Workplace Efficiency Benchmarks

How to compare your eng team's velocity to industry benchmarks (and increase it): Step 1: Send your eng team this 4-question survey to get a baseline on key metrics: https://lnkd.in/gQGfApx4 You can use any surveying tool to do this—Google Forms, Microsoft Forms, Typeform, etc.—just make sure you can view the responses in a spreadsheet in order to calculate averages. Important: responses must be anonymous to preserve trust, and this survey is designed for people who write code as part of their job. Step 2: Calculate your how you're doing. - For Speed, Quality, and Impact, find the average value for each question’s responses. - For Effectiveness, calculate the percent of favorable responses (also called a Top 2 Box score) across all Effectiveness responses. See the example in the template above. Step 3: Track velocity improvements over time. Once you’ve got a baseline, you can start to regularly re-run this survey to track your progress. Use a quarterly cadence to begin with. Benchmarking data, both internal and external, will help contextualize your results. Remember, speed is only relative to your competition. Below are external benchmarks for the key metrics. You can also download full benchmarking data, including segments on company size, sector, and even benchmarks for mobile engineers here: https://lnkd.in/gBJzCdTg Look at 75th percentile values for comparison initially. Being a top-quartile performer is a solid goal for any development team. Step 4: Decide which area to improve first. Look at your data and using benchmarking data as a reference point, pick which metric you believe will make the biggest impact on velocity. To make this decision about what to work on to improve product velocity, drill down to the data on a team level, and also look at qualitative data from the engineers themselves. Step 5: Link efficiency improvements to core business impact metrics Instead of presenting these CI and release improvement projects as “tech debt repayment” or “workflow improvements” without clear goals and outcomes, you can directly link efficiency projects back to core business impact metrics. Ongoing research (https://lnkd.in/grHQNtSA) continues to show a correlation between developer experience and efficiency, looking at data from 40,000 developers across 800 organizations. Improving the Effectiveness score (DXI) by one point translates to saving 13 minutes per week per developer, equivalent to 10 hours annually. With this org’s 150 engineers, improving the score by one point results in about 33 hours saved per week. For so much more, don't miss the full post: https://lnkd.in/grrpfwrK

PRODUCTION PERFORMANCE ACTIVITIES: 1. Productivity Improvement: OEE Monitoring – Tracks machine availability, performance, and quality. Line Balancing – Distributes tasks evenly to reduce idle time. Cycle Time Reduction – Minimizes time per unit. Kaizen – Ongoing small improvements by operators. Time & Motion Study – Removes wasted motion. Bottleneck Removal – Use VSM, Takt Time, TOC to fix constraints. 2. Quality Improvement: First Pass Yield – Measures products without rework. In-Process Checks – Ensures quality at every step. Root Cause Analysis – Identifies defect causes (5 Whys, Fishbone). Poka Yoke – Error-proofing devices or techniques. Defect Analysis – Tracks trends and types of defects. 3. Cost Reduction: Material Yield – Reduces scrap and wastage. Energy Monitoring – Cuts power cost per unit. Tool Life Management – Lowers tool costs and downtime. Inventory Control – Uses FIFO, Kanban to manage stock. Lean Waste Removal – Eliminates non-value-added work. 4. Delivery Improvement: OTD Tracking – Measures actual vs. planned delivery. Production Scheduling – Aligns with customer demand. SMED (Quick Changeover) – Reduces setup times. Logistics Optimization – Streamlines material flow. 5. Safety Enhancement: 5S Implementation – Clean, safe, and organized workplace. Safety Audits – Identify and reduce risks. Incident Tracking – Record and act on near-misses. Safety Kaizens – Employee-led safety improvements. 6. Morale & Engagement: Daily Meetings – Share targets and issues. Suggestion Scheme – Reward employee ideas. Skill Matrix – Enable cross-training and flexibility. Recognition Programs – Appreciate team achievements. 7. Environmental Improvement: Waste Segregation – Improve recycling. Utility Savings – Conserve water and energy. Emission Control – Reduce dust, noise, fumes. Green Practices – Use eco-friendly materials/processes. Supporting Activities: Hourly Boards & Dashboards – Monitor daily performance. Tier Meetings – Escalate and solve issues. SOP Audits – Ensure process compliance. Gemba Walks – Management on the floor to guide teams.

key performance indicators (KPIs) and their purpose: --- 1. Production Efficiency Planned vs Actual Output: Tracks how closely actual production matches the plan. Cycle Time: Measures time for one production cycle. Downtime: Total time lost due to equipment or process issues. OEE (Overall Equipment Effectiveness): Composite score for availability, performance, and quality. --- 2. Machine Performance MTTR (Mean Time to Repair): Avg. time to fix equipment. MTBF (Mean Time Between Failures): Avg. time between equipment breakdowns. --- 3. Manpower Productivity Units Produced per Man-Hour: Efficiency of labor. Attendance & Utilization Rate: Measures workforce availability and use. Skill Matrix Compliance: Assesses alignment of skills with job requirements. --- 4. Material Management Material Yield: Efficiency in material usage. Material Availability: % of time materials are ready without delay. Inventory Turnover Ratio: Frequency of inventory replenishment. --- 5. Maintenance KPIs Preventive Maintenance Compliance: % of planned maintenance completed on time. Breakdown Frequency: Count of breakdowns in a set period. Maintenance Cost per Unit: Cost of maintenance per unit produced. --- 6. Delivery & Planning Schedule Adherence: % of orders completed on schedule. Lead Time: Time from order to production finish. Capacity Utilization: How much of total production capacity is used. --- 7. Energy & Sustainability Energy Consumption per Unit: Energy used per unit output. Carbon Emission per Batch: Emissions generated per batch. Waste Generated: Volume/weight of waste per unit. --- 8. Improvement & Standards Kaizen Events Conducted: Number of improvement activities. Standard Work Adherence: % of tasks done as per SOPs. 5S Audit Scores: Effectiveness in workplace organization and cleanliness. #ManufacturingKPIs #ProductionEfficiency #MachinePerformance #ManpowerProductivity #MaterialManagement #MaintenanceKPIs #DeliveryPlanning #EnergySustainability #ContinuousImprovement #LeanManufacturing #OEE #MTTR #MTBF #CycleTime #Downtime #InventoryTurnover #Kaizen #5SAudit #FactoryPerformance #OperationalExcellence #ManufacturingMetrics #IndustrialEngineering #ProcessOptimization

How useful do you find benchmarks of a software engineering org’s productivity? We've been conducting productivity benchmarks for companies more frequently and would love to understand your perspectives on their use cases, benefits, and potential pitfalls. Below is a sanitized example from a medium-sized company in the SaaS and web space. I view these benchmarks as diagnostic tools. They highlight issues that need fixing and identify top-performing teams whose best practices can be shared across the organization. A team's placement in the bottom quartile doesn't mean they lack potential; it just means they're currently underperforming and that you can help them. Their underperforming could be due to factors beyond the team's control, such as: -Unclear product requirements -Excessive meetings -Being pulled into other projects or initiatives -Slow or unreliable CI/CD pipelines -Unstable development environments -Dependency issues and knowledge silos -Overly complex architecture requiring extensive investigation before changes It could also be that the team: -Has low potential and skill but is performing at their maximum capacity -Has low potential and skill and isn't reaching even that potential We calculate productivity by using our model to quantify software engineering output, adjusting for team roles and seniority, and benchmarking against similar companies in the industry. Think of our model as performing the work of a panel of 10 independent software engineering experts who manually evaluate each commit across different dimensions to arrive at a productivity quantification (output units). A panel of 10 experts would be slow, unscalable, and prohibitively expensive. Our model highly correlates with human expert assessments but delivers ratings in a fraction of the time and cost. You can’t build high-performing software teams on intuition and politics–it simply doesn’t scale. Like it or not, data-driven decision-making in software engineering is essential and here to stay. A chart like this might seem reductive, but it offers critical clarity. By anchoring discussions in objective data, it shifts the focus away from politics and subjective guesswork. This is crucial because, let’s be honest, politics is one of the worst parts of software engineering… and “intuition” is often clouded by personal biases or agendas (we’re all human, it’s normal). And here’s the hard truth: using game theory, it’s rarely in a software engineering manager’s best interest to admit their team is underperforming–let alone that it’s their fault. Without extraordinary trust, the risks outweigh the benefits, as such honesty could lead to blame or career repercussions rather than constructive solutions. Objective data changes the conversation. It’s not about blame; it’s about uncovering the truth and solving problems. What are your thoughts on such benchmarks? I would love to learn your perspective! #softwareengineering #productivity #devops

How do you measure success in work patterns? At Worklytics, we provide guidelines and benchmarks to help organizations improve employee collaboration and well-being. These pieces serve different purposes but are most powerful when used together. Here’s how they differ and why both are essential for People leaders: 🌟 Guidelines: What Good Looks Like ➡️ Purpose: High-level recommended ranges based on industry best practices and Worklytics' experience. ➡️ Focus: Aspirational goals designed to improve focus time, reduce burnout, and optimize productivity. ➡️ Examples: 🔹 Focus Time: Employees should aim for 3.5+ hours per day for sustained productivity. 🔹 Meeting Hours: Teams should keep 4.5–8 hours of meetings per week to ensure a balance between collaboration and deep work. 🔹 After-Hours Messages: Keeping 5–15 messages per week minimizes stress, especially when messages are from direct managers. Guidelines reflect the ideal environment for knowledge workers to thrive, providing a clear target for organizations to align with. 📊 Benchmarks: How You Compare ➡️ Purpose: Industry-based metrics drawn from tens-of-millions of records to compare your organization to others. ➡️ Focus: Contextual insight into where your work patterns stand relative to peers. ➡️ Examples: 🔹 Focus Time: A benchmark might show that most organizations achieve the 50th percentile for focus time, but this often falls short of the guideline range. 🔹 Manager 1:1s: Benchmarks reveal that companies with top engagement levels maintain 0.5–1.5 manager 1:1s per week, aligning with the guideline. 🔹 Meeting Hours: While some organizations hover below 8 hours of meetings weekly, benchmarks may highlight higher averages in specific industries. Benchmarks provide the comparative clarity needed to contextualize whether your current state is competitive or lagging. 💡 Guidelines vs. Benchmarks in Action: ➡️ Focus Time: The guideline is 3.5+ hours per day. A benchmark might reveal your company sits at the 50th percentile, suggesting a need to increase focus time to align with best practices. ➡️ Collaboration Counts: The guideline recommends 5–12 strong collaborators weekly for optimal productivity. A benchmark might show your team exceeds 12, indicating potential bottlenecks in decision-making. ➡️ After-Hours Messages: The guideline sets a range of 5–15 messages. Benchmarks could show industry averages are closer to 20, flagging an opportunity to lead with healthier boundaries. By combining the aspirational clarity of guidelines with the real-world context of benchmarks, People leaders can identify actionable opportunities to improve work patterns and drive better outcomes. Find more examples and insights in the comments below. How could your organization benefit from using guidelines and benchmarks together? #PeopleAnalytics #HRAnalytics #TalentAnalytics #WorkforceAnalytics #WorkforceIntelligence

📊 New ActivTrak Productivity Benchmarks Are Out! We've analyzed data from 135,000 users to bring you insights into daily work patterns. Here's what we found: 🎯 Productive Time: • Median: 6.4 hours/day • Top quartile: 7.6 hours/day 🧠 Focused Time: • Median: 4.1 hours/day • Top quartile: 5.4 hours/day 🤝 Collaboration Time: • Median: 0.4 hours/day • Top quartile: 1.0 hour/day Key Takeaways: ➪ There's significant room for improvement in focused work time across organizations. ➪ The gap between median and top performers highlights the potential for productivity gains. ➪ How does your team compare? see the link in the comments to learn more about these benchmarks. 💡 Pro Tip: Use these benchmarks as a starting point to set realistic goals and optimize your team's productivity. Remember, every organization is unique – consider your specific work environment when interpreting these numbers. #ProductivityInsights #WorkforceAnalytics #ActivTrak #DataDrivenProductivity