Decoding Agility: The Critical Role of Cycle Time in Efficient Workflows

agile planning

Decoding Agility: The Critical Role of Cycle Time in Efficient Workflows

It’s widely recognized that just strict adherence to agile methodologies such as Sprint huddles, Backlog grooming, Sprint planning, and Sprint retrospectives doesn’t automatically ensure agility. The true characteristics of agile teams encompass:

  • The ability to swiftly reprioritize in response to evolving customer needs.
  • The capacity to deploy updates frequently—ideally multiple times a day or at least weekly.
  • The flexibility to refine designs based on newfound insights and experiences.
  • The use of empirical data for data-driven decisions.
  • The capability to ensure predictable releases.
  • The proficiency in measuring the value delivered.

While the above list is not comprehensive, it encapsulates the core principles. Ultimately, if quality products are not being delivered consistently, the investment in agile practices is in vain. This prompts several crucial questions:

  • Where is the best place to start?
  • How can existing bottlenecks be identified?
  • How can historical bottlenecks be traced?
  • Where should improvements be initiated?

Cycle Times

Let’s begin by validating our understanding of two simple but key terms: Cycle Time and Lead Time. The definitions should be apparent from the following diagram (example of a workflow):

Lead Time

Customers are primarily interested in Lead Times, which measure the duration from when a customer requests a feature to when they receive it in production. They don’t care about your internal processes. Their clock starts ticking as soon as they’ve made their request.

Lead Time = Date customer starts using the feature in production - Date customer requested the feature

Cycle Time

Cycle Time is simply the time taken for a team to complete a task from start to finish:

Cycle Time = Timestamp when work is marked 'Done' - Timestamp when work began

Due to its importance let’s unpack this metric further.

  • Effectiveness of your workflow: The above image contains an example of a workflow. Your workflow will be different. By tracking this, teams can gauge their effectiveness and efficiency. Shorter cycle times generally indicate more effective processes, which can lead to faster feature delivery and quicker responses to market or customer demands.
  • Predictability – When cycle times are consistent and predictable, it becomes easier to forecast completion dates. This predictability is crucial for planning releases and aligning with business activities, such as marketing launches or compliance with regulatory changes. It helps manage stakeholder expectations and allows for more reliable scheduling and resource allocation.
  • Quality: Cycle time also affects quality. Rushing through cycles can lead to errors and technical debt. However, optimizing cycle time through improved processes and automation can enhance quality without sacrificing speed. This balance is crucial for long-term product sustainability and customer satisfaction.
  • Cost Efficiency: Reducing cycle time can often lead to reduced costs. Faster cycles mean less time spent on each feature or fix, which can decrease labor costs and overhead. Furthermore, efficient processes reduce waste and can lower the cost of delayed opportunities by getting products to market faster.
  • Improvement Opportunities: Monitoring cycle time helps identify bottlenecks and inefficiencies in the development process. If a particular stage consistently slows down the cycle, teams can target it for improvement. This continuous improvement is crucial for maintaining competitive advantages and improving the quality of the software. Note: it’s more likely that you notice wandering bottlenecks. That’s one of the reasons to implement a Kanban system – identify bottlenecks in real time and swarm to fix it. You can also eventually undertake value stream analysis of your work flow. But we are getting ahead of ourselves.
  • Resource Allocation: Understanding how long tasks take helps in better resource management. Teams can allocate the right amount of people and prioritize effectively. Long cycle times are a cry for help.
  • Feedback and Adaptation: Short cycle times can lead to quicker feedback from end-users and stakeholders. This rapid feedback loop is a cornerstone of agile methodologies, allowing teams to adapt their products based on real user data. The faster an organization can iterate, the quicker it can refine its product to better meet user needs and react to changes in the market or technology landscape.

In summary, Cycle Time is a vital metric that influences operational efficiency, product quality, and organizational agility. By effectively managing and optimizing Cycle Time, you can enhance your teams ability to respond to changes, delight customers quickly, and maintain a competitive edge in the fast-paced tech industry.

Statistical Analysis of your work-flow

Next we turn our attention to tactics – understanding baselines of your Cycle Time data. Without this step you don’t know where to focus your improvements on. You wouldn’t know where the constraints of your workflows lies. If you don’t already have access to this data, gathering it should be your initial priority. (Using Jira? I’ve written a bunch of Python scripts to help you export Jira state transition information) Once obtained, basic statistical calculations can help establish a baseline. Here’s an example of summary statistics:

  • Total tickets: 300
  • Workflow states included in Cycle Time: In progress, development completed, QA in progress, QA completed, deployment to UAT
  • Average cycle time: 37.22 days
  • Median cycle time: 20.85 days
  • Standard deviation: 50.92
  • Maximum cycle time: 490.3 days

Observing just these four statistics, immediate concerns become apparent.

  • A substantial disparity between the Median and Average cycle times indicates the presence of outliers. It could be that a small number of tasks take exceptionally long time (vendor procurements come to mind) or there’s indeed issues with your workflow.
  • The median cycle time of 20.85 days, while more stable against outliers, still exceeds the typical sprint duration of 10 days.
  • The high standard deviation of 50.92 points to a wide variance in how long different tasks take to complete, indicating inconsistency and potential bottlenecks in the workflow.

I like to create a histogram next. Here’s the example of a histogram created with the same dataset.

Cycle time histogram

Now we can glean more information on the current workflow.

  • A pronounced long tail in the distribution suggests exceptionally prolonged cycle times – so we can rule out a small number of tasks with exceptionally long durations.
  • Only 35% of tasks meet the sprint duration of 10 days or less.
  • It takes more than two months for the team to complete 80% of the tasks.

Before I get into suggesting a play book to identify issues, given these set of stats & histogram, what additional insights are you able to tease out? What should be the next immediate objective? What strategies could you and your team consider to achieve your objective?

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