Key Production Performance Metrics

Last week, our team delivered 85% of a project's features in under 6 days. Most agencies take weeks or months. Here's how we do it: We track 5 key metrics in our weekly multi-way meetings: • Total items delivered • Average cost per item • Bug percentage in deliveries • Time to close (85th percentile) • Cycle time (from start to completion) All while giving our clients full visibility into everything. Real example from last week: We had one outlier that took 14 days to complete due to an external dependency. This sparked an immediate discussion on improving our dependency management process. That's the difference between good and great teams: → Good teams track metrics. → Great teams use metrics to spark meaningful process improvements. We're obsessed with performance because we have to be. Full visibility and transparency in planning means we can't hide behind arbitrary timelines or vague progress reports. This level of tracking helps us: • Improve predictability • Identify bottlenecks instantly • Maintain quality while moving fast Lesson: Building quality into the process beats trying to add it later. When you combine the right metrics with the right discussions, speed follows naturally.

Most KPIs in manufacturing aren’t wrong. They’re just misunderstood, misused, or misaligned. Here’s how to fix that 👇 1/ OEE (Overall Equipment Effectiveness) – Misused as a benchmark instead of a diagnostic tool → Use it to find bottlenecks, not to chase 100% 2/ First Pass Yield – Misinterpreted as a pure quality metric → Always pair it with rework data for full context 3/ Downtime – Often tracked, rarely categorized → Split into planned vs unplanned & then go deeper (reason codes) 4/ Scrap Rate – Used in isolation → Relate it to production volume and trends over time 5/ Cycle Time – Teams chase faster cycles → Instead, focus on consistency and takt time alignment 6/ Lead Time – Understood only as delivery time → Include every touchpoint, from raw material to finished product 7/ Throughput – Taken as an output metric → It’s also a signal for process flow health 8/ Changeover Time – Seen as “something to reduce” → Use SMED principles to reduce waste, not cut corners 9/ Capacity Utilization – More ≠ better → Balance with actual demand and takt time 10/ Inventory Turnover – Misjudged without a benchmark → Find the right rate for your product mix — not just “higher” 11/ MTBF (Mean Time Between Failures) – Tracked without root causes → Always contextualize with operator, part, and process data 12/ Overall Labor Effectiveness (OLE) – Sounds complex, becomes ignored → Break into Availability, Performance, Quality, simplify and share 📌 Remember: KPIs are tools, not goals. Use them to make better decisions, not just prettier reports. P.S. Liked this? Repost ♻️ and follow me Angad S. for more!

KEY MANUFACTURING KPIs IN THE PQCDSME FRAMEWORK In manufacturing, PQCDSME is a framework that encompasses several key performance indicators (KPIs) critical for measuring efficiency, productivity, and overall performance. Each letter in PQCDSME represents a different aspect of manufacturing performance: P - Productivity: Output per Labor Hour: Measures the amount of product produced per hour of labor, reflecting workforce efficiency. Overall Equipment Effectiveness (OEE): A composite metric that combines availability, performance, and quality to assess the efficiency of manufacturing equipment. Q - Quality: Defect Rate: The percentage of products produced that do not meet quality standards. Lower rates indicate better quality control. First Pass Yield (FPY): Measures the percentage of products that meet quality standards without the need for rework. C - Cost: Cost per Unit: The total cost of production divided by the number of units produced, helping to identify cost efficiencies or areas for reduction. Manufacturing Overhead Rate: Total overhead costs divided by total units produced, indicating how well overhead costs are managed. D - Delivery: On-Time Delivery Rate: The percentage of products delivered to customers on or before the promised date, reflecting the reliability of the manufacturing process. Lead Time: The time taken from the start of the production process to the delivery of the finished product, affecting customer satisfaction and inventory management. S - Safety: Incident Rate: The number of safety incidents per hours worked, helping to assess the safety culture within the manufacturing environment. Days Away from Work: The average number of days employees are away from work due to workplace injuries, indicating the effectiveness of safety protocols. M - Morale: Employee Satisfaction Index: A measure of employee satisfaction and engagement, often gathered through surveys, which can impact productivity and retention. Turnover Rate: The percentage of employees leaving the organization within a specific period, reflecting workforce stability and morale. E - Environment: Waste Reduction Rate: Measures the percentage of waste reduced from manufacturing processes, indicating sustainability efforts. Energy Consumption per Unit: The amount of energy consumed for each unit produced, reflecting efficiency in energy use and environmental impact.

Metrics for planners are NOT the same. This infographic shows key metrics for demand, supply, production and materials planner: Demand planner ↳  WMAPE (forecast error)= Σ|A−F| / ΣA ×100; average % error, volume-weighted ↳  Forecast Bias (%) = Σ(F−A) / ΣA X100; systematic over/under forecast ↳  Demand Variability (CV) = σ(demand) / μ(demand); volatility of demand ↳  Promo Uplift Accuracy = 1 − |Actual promo − Forecast promo| / Actual promo ×100; promo forecast quality ↳  FVA (%) = (Error_naive − Error_model) / Error_naive ×100; value added ↳  NPI Forecast Accuracy =1 − (Σ|A−F| / ΣA) ×100 (for new items); launch forecast accuracy ↳  Sell-In vs Sell-Out Δ (%) = (Sell-in − Sell-out) / Sell-out ×100; channel alignment & pipeline risk Supply planner ↳ Plan Adherence = Executed supply / Planned supply ×100; execution vs. the agreed supply plan ↳ Inventory Turns = COGS / Avg inventory; speed of inventory cycling ↳ Supply Plan Attainment = Supply delivered / Supply committed ×100; fulfillment of supply commitments ↳ Capacity Utilization (%) = Actual output / Rated capacity ×100; use of available capacity ↳ Backorder Rate (%) = Backordered units / Total ordered ×100; unfulfilled demand level ↳ Service Level (OTIF) (%) = Orders delivered OTIF / Total orders ×100; delivery reliability to customers/DCs ↳ Excess & Obsolete (%) = E&O inventory / Total inventory ×100; risk of write-offs Production Planner ↳ Schedule Adherence = Jobs completed on time / Jobs planned ×100; how well the shop follows the schedule ↳ Output vs Plan (%) = Actual output / Planned output ×100; execution against the plan ↳ OEE = Availability × Performance × Quality; effective equipment utilization ↳ Changeover Time = Avg time to switch product/line; setup loss & flexibility ↳ Throughput Rate = Units produced / Time; flow speed of production ↳ Downtime Rate (%) = Unplanned downtime / Available time ×100; lost time from failures ↳ Scrap Rate (%) = Scrap / Total produced ×100; material loss in production Materials Planner ↳ Material Availability Rate = Materials available / Materials required ×100; readiness of components when needed ↳ Supplier On-Time Delivery (%) = On-time deliveries / Total deliveries ×100; supplier reliability ↳ PO Cycle Time = Avg (time from PO to receipt); procurement lead time ↳ Lead Time Variability = σ(supplier lead time); predictability of supply lead time ↳ Supplier Defects (PPM) = Defective parts / Total parts ×1,000,000; incoming quality performance ↳ Material Stockout Rate (%) = Stockout events / Material requests ×100; frequency of missing parts ↳ Safety Stock Compliance (%) = Lines within target / Total lines ×100; adherence to SS policy Any others to add?