{"id":7893,"date":"2026-08-13T11:21:32","date_gmt":"2026-08-13T09:21:32","guid":{"rendered":"https:\/\/getnave.com\/blog\/?p=7893"},"modified":"2026-08-13T11:21:32","modified_gmt":"2026-08-13T09:21:32","slug":"reduce-cycle-time-scrum-team","status":"publish","type":"post","link":"https:\/\/getnave.com\/blog\/reduce-cycle-time-scrum-team\/","title":{"rendered":"Your Team Is Hitting Its Velocity. So Why Is Delivery Taking So Long?"},"content":{"rendered":"
<\/div>\n

For a long time, I thought that if a Scrum team maintained consistent velocity, we had a pretty good indication that the delivery system was working.<\/p>\n

The team was completing roughly the amount of work we expected every sprint. The chart looked stable. Sprint after sprint, the numbers were telling us that things were going well.<\/p>\n

Then I came across a team that completely changed the way I looked at it.<\/p>\n

The Wizards were a 10-person cross-functional Scrum team building a streaming service for public displays. They worked in two-week sprints and their velocity was remarkably consistent. By the measure they were using, this looked like a healthy team.<\/p>\n

Then we looked at how long their work was actually taking to reach completion.<\/p>\n

The story changed immediately.<\/p>\n

Their Velocity Looked Healthy. Their Delivery Times Didn’t.<\/h2>\n

Half of the Wizards’ work was completed in less than 10 days. The other half could take considerably longer, with some items staying in progress for as long as 130 days.<\/p>\n

At the 95th percentile, their cycle time was 76 days.<\/p>\n

Think about what that means for a team working in 14-day sprints. Roughly half of their work was finishing within the sprint. For the rest, there was very little confidence about when it would actually be delivered.<\/p>\n

And yet, their velocity remained consistent.<\/p>\n

That is the problem with looking at velocity alone. It tells you how much work the team completes within a period. It doesn’t tell you how long an individual piece of work takes to move through your system. Even if there’s a big gap between them, your velocity chart will keep reporting good news the whole time. You can hit your velocity sprint after sprint and still be unable to say when your work will be delivered.<\/p>\n

And that’s the one answer your stakeholders are waiting for.<\/p>\n

So we needed to understand what was creating such a huge difference between the work that finished quickly and the work that stayed in progress for months.<\/p>\n

\"The<\/a>
\nThe Cycle Time Scatterplot makes the difference between the typical work and the long-running outliers visible.<\/em><\/p>\n

The answer was in the way the team decided what to work on next.<\/p>\n

The Team Was Prioritizing the Wrong Work Without Realizing It<\/h2>\n

Imagine that you finish your current task and look at the backlog for what to pick up next. There is a small item you know you can complete fairly quickly. Next to it sits a larger, more complicated piece of work that has already been in progress for some time.<\/p>\n

Which one feels easier to pick up?<\/p>\n

This was happening repeatedly with the Wizards. Smaller, easier pieces of work kept moving through the system while the more complex items continued to age.<\/p>\n

Nobody had deliberately decided that those newer items were more important. It was simply the natural consequence of the way the team was pulling work.<\/p>\n

And every time they made that choice, the same thing happened. The easier work moved quickly, which helped maintain their consistent delivery rate. Meanwhile, the older work became older still.<\/p>\n

This explained the strange combination we were seeing in the data. The team could maintain consistent velocity while having wildly inconsistent delivery times.<\/p>\n

So we introduced one simple policy:<\/p>\n

The work item with the highest age moves forward first.<\/p>\n

Before starting something new, the team looked at what was already in progress. Their attention went to the oldest work first.<\/p>\n

\"The<\/a>
\nThe Aging Chart shows every item currently in progress and how old it is today. The items accumulating the most age become the team’s priority. Learn more at getnave.com<\/a>.<\/em><\/p>\n

That sounds like a very small change. It changed the behavior of the entire system.<\/p>\n

The difficult work could no longer sit unnoticed while easier items moved around it. Aging work became visible and demanded attention. Instead of optimizing for keeping everyone busy and completing whatever was easiest to move, the team started focusing on finishing what they had already started.<\/p>\n

Within two months, the 95th percentile of their cycle time dropped from 76 days to 13 days.<\/p>\n

That is a reduction of more than 80%.<\/p>\n

\"The<\/a>
\nThe same Cycle Time Scatterplot two months later. The 85th percentile sits at 11 days and the 95th at 13 days.<\/em><\/p>\n

And here is the part I find most important.<\/p>\n

They didn’t add people. They didn’t increase working hours. They didn’t change the type of work they were doing.<\/p>\n

They changed the way they managed the work.<\/p>\n

Start With the Oldest Work Tomorrow<\/h2>\n

If your team is completing a consistent amount of work but you are still struggling to answer when individual items will be delivered, take a look at the age of your work in progress.<\/p>\n

Then try one experiment.<\/p>\n

The next time someone becomes available, don’t automatically pull another item from the backlog. Look at everything that is already in progress and identify the oldest item.<\/p>\n

Ask: What is preventing this work from moving forward, and what can we do about it today?<\/p>\n

Make that your explicit policy for what happens before new work enters the system. Then observe what happens to your cycle time distribution over the following weeks.<\/p>\n

You may discover that the biggest opportunity to improve your delivery performance has very little to do with increasing your capacity. It may simply be sitting inside the work you have already started.<\/p>\n

<\/div>\n

This is exactly the kind of behavior we make visible in Nave. The Cycle Time Scatterplot helps you see how long individual work items take to complete and identify the outliers that are driving your delivery times up. The Aging Chart then brings that same perspective to the work that is still in progress, so you can act before those items become your next 76-day outliers. You can explore your own delivery data at getnave.com<\/a>.<\/p>\n

Consistent velocity can make a delivery system look healthy. How long your work actually takes to reach your customer tells you whether it is.<\/p>\n

I’ll see you next week, same time and place for more managerial goodness. Bye for now!<\/p>\n

<\/div>\n
<\/div>","protected":false},"excerpt":{"rendered":"

For a long time, I thought that if a Scrum team maintained consistent velocity, we had a pretty good indication that the delivery system was working. The team was completing roughly the amount of work we expected every sprint. The chart looked stable. 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