Enter Delivery Data
Use one reporting period, such as 7, 14, or 30 days. Results appear above this form after submission.
Example Data Table
| Input | Example Value | Why It Matters |
|---|---|---|
| Reporting period length | 30 days | Defines the normalization window for frequency and throughput. |
| Total deployments | 18 | Drives deployment cadence and average deployment interval. |
| Changes delivered | 72 | Helps estimate throughput and batch size per release. |
| Commits included | 210 | Shows code volume shipped within each deployment batch. |
| Total lead time | 864 hours | Used to calculate average change lead time. |
| Failed deployments | 2 | Feeds change failure rate and release success rate. |
| Total recovery time | 9 hours | Measures resilience through mean time to restore. |
| Pipeline success | 149 of 160 | Shows delivery system reliability before production release. |
Formula Used
These formulas combine speed, quality, and recovery signals. Benchmark labels are practical internal bands for fast comparison.
How to Use This Calculator
- Choose one reporting window, such as a sprint, month, or quarter.
- Enter total production deployments completed during that period.
- Add shipped changes, included commits, and cumulative lead time hours.
- Record failed deployments and total recovery hours from related incidents.
- Enter pipeline runs, successful runs, rollbacks, and hotfix deployments.
- Press the calculation button to show the result block above the form.
- Review the benchmark labels, then compare weak and strong areas.
- Use the CSV and PDF buttons to save or share results.
FAQs
1. What does this calculator measure?
It measures deployment speed, release stability, pipeline reliability, recovery performance, and batch efficiency. Together, these values help software teams understand whether delivery is fast, safe, and sustainable.
2. What should count as a deployment?
Use completed production releases only. Exclude local tests, staging pushes, or partial internal dry runs unless they directly represent production delivery for the period being analyzed.
3. How is lead time interpreted here?
Lead time represents total elapsed hours from commit or approval to successful production release. Lower values usually indicate less waiting, smaller batches, and smoother delivery flow.
4. What qualifies as a failed deployment?
A failed deployment is a release that caused a service issue, rollback, outage, severe bug, emergency fix, or customer-visible degradation requiring remediation after deployment.
5. Why track rollback and hotfix rates?
They reveal instability hidden behind raw deployment counts. A team can deploy frequently yet still struggle if too many releases require urgent correction or reversal.
6. Is the overall delivery score an industry standard?
No. It is a weighted summary score designed for quick internal comparison. Teams can adjust the scoring bands and weights to match their engineering context.
7. Which reporting period works best?
Monthly or sprint-based windows often work well because they balance trend visibility and fresh operational data. Pick one period and apply it consistently for comparisons.
8. Can I use this for team or service comparisons?
Yes, as long as teams follow the same counting rules. Standardized definitions for deployments, failures, lead time, and recovery make cross-team comparisons far more reliable.