What’s the approach to analyze cycle time in Kanban?

I didn’t want to dive into the discussion of cycle time vs lead time right away, so let’s establish some definitions before we proceed.

Throughout this article, I’ll refer to cycle time as the time between when your team starts working on their tickets until they finish working on them.

Analyzing cycle time brings many tangible benefits. It helps uncover opportunities to cut waste in your processes, ultimately improving your time to market and your ability to make reliable delivery commitments based on data.

Today, I’ll walk you through a simple cycle time analysis process. This method is specifically crafted to drive your improvement initiatives forward. So, grab your favorite beverage, take a seat, and let’s dive right in!

3 Steps to Analyze Cycle Time in Kanban

The 3-step guideline to analyze cycle time in Kanban comprises analyzing your cycle time distribution, designing your improvement initiative, and implementing a regular cadence. Let’s delve into each step of the process in detail.

#1 Analyze Your Cycle Time Distribution

When it comes to cycle time analysis, the Cycle Time Histogram is one of the most powerful tools at your disposal.

The chart shows the frequency distribution of your cycle time. The horizontal axis displays your delivery times, and the vertical axis shows the number of work items with the same cycle time.

Frequency Distribution Of Cycle Time Histogram | Example

In the histogram above, we can see that this team has completed 29 tickets in 1 day, 11 tickets in 2 days, 3 tickets in 3 days, and so forth.

By analyzing the frequency distribution of your cycle time, you’ll be able to determine whether there is too much variability in your process.

A wide spread indicates that your cycle time varies significantly and your workflow is inconsistent.

The histogram above displays a fat-tailed distribution. Systems with fat-tailed distributions are unstable and unpredictable.

Now, if you’re using your past performance data to forecast your delivery dates, having that picture above will reveal how fragile your system is. Here’s why:

The accuracy of your forecast strongly depends on the shape of your distribution.

In order to decide whether you can rely on your probability forecast, you should determine whether your distribution is thin-tailed or fat-tailed. David Anderson and Teodora Bozheva have elaborated on that study in their book, The Kanban Maturity Model, 2nd Edition.

In summary, simply divide your 98th percentile by your 50th percentile. If the result is greater than or equal to 5.6, this means that your frequency distribution is fat-tailed. If the result is less than 5.6 – it’s a thin-tailed distribution.

And to keep things simple, the type of your cycle time distribution will be indicated directly below your chart’s name at Nave.

#2 Design Your Improvement Initiative

Now that we know what the distribution looks like, we want to focus on reducing the spread of the width and look into the outliers – the work items on the tail of your distribution that have taken significantly longer to finish.

Managing blockers effectively is the first step towards revealing the obstacles that cause delays and hinder your delivery speed. One of the practices that drive continuous improvement is blocker clustering.

The blocker clustering technique enables you to track the impact that blockers have on your performance and prioritize those that affect your delivery times the most.

The goal is to prioritize the most impactful blocker that causes delays in your delivery. The trick here is to count the total days your work spent blocked and split that data into each of the blocked reasons you have identified.

Here’s where the Cycle Time Breakdown Chart comes into play.

Cycle Time Breakdown Chart by Nave | Illustration

The above cycle time breakdown shows that 64% of the blocked time is being caused by Expedite Requests. This group is the most impactful one of the blockers that cause delivery delays.

You have now identified the one impediment you should put your focus on right now to improve your delivery speed and see results quickly.

And here is why this is so important. When you use this approach to identify your next step, you make a strategic decision.

Remember, you can’t solve all of your problems at once. You just can’t. You want to invest your labor and resources wisely to maximize your improvement potential. Always remember the most productive way to move the needle is to focus on one thing at a time.

Here is a step-by-step guide on how to implement blocker clustering

#3 Implement Regular Cadence

Integrate cycle time analysis into your regular retrospectives; ensure it’s a scheduled part of the agenda and follow through with these steps.

Pro tip: use an AI meeting assistant to handle the paperwork for you!

Next time, start the meeting by following up with the action plan. Then, repeat the process.

If your efforts are paying off, you’ll start observing how the tail of your distribution keeps shrinking, and at some point, it becomes a thin-tailed one.

Cycle Time Histogram by Nave | Illustration

Remember, the reliability of your forecasts will always depend on the stability of your system.

Transforming your fat-tailed distribution to a thin-tailed one is a real challenge, but it’s essential to meeting your customer’s expectations. The secret to improving predictability is consistency. So make sure to adopt that practice and stick to it during your retrospectives.

And here is your action item: If you haven’t started using Kanban Analytics yet, now’s the time. Give it a try – it’s free for 14 days, no credit card required

By following these three steps, you’ll build a system that continuously drives opportunities for improvement, ultimately reducing your time to market.

Remember, consistency is key. Keep executing the guidelines regularly for long-term results.

I hope this has been helpful! Please share this article with your teammates through your social media channels, I’d highly appreciate it! I’ll see you next week same time and place for more managerial insights! Bye for now.

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