How to Handle Abandoned Work Items to Preserve the Stability of Your Delivery System
Are you aware of the impact that abandoned work has on the predictability of your system and your overall performance? This is a hugely neglected topic, that actually has great potential to introduce opportunities for improvement in your working practices.
I’ve never been skydiving and I probably never will. In my honest opinion, you must be genuinely insane to take a parachute backpack, hop onto an airplane, fly up to 10,000 feet and jump out into the sky.
Once you jump out of that airplane (apart from proving that you’re out of your mind), something very important happens. You commit to falling back down to the ground. Up until the moment you leave that plane, you had every chance to give up. Jumping into the sky is the actual second that you commit to getting back down to the earth.
When you start a new task, you commit to delivering it. Once your work item gets pulled into your workflow, you commit to meeting your customer’s expectations and finishing the work. It’s a promise that should always come with a high level of confidence that you are going to keep it.
Let me ask you something – would you jump out into the sky if you didn’t know whether or not you would land back onto the ground? Probably not.
Nevertheless, very often, work items get aborted in the middle of the delivery workflow. And here is the thing – the further your work has moved through your value stream, the higher the amount of time and effort you’ve already spent on it.
If this is a behavior that you observe, it is tremendously important to acknowledge that fact and take action to prevent it from happening again in the future.
The Impact of Dropping Out Cards from Your Board on Your Predictability
Ok, let’s be realistic. It is reasonable to cancel work items in the middle of your delivery workflow if you decide that they no longer provide customer value. There is no point in investing more time and effort into finishing something that no one will benefit from. That just doesn’t make sense.
In terms of predictability, there is a good way and a bad way to cancel a work item.
The Bad Approach to Discarding Work In Progress
The bad approach to handling this situation is to pull the card out from the board or delete it altogether. There are two reasons that prove this point.
- By removing a task in progress, you break Little’s Law assumptions. Little’s Law defines stable systems. One of Little’s Law assumptions states that “all work that enters the system must flow through the completion and exit of the system”. By deleting a card in the middle of the process, that assumption won’t hold anymore. Therefore, you will affect the stability of your system. The less stable it is, the less predictable it becomes.
- By dropping cards out from the board, you lose opportunities for improvement. Aborting something you have already started is a bad practice in every sense. You want to know what caused that behavior and how often that happens. It is important to learn a lesson from it and identify opportunities for improvement in order to prevent it from happening again. By dropping out discarded items from the board, you don’t solve the problem, you cover it up.
The Good Approach to Handle Abandoned Work
In order to preserve Little’s Law assumptions and enable the possibility to analyze your discarded work, it would be much better to add a “Discarded” label to the ticket and move it to your “Done” column. You can then collect all the discarded items and investigate what has caused that behavior.
You should strive to meet the Little’s Law assumptions to maintain a stable system because stable systems translate to predictable systems.
The main goal of tagging abandoned work items is to enable the possibility to analyze these work items afterward. That’s what continuous improvement is all about. You want to know what causes you to cancel your work and then take action accordingly.
The Effect of Abandoned Work on Your Performance
It’s also important to understand how canceled work affects your overall performance. How does it impact your delivery times?
To answer this question, let’s look into the Cycle Time Histogram.The chart shows the frequency distribution of your delivery times. The horizontal axis displays your cycle times and the vertical axis shows the number of work items with the same cycle time.
In the histogram above, we can see that this team has completed 35 items in 1 day, 14 items in 2 days, 9 items in 3 days, and so forth.
By analyzing the frequency distribution of your cycle times, you’ll be able to determine whether or not there is too much variability in your process. A wide spread indicates that your cycle time varies significantly and your workflow is inconsistent. The histogram above displays a fat-tailed distribution. Systems with fat-tailed distributions are unstable and unpredictable.
Now let’s analyze your completed work without the canceled work items. What is the difference? How does abandoned work affect your delivery times? How does it affect the shape of the overall cycle time distribution?
Analyzing your work with and without discarded work is a great opportunity for process improvement. It enables you to answer the question “Why do we keep canceling work items?”.
Is it because in the middle of the process your client decides that they no longer need this feature? Or possibly, your work has been blocked for so long that it has become irrelevant for your clients? Whatever the reason behind canceling your work, always remember that when you start working on something, you commit to delivering it.
If you are interested to learn about the strategies that will enable you to achieve stable, predictable delivery systems, I’d be thrilled to welcome you to our Sustainable Predictability program.
You commit to bringing customer value and solving your customer’s problems. By abandoning work, you pull out of that commitment. That behavior will inevitably affect your ability to build trusting and reliable relationships, and keep your customers happy.
Meet the Author
Sonya Siderova is a passionate product manager and a driving force behind Nave, a Kanban analytics suite that helps teams improve their delivery speed through data-driven decision making. When she's not catering to her two little ones, you might find Sonya absorbed in a good heavyweight boxing match or behind a screen crafting a new blog post.