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Cycle Time

Cycle Time

Owned by Shir Koretski
Jan 09, 2025
5 min read

Overview

Cycle time measures the total duration it takes to resolve or complete an issue, from its creation to its closure. Understanding cycle time helps teams identify bottlenecks, optimize workflows, and enhance overall efficiency in the issue resolution process. By analyzing cycle time, teams can pinpoint areas where additional support or process improvements are needed, leading to faster issue resolution and improved project timelines.

Description

Cycle time refers to the total time spent on an issue from the moment it is created (or reported) until it is resolved and closed. It encompasses every step involved in resolving the issue, including:

  • Analysis: Understanding the issue's root cause.

  • Development: Fixing the issue or implementing the change.

  • Testing: Ensuring the fix works as expected.

  • Deployment: Moving the solution to production.

How is Cycle Time Calculated?

Cycle time is calculated by subtracting the issue's creation timestamp from the timestamp when it is closed or resolved. This provides the total time taken for the entire issue resolution process.

Formula:
Cycle Time = Resolution Time - Creation Time

For example, if an issue is created on January 1st and resolved on January 5th, the cycle time is 4 days.

This data is collected from your issue tracking or project management tool (e.g., Jira, GitHub) and provides a clear picture of how long it typically takes to address issues.

How it’s Generated?

We generate this data by taking each issue that is not marked as closed in all services that provide issue tracking [ex.Jira, ADO], and record the average time that each issue is in progress. The chart defaults to average but we recommend aggregating the data by median in case of skewed data distribution by outliers.

Questions You Can Answer with Cycle Time Data

  • What is the average cycle time for different issue types?
    Understanding how long it takes to resolve different types of issues (bugs, feature requests, improvements) can help allocate resources more effectively and set more realistic expectations.

  • Are there any bottlenecks or delays in the issue resolution process?
    By analyzing cycle time, you can identify stages in the process that consistently take longer than expected, helping you target inefficiencies and address bottlenecks.

  • How has issue resolution time changed over time?
    Tracking cycle time over weeks or months can reveal trends, showing whether your team's efficiency is improving or deteriorating.

  • What factors correlate with longer cycle times?
    Analyzing cycle time in relation to factors such as issue complexity, team capacity, or external dependencies can help identify why certain issues take longer to resolve.

  • How does cycle time impact customer satisfaction or project timelines?
    By understanding the connection between cycle time and project deadlines, you can better predict timelines and improve stakeholder communication. Longer cycle times may delay customer deliverables or reduce satisfaction.

Key Takeaways from Cycle Time Data

  • Efficiency & Process Optimization: Monitoring cycle time helps pinpoint inefficiencies in the workflow. By addressing bottlenecks or delays, teams can streamline their processes, making the resolution of issues faster and more efficient.

  • Resource Planning & Workload Management: Cycle time analysis provides insights into how much time each issue takes, helping teams allocate resources effectively. It ensures that workloads are balanced, preventing burnout while also ensuring deadlines are met.

  • Realistic Expectations & SLA Compliance: Tracking cycle time helps establish realistic service level agreements (SLAs) and delivery timelines. By understanding the average time to resolve issues, you can set more accurate expectations with stakeholders and customers, ensuring you meet your commitments.

  • Continuous Improvement & Performance Evaluation: By analyzing cycle time over time, teams can evaluate the effectiveness of process changes and improvements. Identifying trends allows teams to make data-driven decisions, optimize workflows, and continuously refine practices for better performance.

General Filters

Use these filters to narrow down the information you want to see. After making any updates, make sure you click ‘update’ to have the changes reflect on the chart below.

 Issues Closed Between: For Issue Cycle Time, we recommend using monthly or greater. To visualize change over time, use at minimum, the quarterly date range. 

 Descendants of: Limit the data by JIRA or ADO projects. You can also filter by project or repo. 

 Issues Assigned to: You can narrow down your search by tag labels as well as specific individuals.

 Advanced Filters: Filter your data using fields and properties located in your project management tool (e.g. JIRA, ADO). Make sure to remove ‘Inactive’ or ‘Removed’ issue states.

TIP: Make sure to apply the changes you made to General Filters by clicking ‘Update’.

Chart Settings

You can use chart settings to format how the chart displays your data.  This is powerful when it comes to creating data visualizations to support the story you’re trying to tell.

Advanced Settings

Set up Issue Cycle Time on a month-by-month basis / quarter-over-quarter

 Cycle Time grouped by month:

Cycle Time grouped by Quarter:

Measure Cycle Time by Issue Type

 Measure Cycle Time by Story Points

 Track cycle times for parent cards

  • Under Chart Settings > Filtered to: > select ‘Include All Item Types’. 

This will allow parent cards to display in the chart (see Epic and Feature in legend).

Conclusion

Cycle time is a critical metric for understanding how efficiently your team resolves issues. By tracking this data, teams can identify delays, improve workflows, and better manage resources. Cycle time analysis not only helps with internal process optimization but also enables teams to deliver on their commitments to customers more consistently and on time.