Lead Time vs. Cycle Time: What’s the Difference?
A key part of effective agile software team leadership is focusing on the right metrics. There are lots of behaviors that you can measure, but knowing that you’re measuring something doesn’t mean you know what that measurement tells you. It also doesn’t tell you how or when to push to improve your team’s measurements.
Two commonly measured metrics are lead time and cycle time. In this post, we’re going to spend some time talking about both of them and how they’re different, how they work together, and what you should learn from measuring them.
What Do They Measure?
Both lead time and cycle time are fairly similar. Lead time (or time to value) is how long it takes something to arrive after a customer asks for it. Cycle time is how long it takes your team members to deliver something after they start working on it. In both cases, higher values mean slower overall delivery of software features and bug fixes. However, you shouldn’t try to optimize both metrics the same way, nor should you even think about them the same way. Let’s dive into each and discuss some nuances that aren’t immediately clear.
The Value of Lead Time As a Metric
For a lot of executives, the value of lead time as a metric is obvious. It’s a customer-facing metric that tells you, on average, how long it takes customers to get what they want. Most customers tend to be pretty happy when they get what they’re asking and paying for.
But when we’re looking at overall lead time, there are some hidden dangers that we should be aware of. A lower lead time doesn’t always mean that things are going better, and your focus shouldn’t always be on reducing lead time when you might find that there are more valuable ways to spend that time.
When Lead Time Doesn’t Tell the Whole Story
While lead time is a customer-facing metric, it’s important to know what exactly your lead time is measuring. No team measures lead time against their entire bank of stored tickets. If your team is like every other software team in existence, you likely have a whole backlog of tickets that you’re never going to work on. You don’t want to include those tickets in how you measure lead time. If someone has a feature idea but neither you nor the customer think it’s important to implement right away, the lead time on that ticket can balloon your total lead time.
Generally speaking, the best way to talk about lead time is by narrowing it down to specific types of tickets. Instead of measuring lead time across all tickets, it’s best to narrow your measurement to high-priority and medium-priority tickets.
Not Always the Best Thing to Optimize for
This might seem counterintuitive, but it’s true: you don’t always want to optimize for lead time. Running a software team is a careful balance of coordinating what your customers need today and what they’re going to need six months or a year from now. Sometimes, adding too many features too quickly craters the overall quality of your codebase, making it much more difficult to add new features down the line. Your work as a leader requires balancing quick responses to customer requirements with long-term investment in your platform. If you’re optimizing your team’s performance to minimize lead time, you might not be spending enough time investing in the future. Unfortunately, lead time is also a trailing metric, meaning that it won’t tell you when the quality has declined too far until it’s too late.
The Value of Cycle Time As a Metric
In contrast to lead time, cycle time is primarily an internally facing metric. That’s not to say that cycle time doesn’t impact customer satisfaction. Cycle time often influences lead time, for obvious reasons. If your team spends more time working on an average task, it will on average take longer to ship that feature. Cycle time provides a lot of informative value to engineering leaders. What’s more, it’s a metric that you can optimize in a lot of ways. The key thing you want to remember with lead time is that as an overview, it’s valuable. But the most value comes from diving into the specifics of the factors influencing your lead time.
How to Optimize for Cycle Time
When you’re looking at cycle time, you don’t want to think about it as an exclusively top-level number. Sure, it’s easy to say that on average, your team takes seven days from starting to work on a feature to shipping it. That’s useful to know. But with a good dashboard, you can drill down much deeper than that. How much time is your team spending on planning? What about coding and reviewing the code? How long does the code spend in QA or waiting for release? All of those are useful metrics that comprise cycle time.
If you can split those values apart, you’ll get a lot more insight into your cycle time. For instance, what if your features spend three days waiting for QA after developers finish coding? That’s useful insight that can lead to real change for your team. Cutting that lead time to a few hours by automating repetitive QA processes means you’ll reduce your overall cycle time. Or perhaps your team is grooming tickets to include too much work. Instead of working on big tickets, which have a higher chance of bugs and take longer to complete, you can reduce overall cycle time by reducing the size of your tickets.
The biggest value to reducing cycle time is increasing the efficiency of your team. By focusing on reducing wasted time in your cycle, you can boost not only team efficiency but also customer lead time as well.
Cycle Time and Lead Time Work Together for Your Team
Now that you know how cycle time and lead time work together, figuring out what they mean for your team is the critical next step. It’s clear how these two metrics impact your team and customer satisfaction, so you need to figure out how you’re going to optimize them. The solution is different for every team, but the first step is plugging into a high-quality dashboard like Plutora’s.
Plutora works with tools you already use to track your team’s progress, so it’s easy to get started. And once you’ve started measuring those values, you can recognize inefficiencies in your process. Ironing out those wrinkles will make your team more effective. If you think Plutora may help your team deliver more value to your customers, you can request a free demo.
For more great articles about software value streams and how to manage it, please visit our blog: https://www.plutora.com/blog.