Hi
I want to calculate spill over rate against tasks/bugs/stories which remain incomplete in sprint and is carried forward in next sprints or backlog.
I was using status time free gadget to calculate lead/cycle time and could get the number of tasks which are spilled in current sprints but PICKED IN LATER sprints. But i am cannot get tasks which are moved in backlog after spill over.
Any other way to calculate this rate in both situations of all sprints in one place?
Thanks.
Hi @attaf sheikh 👋
I wanna suggest you to try Time in Status for Jira (Cloud) where we have all the metrics for each sprint.
With add-on you can track lead/cycle time more precisely
and check your spill over rate easily with Sprint Performance Report feature.
I would be happy if you had a look at it!
Try Time In Status for Jira + Sprint Report for free.
You can also book a live demo - we'll show you the application inside out and answer all your questions.
Have a nice day, and happy sprinting 🥳
Welcome to the Atlassian Community!
There is usually no need to report on this. When an issue "spills over", the team that failed to complete it is supposed to explain what went wrong, improve their process so that it won't happen again (or just write it off - you can't adjust a process to cover unexpected sickness, accidents, or emergencies taking people out of the team during the sprint)
Why do you think you need to report on this?
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Agreed that there shouldnt be a need of this record.
But when teams become habitual of spillovers when they know that its not recorded through any metric, we can gave reasons as you mentioned above because no one can cross check those absence and we can roll over those tasks in next sprint.
Just need a way to bring this into accountability. So it can be improved.
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If this is a temporary symptom, and so necessitating some temporary measurement to help, you could do the following:
Export the issues from recent sprints to a spreadsheet, count the sprint values per issue with spreadsheet functions, visualize that data, and use it to have a conversation with the team about how to experiment to improve.
Kind regards,
Bill
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