Productivity Benchmarking Standards

#knowledgeable articles in Manufacturing excellence. OEE( Overall Equipment Effectiveness) is a lean manufacturing tool and universal best practice to monitor, evaluate and improve the effectiveness of a production process. identifies the percentage of planned production time that is truly productive. An OEE score of 100% represents perfect production: manufacturing only good parts, as fast as possible, with no downtime. OEE is the ratio of Fully Productive Time to Planned Production Time Terms used in the OEE Formula: Good Count: pieces that are manufactured without any defects Ideal Cycle Time: the theoretical fastest possible time to manufacture one piece Planned Production Time: the total time that the production asset is scheduled for production Fully Productive Time: producing only good pieces, as fast as possible, with no stop time. Simple calculation of OEE OEE = (Good Count × Ideal Cycle Time) / Planned Production Time OEE is calculated by multiplying the three OEE factors: Availability, Performance, and Quality.(OEE = Availability × Performance × Quality). Availability is calculated as the ratio of Run Time to Planned Production Time, where Run Time is simply Planned Production Time less Stop Time. Availability = Run Time / Planned Production Time Run Time = Planned Production Time − Stop Time Performance Loss includes all factors that cause the production asset to operate at less than the maximum possible speed when running (including Slow Cycles and Small Stops). Performance = (Ideal Cycle Time × Total Count) / Run Time Quality is calculated as the ratio of Fully Productive Time (only Good Count manufactured as fast as possible with no Stop Time) to Net Run Time (fastest possible time for Total Count). Quality = Good Count / Total Count OEE = Availability × Performance × Quality OEE is the ratio of Fully Productive Time to Planned Production Time. OEE BENCHMARKS : what is considered a “good” OEE score? What is a world-class OEE score? 1.100% OEE is perfect production: manufacturing only good parts, as fast as possible, with no stop time. 2. 85% OEE is considered world class for discrete manufacturers. For many companies, it is a suitable long-term goal. 3. 60% OEE is fairly typical for discrete manufacturers, but indicates there is substantial room for improvement. 4. 40% OEE is not at all uncommon for manufacturing companies that are just starting to track and improve their manufacturing performance. It is a low score and in most cases can be easily improved through straight forward measures. Plant floor employees will perform best when they are given goals that are real-time, easily interpreted and highly motivational. 1.Target: a real-time production target driven by the planned rate of production 2.Actual: the actual production count 3.Efficiency: the ratio of Target to Actual; how far ahead or behind production is running in terms of a percentage 4.Downtime Monitoring & improvements by considering all 16 losses.

𝗛𝗼𝘄 𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆? With all the conversation around McKinsey's article on measuring productivity, it would be helpful to summarise some industry-accepted methodologies. This week, we will go through all of them. 𝟭. 𝗗𝗢𝗥𝗔 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 (𝟮𝟬𝟭𝟰) Authored by Nicole Forsgren, Jez Humble, and Gene Kim, it comprises a set of performance indicators known as the Four Key Metrics. These metrics help businesses assess and measure the success of their DevOps initiatives: 🔹 Lead time for changes is the duration between a commit and production. 🔹 Deployment frequency measures how often changes are shipped. 🔹 Mean time to recovery (MTTR) is the average time to restore service after an outage. 🔹 Change failure rate is the percentage of releases resulting in downtime. Their research indicates that shorter lead times, higher deployment frequencies, lower change failure rates, and shorter recovery times are common traits of high-performing businesses. 𝟮. 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗦𝗠 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 (𝟮𝟬𝟭𝟴) Google takes a data-driven approach using the Goals/Signals/Metrics (GSM) framework to measure engineering productivity: 🔹 Goals define desired outcomes. 🔹 Signals indicate achievement of goals. 🔹 Metrics serve as proxies for signals. Google categorizes productivity into five core components (QUANTS): 🔸 Quality of Code: Evaluates the code produced. 🔸 Attention of Engineers: Assesses focus and distractions. 🔸 Intellectual Complexity: Measures cognitive load. 🔸 Tempo and Velocity: Analyzes task completion speed. 🔸 Satisfaction: Gauges happiness with work and tools. Each component allows for selecting goals, defining signals, and measuring metrics to drive continuous improvement. 𝟯. 𝗦𝗣𝗔𝗖𝗘 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 (𝟮𝟬𝟮𝟭) The DORA team extended their approach with the SPACE framework to evaluate engineering productivity comprehensively: 🔹 Satisfaction: Measures team fulfillment and happiness. 🔹 Performance: Evaluates individual and team policies. 🔹 Activity: Quantifies code commits within a timeframe. 🔹 Cohesion: Assesses review thoroughness and team dynamics. 🔹 Effectiveness: Balances efficiency and flow disruptions. SPACE aims to provide a holistic view by considering satisfaction, efficiency, and team dynamics in productivity assessments. 𝟰. 𝗗𝗲𝘃𝗘𝘅 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 (𝟮𝟬𝟮𝟯) Developed by Abi Noda, Margaret-Anne Storey, Nicole Forsgren, and Michaela Geriler, the DevEx framework focuses on enhancing developer experience: 🔹 Feedback Loops: Evaluate speed and quality of response to developer actions. 🔹 Cognitive Load: Measures mental effort required for tasks. 🔹 Flow State: Assesses immersion and enjoyment in work. Improving these dimensions enhances developer productivity by addressing obstacles and enhancing value delivery. #technology #softwareengineering #techworldwithmilan #careers #productivity

INDUSTRY BENCHMARK KPIs – OPEN CAST METAL MINES (Iron ore, Manganese, Bauxite, Limestone – Excavator–Tipper system) 🏗️ 1. Production & Productivity KPIs KPI Formula Industry Benchmark Excavator Productivity Tonnes / Operating Hour 800–1,500 t/hr Bucket Fill Factor Actual ÷ Rated 80–95% Tonnes per Shift (Shovel) Total t ÷ shift 6,000–12,000 t/shift OB : Ore Ratio OB ÷ Ore 2:1 to 6:1 (deposit specific) Plan Achievement Actual ÷ Plan ≥ 95% ⏱️ 2. Equipment Time Utilisation KPIs KPI Formula Benchmark Availability (%) (Available hrs ÷ Calendar hrs) ×100 85–90% Utilisation (%) (Operating hrs ÷ Available hrs) ×100 70–80% Effective Utilisation Operating hrs ÷ Calendar hrs 60–70% Idle Time Idle hrs ÷ Shift hrs < 15% 🚛 3. Haulage & Transport KPIs KPI Benchmark Tipper Productivity 120–180 t/hr Avg Cycle Time 10–18 min Queue Time at Excavator < 3 min Loaded Speed 20–30 km/hr Empty Speed 25–35 km/hr Trips per Shift 20–35 ⛽ 4. Fuel Efficiency KPIs (High Impact) Equipment Benchmark Excavator Diesel 0.20–0.30 L/t Tipper Diesel 0.30–0.45 L/t Drill 3–6 L/m drilled Dozer 25–40 L/hr Fuel Loss (Variance) < ±5% 🔧 5. Maintenance & Reliability KPIs KPI Benchmark Mean Time Between Failures (MTBF) > 80–120 hrs Mean Time To Repair (MTTR) < 3–4 hrs Breakdown % < 10% of shift Planned Maintenance Ratio > 70% Tyre Life (tippers) 6,000–10,000 hrs 💥 6. Drilling & Blasting KPIs KPI Benchmark Drilling Rate 20–35 m/hr Powder Factor (Metal Mines) 0.6–0.9 kg/t Boulder Generation < 5% Oversize (>1 m) < 3% Blast Vibration Control As per DGMS limits 7. Safety KPIs (DGMS Aligned) KPI Benchmark Reportable Accident Frequency Rate 0 Near Miss Reporting ≥ 2 per 100 persons/month PPE Compliance 100% Unsafe Acts Closed > 95% within 7 days Safety Training Coverage 100% workforce 8. Cost KPIs (Indian Context) KPI Benchmark Mining Cost (OC Metal) ₹150–350 / tonne Haulage Cost 40–55% of total cost Maintenance Cost 10–15% of revenue Fuel Cost Share 25–35% of OPEX Cost Variance < ±5% 📈 9. Overall Equipment Effectiveness (OEE) Parameter Benchmark Availability 85–90% Performance 85–95% Quality (no re-handling) 98–100% OEE Target 65–75% (Excellent mine) 10. Management Control KPIs (Daily View) Must be seen daily by Head of Mines: ✅ Production vs Plan ✅ Availability & Utilisation ✅ Fuel per tonne ✅ Breakdown hours ✅ Top 3 delay reasons ✅ Safety incidents / near misses HOW TO USE THESE KPIs PRACTICALLY Daily: • Availability, Utilisation, Fuel/t, Production Weekly: • Delay analysis, MTBF, productivity trend Monthly: • Cost/t, OEE, maintenance ratio, safety trends

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

📊 Construction Productivity Benchmarks in Civil Works 📊 In project execution, accurate productivity assessment is critical for: ✔️ Work planning & scheduling ✔️ Resource allocation (labour + equipment) ✔️ Cost control & progress monitoring The following reference sheet provides indicative productivity ranges for major civil activities: 🔹 Excavation & Earthwork – Manual (1.5–2.0 m³/labour/day), JCB (60–80 m³/hr), Poclain (100–1500 m³/hr), Roller Compaction (200–250 m³/day) 🔹 Concrete Works – Manual Mixing (1.5–2.0 m³/labour/day), Placement by Pump (20–30 m³/hr), Shuttering (1.2–1.5 m²/labour/day) 🔹 Reinforcement Work – Cutting, Bending, Tying (100–150 kg/labour/day), Fixing for Columns/Beams (80–120 kg/labour/day) 🔹 Masonry – Brick Masonry (0.2–0.25 m³/labour/day), AAC Block Masonry (0.5–0.7 m³/labour/day) 🔹 Plastering & Finishing – 12 mm plaster (8–10 m²/labour/day), Painting (15–20 m²/labour/day) ⚠️ Disclaimer: These values are guidelines only. Actual site productivity varies based on: 🔸 Site logistics & working conditions 🔸 Labour skill & crew efficiency 🔸 Weather & seasonal factors 🔸 Equipment utilization & maintenance 🔍 Using such benchmarks helps in establishing realistic baselines, monitoring deviations, and optimizing productivity in line with project objectives.

📊 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