Flow metrics

5 minute read

Flow metrics is a framework that connects business strategy with software delivery, by providing a view of value flow and correlating it with expected business outcomes. In the CloudBees platform, flow metrics helps organizations measure business performance, from customer request to software delivery.

By monitoring flow metrics, you can have improved predictability, bottleneck identification, better risk management, and more cost-effective software delivery.

The CloudBees platform calculates five flow metrics that measure how value flows through a product value stream, as follows:

Table 1. Flow metrics definitions
Metric Description

Work load

Number of flow items currently in progress

Work items distribution

Proportion of each flow item type

Velocity

Number of completed flow items

Cycle time

Time it takes to complete a flow item from start to finish

Efficiency

Percentage of active time vs total cycle time

CloudBees categorizes work tasks for a given product value stream into four flow item types:

  • Feature (a business value)

  • Risk (possible lack of security or compliance)

  • Bugs (a coding defect)

  • Tech debt (a potential blocker to future delivery)

A flow item is considered to be in progress if it is started but not yet complete. The total time for a flow item to complete includes both time spent actively working on the flow item, and time spent in waiting, such as for a code review or approval.

Access and filter flow metrics

Flow metrics data helps you measure the effectiveness of your business in delivering value. Filter on a workflow group and a timeline duration.

To access and filter flow metrics views:

  1. Select the next to Analytics on the left pane.

  2. Select DORA and flow metrics insights.

You are displaying flow metric views, listed according to any sorting, filtering, or searching you have done.

Customize the dashboard

Customize the dashboard to display only the charts and tables that matter the most to you.

To customize the dashboard:

  1. Select the next to Analytics on the left pane, and then select the dashboard.

  2. Select the on the top right of the dashboard.

  3. Select Edit dashboard.

  4. (Optional) Remove a chart or table from the dashboard.

    1. Select the next to the chart or table you want to remove.

    2. Select SAVE.

  5. (Optional) Add back a chart or table to the dashboard.

    1. Select ADD FROM LIBRARY to display a list of the available charts or tables.

    2. Select ADD TO DASHBOARD next to the item you want.

    3. Select APPLY.

  6. (Optional) Rearrange the items on the dashboard.

    1. Select the Move icon on a chart or table you want to move.

    2. Drag the item to a different location on the dashboard.

System health and runs overview
Figure 1. Editing a dashboard, with move and remove icons highlighted.

The dashboard is customized accordingly.

Only those charts and tables with a in their upper-right corner can be edited.

Explore work load, work items distribution, and velocity

security insights
Figure 2. Example Work load, Work items distribution and Velocity charts.
  1. Work load: Review the work load of the team for a selected period. Work load measures the number of flow items currently in progress within a specific value stream, which is an indicator of productivity. An increasing workload suggests excess work in progress, which can lead to future increases in flow item completion time. Frequent software delivery reduces workload and improves cycle time and velocity. In this example, for the selected component and duration, 100 work items are in progress: the sum of bugs, features, risk, and tech debt.

    • The total average number of work items in progress is a link that displays a list of flow item types that includes:

      • Issue ID

      • Issue type

      • Summary

      • Assigned to

      • Issue created

      • Flow item type

  2. Work items distribution: The distribution of completed work item types for the selected duration. Work items distribution measures the proportion of each flow item type, over time, in a given value stream. Monitoring work items distribution helps prioritize specific types of work during different product lifecycle stages. For new business features, this helps to balance the mitigation of risk, bugs, and technical debt..

    flow metrics
    Figure 3. Example Work items distribution chart.

    The graph above indicates that 22% of team effort is on feature and 78% is on defects.

  3. Velocity: Throughput of the value stream, measured as the number of completed flow items within a given time frame. An increase in velocity over time signals that productivity is improving. Tracking velocity is helpful to forecast software delivery rates, and to uncover any problems at an early stage. In the example below, 241 work items are completed, by type.

    flow metrics
    Figure 4. Example Velocity chart.

    The above graph indicates that for the particular date, the team has worked on 70 defects and 31 features, and has not worked on any risk or tech debt items.

Select a chart to explore Workload, Work items distribution, and Velocity

flow metrics
Figure 5. Example Workload Work items distribution and Velocity charts.
flow metrics
Figure 6. Example list of Work item distribution.

Hover the cursor over the lines of the workload and velocity charts to display the data with the start and end dates. Select the red area of the work items distribution chart to display the work items distribution filtered by defect.

Explore cycle time, and work efficiency

Hover the cursor over the lines of the Cycle time and Work efficiency charts to display the data with the start and end dates.

flow metrics
Figure 7. Example Cycle time and Work efficiency charts.
  1. Cycle time: The sum of active work time and wait time. Cycle time gauges time to market by measuring how long it takes to complete a flow item from start to finish, including both active and wait times. As you increase the speed of your software delivery, you reduce the cycle time. With detailed data of flow item completion times, you can predict future cycle times with confidence, and better understand the efficiency of your value stream. This example displays the average time taken to create and then close an issue in Jira.

    flow metrics
    Figure 8. Example Cycle time chart

    The above graph indicates that on average for the selected duration, four days were spent on fixing defects, and seven days on feature.

  2. Work efficiency: The average active work time. The graph below displays that 62% is the average active work time and 38% is the work wait time.

    flow metrics
    Figure 9. Example Work efficiency chart

    The above graph indicates that for:

    • Defects, 76% is active time, with the remaining 24% wait time.

    • Feature, 75% is active time, with the remaining 25% wait time.

    • The 38% Work wait time, 22% is spent on code review, and 15% on blocked.

    • Select the total average active work time link to display a list of flow item type that includes:

      • Issue ID

      • Issue type

      • Summary

      • Active time

      • Waiting time

      • Efficiency

      • Flow item type