Aging Chart
If completed-item outliers are driving delivery times up, check work still in progress before it becomes the next outlier.
Cycle Time Scatterplot
What it answers
How long does work usually take?
See how long completed work took and whether cycle time is becoming more predictable.
Example
How to read it
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A completed work item
Each dot represents one work item completed during the selected period.
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Cycle time
The higher the dot is on the vertical axis, the longer the item took to complete.
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85th percentile
This line marks the cycle time that 85 of every 100 items came in under.
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Trendline
The dotted green line shows how cycle time has been moving over the selected period.
See the Cycle Time Scatterplot in action
Analyze individual work items
Each dot represents a completed work item. Click it to see the work item type, a direct link to it on your management platform, and the time spent in each process state.
Work items with the same cycle time and delivery date are clustered together.
Use the Blocked Work toggle to show or hide blocked items. It is enabled by default. When turned off, blocked items no longer appear as red dots.
Click a dot and expand the list to analyze each work item individually.
Use percentiles to set delivery expectations
The dotted horizontal lines are percentile lines. Use them to establish service level agreements and define the probability of meeting different commitment points.
A higher percentile means a greater chance of completing a work item on time.
Filter by work item type or class of service to provide different SLAs.
Analyze cycle time by process state
The Process Metrics widget shows how long work spends in each state and how predictable that time is.
Compare percentiles to identify states where work is consistently delayed or highly variable.
Use the Blocked issues control to choose All time, Blocked time, or Time without blockers. The scatterplot updates to match your selection, and the Process Metrics table recalculates using the filtered data.
A high 85th or 95th percentile means work often gets stuck in that state for long periods. A large gap between the 50th and 95th percentile means some tasks move quickly while others take much longer.
Monitor your cycle time trend
The dotted green line is your cycle time trendline. It uses polynomial regression to show how cycle time has moved over the selected period.
If the trendline goes up, your team is struggling to deliver results.
Consider explicit process policies to swarm work items that pass certain percentile lines.
Observe how your percentiles change over time
The Percentiles widget on the Cycle Time Scatterplot shows how your cycle time percentiles change over time.
Ideally, the lines stay close together while moving slightly down. This means the workflow is efficient and the team delivers value at a consistent pace.
If the lines go up, review work in progress and apply WIP limits your team agrees to respect.
Export the filtered data shown in this chart as a CSV instead of the raw dashboard data.
What to look for
An upward triangle
A triangle with an increasing gradient means cycle times are getting longer over time.
Wide percentile gaps
The wider the gap between percentile lines, the less predictable your system is.
Isolated outliers
A tight cluster with a few dots striking out on their own points to extreme outliers.
What decisions it helps you make
- How to reduce delivery times to an optimal level for your team.
- Which SLAs to provide for each work item type or class of service.
- Whether to use explicit process policies to swarm work items that pass certain percentile lines.
- Whether to apply WIP limits to workflow steps and agree as a team to respect them.
- Which outlier work items to examine more closely for process impediments.
Related charts
Cycle Time Breakdown
If you see a bottleneck or high variability, use the breakdown to identify areas for improvement.
Cumulative Flow Diagram
For deeper analysis of your process, look at the Cumulative Flow Diagram next.
See these analytics with your own delivery data.
Connect your existing delivery data and start exploring your flow.