Despite using a variety of common agile practices, companies are still experiencing less transparency into when the work will be completed, and work is taking a year or two to deliver the value requested to the customer. In some cases, it is taking longer to deliver with agile methodologies than when project based methods were used. What could be happening here?
Teams are doing the right things, following the good agile practices they have been taught, breaking down stories into small chunks, completing in a sprint time box, completing stories in under 14 days. Yet the customer and business is still waiting for the value to be delivered for a year or two
How could this be? What can we do? What are some common objections heard and how do you overcome them?
This can be answered by looking at the age of items sitting in your backlog!
- In Viz, rather than having the backlog (for example, “todo”) state modeled as “new”, model it as “wait” to see the age of the backlog items in the Flow Item Analyzer.
- Review the age of backlog items in your sprint, monthly and/or quarterly retrospective. It will likely be very eye opening and drive some great discussion.
- Pay special attention to the age of the highest priority items.
This practice will ensure incorporation of further agile values such as:
- Transparency
- Inspect and Adapt
- Understanding what is impeding Fast Feedback
A common objection from teams are that they want to hide this work as they feel they will get punished for it. This is where Value Stream Management comes in. Everyone along the value stream is in it together and has a role to play. In this case, those who are pushing in/committing/creating work at a rate faster than teams can complete it are also held accountable to the role they play in improving flow. The work has no choice other than to age if demand exceeds capacity.
Some common results seen when teams begin to review this data and take action are:
- They identify ways to change the sequence of when they plan and complete certain work, decreasing the age and therefore, Flow Time.
- Hidden dependencies between teams are made visible and measured as wait time.
- It is found there are years of work and dependencies hidden in backlogs, showing that demand is way over capacity. The critical tough choices can be made to expand capacity, remove lower priority work, identify further automation that can be done.
- Predictability improves as it wasn’t the estimates that were the problem, it was the delay waiting to get pulled into the sprint, hidden dependencies and work aging due to demand exceeding capacity.
We’d love to hear your thoughts! Give it a try, or if you are doing this today, what do you find?