Standard : Build and Deploy Wait Time

Description

Build and Deploy Wait Time measures the elapsed time between when code is ready (e.g. merged to main) and when it is successfully built, tested, and deployed into a production or staging environment. It reflects the non-value-adding delay in the delivery process caused by slow pipelines, manual gates, or batch processing.

This metric helps identify friction after development is complete and before value is realised by end users.

How to Use

What to Measure

  • Time from code commit or PR merge to successful deployment completion.
  • Optionally track wait time by pipeline stage (build, test, deploy) or environment (dev, staging, prod).

Formula

Build and Deploy Wait Time = Deployment Completion Time – Merge Time

Break into:

  • Queuing delay before pipeline begins
  • Duration of each pipeline stage
  • Manual approval time

Instrumentation Tips

  • Use timestamps from VCS (e.g. GitHub) and CI/CD tools (e.g. Azure DevOps, GitLab CI).
  • Add tracing or logging across pipeline stages.
  • Segment metrics by team, service, or type of work for diagnostic value.

Why It Matters

  • Highlights delivery friction: Unnecessary wait time delays value delivery and learning.
  • Improves flow efficiency: Lean delivery requires low handoff and delay waste.
  • Focuses improvement: Pinpoints where automation or policy reform is most needed.
  • Enables safe speed: Faster build-to-deploy enables rapid, reliable iterations.

Best Practices

  • Streamline CI/CD pipelines to reduce stage duration and queuing.
  • Remove unnecessary approvals and replace them with automated gates.
  • Use parallelisation and caching to improve test and build times.
  • Reduce WIP to avoid long queues of changes awaiting deploy.
  • Visualise wait time as part of your value stream dashboard.

Common Pitfalls

  • Measuring from ticket creation rather than from "ready" code.
  • Failing to distinguish between pipeline runtime and idle queue time.
  • Over-optimising for speed at the cost of observability or test completeness.
  • Relying on manual handoffs without SLA or visibility.

Signals of Success

  • Build and deploy wait times shrink over time as pipelines improve.
  • Pipelines complete consistently and predictably across environments.
  • Developers get fast feedback after merging code.
  • Teams view wait time data regularly and act on outliers.
  • [[Lead Time for Change]]
  • [[Pipeline Reliability Score]]
  • [[Manual Intervention Rate in Pipelines]]
  • [[Deployment Frequency]]
  • [[Feedback Loop Time]]