Why Is Lead Time Important In Software Development?

In an engineering organization, growth is closely related to producing fast and reliable software. In software development, almost everyone, from developers and technical managers to C-level executives, wants to deploy software faster, write better code, and deliver more value to end users.

Focusing on the right metrics is an important factor. As a technical team leader, your job is to keep the team up to speed by constantly improving the way it works. In terms of speeding up the software delivery process, lead time and cycle time are two important speed metrics to measure and monitor.

The standard definition of lead time adopted by the DevOps Research and Assessment Organization (DORA) looks at the time from when a commit is signed to when it goes into production. Therefore, it tends to measure the effectiveness of the CI/CD process in the organization. However, it can be viewed more broadly by measuring the end-to-end time required to deliver software:

- Lead Time: Lead time is the time that elapses from the start of a process to its completion. Change can be explained until it makes sense. For example, it could be a new product feature defined by a product manager, a fix after a problem, or a fix after a customer service case. Lead time is calculated by adding any combination of days to purchase materials, manufacture goods, and ship finished goods. For example, in manufacturing, lead time represents the time it takes to create a product and deliver it to the customer.

- Cycle Time: The cycle time of a task or process is the time taken to complete a particular task or process from start to finish. You can think of it as the time it takes to produce a component or item from start to finish. This is usually a fraction of the total delivery time.

Teams measure their average lead time and cycle time to understand how quickly they release software changes and where the bottlenecks are.

Why is lead time important?

Lead time measures the speed of an engineering organization in delivering software – from concept to production. Longer lead times affect the adaptability and agility of your business. As more resources are spent buying similar products in bulk, it becomes more difficult to introduce new or improved products to your brand.

In addition to end-to-end execution time, measuring the cycle time of each step of the software delivery process reveals bottlenecks and helps uncover inefficiencies. For example,
The code review process can take a long time because the review process is not evenly distributed across the team.

- Quality control processes can delay releases, indicating the need to invest in more test automation.
- Sprint planning and task construction may take longer than expected due to limited resources such as designers.
- Or the team may be distracted from putting out fires all the time, leading to a lot of context switching and multitasking.
- A data-driven approach to managing engineering operations can help determine these speed bottlenecks, but historical and current data can also be used to assess the impact of interventions over time.

DORA research has shown that speed and stability often go hand in hand! This is because efforts to reduce lead times encourage techniques common to high-performing teams, such as working in small groups, that quickly add value and reduce risk.

In other words, it's better to measure and optimize these metrics yourself, helping teams leverage technical features and modern techniques to improve overall performance. Thus, by constantly measuring and iterating speed metrics such as lead time and cycle time, engineering teams can deliver better software to their customers faster and achieve better business results.

So, how do measure lead time?

Measuring organizational lead times can be challenging, and analyzing lead times at different stages is even more so. This is because the software development process often involves many different systems - task management systems, resource control systems, CI/CD systems; and many different teams - design teams, implementation teams, QA teams, and release management teams - each of which may use different systems and follow different processes to manage their tasks.

Some organizations try to carefully follow the process of managing and updating task status in a task management system such as Jira, and then use the resulting data to measure time spent at each stage of the process.

However, today's software engineering teams are notoriously weak on processes and processes across teams are not standardized. When work spans multiple teams with different processes, it can be difficult to get a single view of tasks. Relying on human input to monitor and update these views is error-prone. In addition, redundant processes can significantly slow down the team. To the extent possible, automating the collection of timestamps and state changes is the preferred way to measure and analyze runtime.

For example, a unified platform such as NetSuite ERP gives companies visibility into each of these business functions. In this way, they operate from a single, accurate source of truth to optimize nearly all operations that affect runtime.

In short, finding the right balance between process/predictability and agility can be challenging, but automation can help bridge the gap between the two - allowing teams to accurately measure velocity metrics such as execution time and lead time. cycle, without excessive process overload.