What is Cycle Time? Getting started with flow metrics
- 55 Degrees
- 7 days ago
- 4 min read
Do you know how long it actually takes for your team to finish a task from start to finish? Cycle Time measures the total elapsed time from when a work item begins to when it completes.
This means that, depending on how you define start and finish for your context, you can measure the Cycle Time for a whole process or just a portion of it.
How to Calculate Cycle Time
Calculating Cycle Time is straightforward. First, define what “Start” and “Finish” mean for your process. For example, work might start when a ticket moves into an “In Progress” column and finish when it reaches “Done.”
Once those points are clear, you can compute Cycle Time with a simple formula:
Cycle Time = (Finsh Date - Start Date) +1
You might be asking, "But why do you add + 1?" - Fair enough. The +1 accounts for work that starts and finishes within the Cycle Time so no time is left out.
For instance, if a task started on January 1 and finished on January 5, its Cycle Time would be 5 days (inclusive). We add that extra day so that an item started and finished the same day isn’t recorded as 0 days . You never have a zero-length Cycle Time; even a task completed within hours counts as a 1-day Cycle Time
Remember, Cycle Time doesn’t stop when work pauses. If a task gets blocked or sits idle over a weekend, that idle time still counts in the Cycle Time. This makes conversations with stakeholders simpler, as calendar days are relatable to both internal and external audiences.
Why Cycle Time Matters
Cycle Time is one of the four basic flow metrics, along with Throughput, WIP, and Work Item Age. These four flow metrics are baselines metrics that give you some insight into the underlying flow health of a process.
Cycle Time is a critical flow metric for understanding and improving your delivery speed. It directly answers the question: “How long does it take us to complete a work item?” This is hugely important when stakeholders ask, “When will it be done?”
Tracking Cycle Time allows you to answer with data rather than guesses. Lower (and consistent) Cycle Times mean you’re delivering value faster and more predictably to your customers.
Measuring Your Predictability
Looking at Cycle Times helps us understand how predictably we deliver individual work items. If your process generates a wider range of Cycle Time data now than it did in the past then, objectively, you could say that your process has become less predictable than it used to be.
By looking at how long it took you to finish a given percentage of items historically, you can get an idea of how long it may take you to deliver an item in the future - assuming your process hasn't significantly changed. This is best seen on a Cycle Time Scatterplot.
Each dot represents the Cycle Time of a given work item.
Using Cycle Time for Forecasting
One powerful use of Cycle Time data is forecasting single work items. Instead of relying on gut feel or arbitrary story point estimates, you can look at your past Cycle Times to predict how long similar work might take.
A common technique is to determine a percentile from your historical data. For example, by looking at the above Cycle Time Scatterplot we can easy see that 95% of the completed stories had a Cycle Time of 23 days or less.
You can then communicate something like, “Based on our data, there’s a 95% chance we’ll finish an item like this within 23 days.” This probabilistic forecast sets realistic expectations using evidence from your actual performance.
Be careful not to just take a simple average of past Cycle Times, averages can be misleading if you have outliers. One huge delay can skew an average upward, and it doesn’t reflect the variability or risk.
It’s better to use percentile ranges (as mentioned above) or to employ simulation methods. Some teams use Monte Carlo simulations on their Cycle Time data to forecast completion dates. Monte Carlo simulation leverages the distribution of your Cycle Times to run many “what if” scenarios, providing a more reliable and risk-aware forecast than a single average.
A great metric to start with
Cycle Time is often the first flow metric that teams attempt and it is very easy to track — even by hand! All you need is to write down the start date and the end date of a work item and you can calculate cycle time. You can even make your own charts!
Ready to get started with flow metrics?
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