX

Yes, even Errors have to be on budget! 2 simple metrics you need to know to call your system resilient. You publish your source code into the wild with a promise: it will work most of the time. To make sure your system is reliable, you need to understand: 𝗘𝗿𝗿𝗼𝗿 𝗕𝘂𝗱𝗴𝗲𝘁𝗶𝗻𝗴 Error budgeting defines how much downtime is acceptable over a certain period. Let's say our goal is to be 99.9% uptime, then the allowable downtime can be calculated as follows: SLO = 99.9% uptime Total Time Period = 30 days (43,200 minutes) Allowable Downtime = Total Time * (1 − Uptime %) Allowable Downtime = 43,200 minutes * (1−0.999) So, your system can have a maximum of 43.2 minutes of downtime in 30 days. Knowing your error budget helps you decide when to add new features and when to focus on fixing problems. 𝗠𝗲𝗮𝗻 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 (𝗠𝗧𝗧𝗥) Mean Time to Recovery is the average time to fix a problem and get your system back up and running after an issue occurs.  Let's say: Total Downtime = 240 minutes Number of Incidents = 6 MTTR = Total Downtime / Number of Incidents MTTR = 240 minutes / 6 = 40 minutes So, the average recovery time per incident is 40 minutes. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 • Error Budget -> It's about balancing innovation with system reliability. • MTTR -> How quickly you can bounce back from failures. • Lower MTTR = Higher Resilience! Resilience isn't just dodging failures; it's about planning for them and bouncing back fast.