Today, we’re diving into a fun use case that showcases how process mining can really help you hone in on specific aspects of your business processes. We had a great question brought up to us asking how can we look at our process from a day vs night shift. They suspected there was some variation in how incidents that came in at certain times were resolved / maintained.
This gave me a great excuse to write a little something on how we achieve this type of time based analysis using process mining.
As always, we’re thrilled to share these examples drawn from the real-life experiences of our customers!
Here is a quick 4 minute video on how to achieve this or follow along below:
You can find other Process Optimization use cases here
How to do the analysis
This approach can be applied to any workflow(Incident, HR Case, Customer Service Case, etc) and does not require any additional/special Process Optimization configuration beyond the out of the box content packs offered with a given workflow
Open your project to the Analyst Workbench view
From here we are going to use the “Conditions” option from the Advanced filters section in the lower left hand corner of the screen.
Click on the Conditions button.
Condition filtering allows us to identify records that meet a specific condition. An example of this is if we wanted to identify and isolate all records with a High Priority, we could do so here. One important note here is that this will pull all records where this condition is CURRENTLY BEING MET. So if an incident was initially classed as "High Priority" but is currently "Low" it would not appear as part of this filtering. Condition filters are very good for identifying records that have a field that stays relatively static during a records lifecycle.
It works very well for what we are trying to accomplish with this analysis!
Here I am using the condition filters to identify all incidents that were opened on any given day BEFORE 6am and AFTER 6pm. This will exclude all the incidents that were opened during the day and only show us the night 'shift' incidents.
This is actually 2 tips in one as it shows off a very little used (but VERY helpful) filter operator "trend".
From here I will just save this as a new filter set named "Night Shift" -> Clear all filters -> apply the inverse filter conditions to get the day shift incidents.
Now I am free to compare & contrast both processes!
Thanks for reading and as always,
If you are looking for more in-depth training you can use the Process Mining Academy library of content\
You can find other Process Mining use cases here
BelalA
