Top 40 Engineering KPIs and Metric Examples for 2025

Modern engineering teams rely on clear metrics to guide decisions, improve processes, and align with business goals. ​​In fact, tracking the right engineering KPIs is essential to understand team performance, product quality, and delivery speed.

Read this guide to learn more about the top 40 Engineering KPIs and metrics, along with their formulas.

What are Engineering Metrics?

Engineering metrics are measurable indicators that track the performance, quality, and efficiency of engineering activities. They help teams evaluate progress, identify bottlenecks, improve processes, and align technical outcomes with business goals.

Engineering KPIs and metrics apply to various areas, such as software development, R&D, manufacturing, project management, consulting, and financial operations.

Why Track Engineering Metrics?

Tracking engineering metrics helps teams understand how well their processes, systems, and resources perform. These metrics offer a clear picture of efficiency, quality, and cost-effectiveness across engineering functions.

• Process efficiency: Metrics highlight delays, rework, or inefficiencies in workflows so teams can improve them.
• Product quality: They help detect defects, failures, or downtime that can affect customer satisfaction or safety.

• Resource utilization: Metrics show how well teams, equipment, or materials are being used to meet demand.
• Cost control: They track expenses and returns to ensure engineering efforts stay within budget and deliver value.
• Project delivery: Metrics help monitor timelines and ensure milestones are met without overruns.
• Strategic alignment: They ensure engineering outcomes support broader business goals like innovation, speed, and profitability.

Top 40 Engineering KPIs and Metrics

Whether you’re working in software, R&D, manufacturing, or consulting, the right metrics help you track progress, find areas for improvement, and make better decisions. Below are some of the best examples of engineering metrics.

Software Engineering Metrics Examples

Software engineering metrics provide insight into the development process, code quality, and team productivity. These metrics indicate code quality, team efficiency, and delivery performance.

1. Code Churn

High levels of code churn indicate instability or frequent rework in a codebase. While some churn is natural during early stages, high churn indicates poor planning or unclear requirements. Monitoring this helps maintain code quality and team efficiency.

Determine code churn by:

2. Number of Bugs

This measures how many defects are found during or after development. Tracking bugs helps teams understand software quality trends and identify areas needing better testing or architecture.

Calculate the number of bugs with this formula:

3. Code Cycle Time

This measures how long it takes for a feature or bug fix to go from the first commit to production. A shorter cycle time indicates an efficient CI/CD pipeline, while longer cycles may indicate issues in development.

To calculate code cycle time:

4. Comments per Pull Request

This metric measures the average number of review comments on each pull request. A healthy number of comments suggests that team members actively review each other’s code, ask questions, and give feedback.

Determine Comments per Pull Request by:

5. Number of Releases

This metric tracks how often new product or system versions are deployed to users. A higher number of releases typically means the team is shipping updates, features, or fixes more frequently. It reflects how quickly the team can move from development to delivery.

Calculate the Number of Releases with

6. Average Downtime

This metric measures the average time a system remains unavailable due to outages, failures, or maintenance. Tracking average downtime helps teams identify recurring issues, assess the impact of incidents, and take steps to improve system stability and response times.

To calculate average downtime:

7. Code Coverage

Code coverage shows the percentage of the codebase covered by tests. While 100% coverage isn’t always necessary, maintaining strong coverage helps prevent regression bugs and builds confidence in system stability.

To calculate code coverage, use:

8. Developer Delta

This metric tracks how much value a developer adds over time, factoring in contributions, speed, and code quality. It highlights growth and productivity at the individual level and can support performance reviews and tutorials.

Calculate Developer Delta with:

Research and Development Metrics Examples

R&D metrics show how effectively an organization invests in innovation, manages resources, and delivers long-term value. They help measure project efficiency, cost control, and the balance between new development and ongoing support.

9. R&D Cost/Benefit Ratio

This metric compares the cost of R&D efforts to the benefits they generate, such as revenue, efficiency gains, or market impact. A strong ratio means the organization is getting good value from its R&D investment. A weak ratio may indicate that resources are being spent on low-impact projects or that the scope needs realignment.

Here’s how to determine R&D Cost/Benefit Ratio:

10. Existing Product Support Cost

This metric measures the total cost of maintaining and supporting older or legacy products. High support costs can reduce the resources available for new development and may indicate a need to modernize or retire certain products. Tracking this helps teams allocate resources more effectively and balance support with innovation.

Calculate product support cost using:

11. Engineering Effectiveness

This metric measures how many engineering projects meet their intended goals compared to those that fall short. It reflects how well teams execute plans, manage risks, and align work with strategic objectives. Higher effectiveness indicates stronger delivery and better use of engineering resources.

You can calculate engineering effectiveness by applying:

12. Payback Period

This metric measures how long it takes for an R&D investment to recover its initial cost through generated returns. A shorter payback period means the investment becomes profitable more quickly, which helps in budgeting, evaluating project risk, and making future investment decisions.

Determine Payback Period by:

13. Net Present Value (NPV)

This metric calculates the current value of expected future cash flows from an R&D project, minus the initial investment. It helps assess the long-term financial viability of a project. A positive NPV means the project will likely generate more value than it costs, making it a worthwhile investment.

Net present value is measured using:

14. Internal Rate of Return (IRR)

This metric estimates the rate of return at which a project’s net present value (NPV) becomes zero. It helps evaluate the profitability of R&D investments over time. A higher IRR indicates a more attractive project, especially when comparing multiple opportunities within the engineering portfolio.

There’s no simple formula for IRR, as it involves solving for the rate that makes NPV zero. However, it can be easily calculated using tools like Excel or Google Sheets with the IRR() function.

Manufacturing and Lean Engineering Metrics Examples

These metrics track operational performance, process efficiency, and equipment usage. They help identify bottlenecks, reduce waste, and support continuous improvement to ensure consistent, high-quality output.

15. Throughput

Throughput measures the number of units a production system produces over a specific period. It checks how effectively a manufacturing process operates. Monitoring throughput helps to identify issues and supports continuous improvement initiatives.

To calculate throughput, use the following formula:

16. First Pass Yield

This metric shows the percentage of products that meet quality standards on the first try without requiring rework. A high first pass yield reduces waste, minimizes production costs, etc.

First pass yield is typically calculated as:

17. Cycle Time

It measures the total time taken to produce a single item, from the beginning of production to completion. It’s essential for identifying issues and inefficiencies. Shorter cycle times indicate streamlined, lean operations, while longer ones may suggest delays, excessive handoffs, or resource constraints.

You can calculate cycle time by applying

18. Production Attainment

This metric compares actual production output to the planned target. It helps identify issues caused by staffing shortages, material delays, or equipment inefficiencies. High attainment reflects accurate planning and operational execution.

To measure production attainment, use:

19. Capacity Utilization

This metric assesses the extent to which available manufacturing capacity is being effectively used. Low utilization shows excess capacity and lost revenue potential, while over-utilization can lead to quality issues, increased wear and tear, or employee burnout.

Capacity utilization is measured using:

20. Changeover Time

This metric measures the time it takes to switch a production line or machine from making one product to another. High changeover time leads to longer downtime and reduced productivity. Reducing it helps improve manufacturing flexibility, especially when handling small batches or frequent product changes.

To calculate changeover time, apply:

21. Machine Downtime Rate

This metric measures the percentage of time machines are unavailable due to breakdowns, maintenance, or setup. A high downtime rate reduces overall equipment efficiency and disrupts production schedules.

Machine downtime rate is calculated by:

22. Percentage Planned Maintenance

This metric shows how much of the total maintenance activity is planned in advance rather than being reactive. A high percentage indicates a proactive approach that reduces unplanned downtime, improves equipment reliability, and supports better cost control. It also contributes to higher overall equipment effectiveness (OEE) and longer asset life.

To calculate the percentage of planned maintenance, use:

23. Avoided Cost

This metric estimates the expenses saved by preventing potential failures, defects, or inefficiencies through proactive actions. It reflects the financial benefit of process improvements, risk reduction, and quality initiatives. While often based on projections, tracking avoided costs helps justify investments in operational efficiency.

Avoided cost is typically determined by:

Project Management Metrics Examples

These metrics assess how effectively engineering projects are planned, executed, and completed. They track cost efficiency, schedule adherence, and task progress.

24. Cost Performance Indicator (CPI)

CPI measures how efficiently a project is using its budget. It is calculated as the ratio of earned value to actual cost. A CPI above 1 means the project is under budget, while a CPI below 1 indicates cost overruns.

To calculate CPI, apply this formula:

25. Schedule Performance Indicator (SPI)

SPI evaluates how well a project progresses against its planned schedule by comparing earned value to planned value. An SPI above 1 means the project is ahead of schedule, while below 1 indicates delays. Monitoring SPI helps teams adjust timelines and resources to stay on track.

The schedule performance indicator is calculated using:

26. Engineering-on-Time Delivery

This metric measures the percentage of engineering tasks or deliverables completed by their planned deadlines. It reflects how reliably teams meet schedules and helps improve planning accuracy, resource coordination, and confidence among stakeholders.

To measure engineering-on-time delivery, use:

27. Outsourcing Rate

This metric checks the proportion of work delegated to third-party vendors. It helps understand the internal capacity, identify cost-saving opportunities, and balance risk.

The outsourcing rate is typically calculated as:

28. Project Timeline

This metric compares the actual duration of a project to its original planned schedule. Deviations from the timeline can reveal planning gaps, execution delays, or resource issues. Tracking this helps teams identify what caused delays and improves scheduling accuracy for future projects.

To calculate the project timeline metric, use:

29. Project Margin

This metric measures a project’s profitability by comparing its revenue to the total cost. A higher margin indicates that the project was executed efficiently with good cost control, while a lower margin may signal overspending or pricing issues that need attention.

To calculate project margin, use the following formula

Consulting Engineering Metrics Examples

These metrics assess how well an organization performs in client acquisition, retention, revenue generation, and workforce utilization. They help track business growth, client satisfaction, and how effectively teams deliver billable work.

30. Number of Clients

This metric tracks how many clients an engineering or consulting firm actively serves. A growing client count signals successful outreach, competitive services, and brand visibility. Monitoring this metric helps assess the overall health of business development efforts.

The number of clients is measured by:

31. Number of New Clients

This metric tracks how many new clients are acquired over a specific period. It reflects the success of marketing efforts, sales outreach, and business development strategies. A growing number of new clients indicates strong brand visibility, market demand, and competitive positioning.

To calculate the number of new clients, use this formula:

32. Percentage of Revenue from Existing Clients

This metric measures how much of a company’s revenue comes from existing clients. A high percentage suggests strong client relationships, consistent service quality, and customer loyalty. It also indicates stable income with lower acquisition costs.

This percentage is calculated using:

33. Repeat Business Rate

This metric indicates how often clients return for additional projects, making it a strong signal of customer satisfaction and service quality. A high rate shows that clients trust the firm’s work and value the relationship. It also reduces client acquisition costs and supports sustainable, long-term growth.

To determine the repeat business rate, apply this:

34. Utilization Rate

It measures how much of your team’s available working hours are spent on billable tasks. It reflects employee productivity, project efficiency, and overall business profitability. This metric is important for consulting firms where billing hours directly influence revenue.

Utilization rate is typically measured using:

35. Average Fee Per Hour

This metric measures the average revenue earned for each billable hour. It helps firms assess the profitability of consulting work, evaluate pricing strategies, and understand how clients value their expertise. A higher average hourly fee often reflects strong demand, specialized skills, and effective service delivery.

To calculate the average fee per hour, use

Financial Engineering Metrics Examples

These metrics assess how effectively an organization manages its financial resources, profitability, and risk. They support budgeting, capital planning, and long-term strategy by giving insight into liquidity, financial health, and return on investment.

36. Operating Cash Flow

This metric shows the net cash generated from a company’s main business operations. It reflects the company’s ability to maintain and grow its operations using internally generated funds, without relying on external financing. Strong operating cash flow indicates healthy day-to-day financial performance.

Operating cash flow is calculated by:

37. Break-Even Point (BEP)

This metric identifies the sales volume at which total revenue equals total costs, meaning the business makes no profit or loss. It also shows when a business or product line will become profitable.

To calculate the break-even point, use the following:

38. Net Profit Margin

This metric measures the percentage of revenue that remains as profit after all operating expenses, taxes, and interest are deducted. A high net profit margin indicates effective cost management, pricing strategy, and overall operational efficiency.

Net profit margin is typically measured using:

39. Interest Coverage Ratio

This metric measures a company’s ability to cover its debt interest payments using its earnings before interest and taxes (EBIT). A higher ratio indicates strong financial stability and a lower risk of default, showing that the company can comfortably meet its debt obligations.

To calculate the interest coverage ratio, use:

40. Return on Assets (ROA)

ROA measures how effectively a company uses its assets to produce net profit. A higher ROA indicates better asset utilization when generating earnings. This metric is beneficial for comparing performance across asset-intensive businesses or tracking year-over-year efficiency.

Return on assets is calculated by:

How BrowserStack Helps Track and Improve Engineering Metrics

BrowserStack provides a unified platform to test across browsers and devices, and also gives engineering leaders deep visibility into their team’s testing efficiency and quality outcomes. Quality Engineering Insights (QEI) goes beyond test execution by helping teams track, analyze, and act on the metrics that matter across tools and pipelines.

Here’s how BrowserStack helps measure engineering performance through QEI:

• Executive Dashboard: Use a single pane to view all key quality metrics like coverage, defect leakage, and cycle time across teams and tools.
• Focus Areas: Spot critical issues affecting stability or speed to take quick, targeted action during development or release cycles.
• Key Wins: Track quality improvements across sprints or releases to measure where engineering effort delivers measurable outcomes.
• Custom Views: Create role-based dashboards to track the most relevant metrics for specific teams or projects.
• Tool Integrations: Sync data from CI/CD, test management, automation, and issue tracking tools to get a unified view without switching platforms.
• Shareable Reports: Give engineering and business stakeholders clear visibility into test ROI and quality trends through exportable dashboards.

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Conclusion

Tracking the right engineering metrics helps identify bottlenecks and guide operational improvements. But metrics only add value when they’re tied to tangible goals. Instead of tracking everything, focus on the KPIs that influence quality, delivery, and long-term sustainability, and revisit them regularly as your team or product evolves.

BrowserStack helps teams act on these metrics by bringing all quality signals into one view with Quality Engineering Insights (QEI). It connects data across your CI, test management, automation, and issue tracking tools to highlight where quality efforts are working, where risks lie, and what needs attention.

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