Cumulative Flow Work Type Days
Estimates average days in a CFD stage from items and entry rate.
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Cumulative Flow Diagram (CFD): work-in-progress over time
A Cumulative Flow Diagram is a stacked area chart with time on the x-axis and the cumulative count of items on the y-axis, where each workflow state gets its own coloured band (Backlog, Doing, Review, Done). Pick any day and the vertical thickness of a band tells you the WIP in that column; the horizontal distance from the top of Doing to the top of Done gives you the average cycle time. The thing that ties those two together is Little's Law: cycle_time = WIP / throughput.
Most of what you need to know is in the slopes of the lines. When they run parallel, arrival rate โ throughput and the system is stable. A band that keeps widening is a bottleneck, with items piling up faster than they leave, and a top line that pulls away from the rest means demand is outgrowing your capacity. CFDs got their foothold in Kanban software through David J. Anderson, who wrote about them in his 2010 book Kanban: Successful Evolutionary Change for Your Technology Business, and today you'll find them built into Jira, Azure DevOps, GitHub Projects and the like.
Applications
Teams use it for bottleneck detection on a Kanban board, for sizing up flow stability, and for SLA forecasting off cycle-time trends. It also makes solid retrospective evidence when you're arguing for a process change, and it scales up to portfolio-level reporting where each band stands for an initiative or a work type.
FAQ
What does a flat line mean? Nothing moved into that state on that day. Either there was no demand or something upstream was hard-blocked.
How do I spot a bottleneck visually? Look for the band that grows thicker vertically as time passes; that's where work is piling up. The columns just downstream of it tend to look starved and thin.
Should the bands ever shrink? They shouldn't. Cumulative bands only ever push their top edge upward, never down. A band that shrinks means items were removed or reclassified, and that usually points to a data quality problem.
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