Cycle-Time by Control Charts
Control charts are a powerful tool in the agile software domain, specifically for measuring cycle time. They are a substantial influence in data-driven decision-making, empowering teams to discern improvement opportunities and implement strategies for heightened efficiency.
Control Chart Explained
A control chart of cycle time helps you monitor the duration it takes to complete a process cycle, identifying variations and potential improvements. Here’s a step-by-step guide to understanding and interpreting these charts.
- X-Axis (Time): This axis represents time, usually plotted in chronological order. Each point on the x-axis corresponds to a specific time period or batch.
- Y-Axis (Cycle Time): This axis measures the cycle time, indicating the duration taken to complete a backlog item, from them moment we started working on it till the time it is Done.
- Data Points: Each data point represents the cycle time for a specific backlog item or a group of backlog items. The dot appears on the control chart when the backlog item is Done (placed on the X-Axis on the day of Done). The height of the dot (Y-Axis) is according to the cycle time of the backlog item.
- Average Line: This line represents the average cycle time over the entire data set.
- Rolling average Line: also known as a moving average, smooths out short-term fluctuations in cycle time data to highlight longer-term trends. It calculates the average cycle time over a specified window, such as the last 7 or 30 days, and continuously updates this average as new data points are added and older ones are dropped.
Cycle Time of Backlog Items
Cycle time measurement is a fundamental aspect facilitated by control charts, offering a visualization of the delivery time taken for backlog items such as stories, bugs, or tasks. This metric is pivotal for evaluating the efficacy of the delivery workflow.
It is important to note that cycle-time control charts are not bounded by sprints or size estimations of backlog items. This makes this chart useful even for teams that didn’t fully apply sprints or are not fully using size estimations for backlog items.
Control charts support retrospective analyses, enabling teams to identify trends and patterns in items with prolonged cycle times. This retrospective view helps pinpoint specific bottlenecks or issues contributing to extended cycles.
At the core of control chart utility is the drive for continuous improvement. Teams, guided by these charts, can experiment with techniques such as breaking down larger items, swarming, or limiting Work in Progress (WIP) to systematically reduce average cycle time. Using those techniques, the cycle time trend should decrease, implying faster delivery and faster feedback loops with the stakeholders.
These charts can use filters to view specific backlog item types, whether they are stories, tasks, or bugs. These views can generate insights specifically for each of the backlog item types.
Scaling up the application of control charts is valuable for analyzing trends across multiple teams, either within a single product or across an entire organization.
Cycle-Time in Roadmaps
While control charts are conventionally applied to smaller items (e.g. stories, tasks, bugs), their adaptability extends to larger deliverables such as features and epics. Analyzing cycle time at this scale is crucial for understanding and improving the efficiency of handling more complex work items.
In organizations following a roadmap reflected in Kanban boards, control charts become instrumental in predicting delivery timelines for features and epics. This predictive capability supports enhanced planning and coordination with stakeholders.
Closing
In essence, control charts in the agile software domain serve as a robust framework for measuring and improving cycle time across various levels, from individual tasks to comprehensive features and epics. Their versatility positions them as a cornerstone for data-driven decision-making and the continuous enhancement of agile development processes.
