this short video is part of the process optimization use case Series where we'll focus on different techniques to identify process inefficiencies non-conforming activities and Improvement opportunities so when we design a workflow we design for both completeness and efficiency to ensure that everyone involved has an optimal experience as part of the design of a workflow we will often build in the ability to reassign work to the appropriate team to ensure that the task gets completed successfully even if reassignments are are part of the design of the process they tend to be time consuming and have an impact on overall productivity so anything we can do to reduce the number of reassignments or how efficiently they are handled is a plus like with smoke like with most things the the first step in making improvements is understanding what is actually happening so many of us have probably been asked in the past to answer the question of who's holding on to the tickets the longest in fact I've been asked so many times I I've given it a name I like to call it the finger-pointing report or if you're more of a glass half full person it's the opportunity to improve reports or another question we're often asked is where are my incidents or cases ping-ponging back and forth between teams process optimization or servicenows in platform process mining solution can be used to help us get a better understanding of how often certain reassignments are happening which handoffs are taking longer than others and where do we have these dreaded ping pong situations so what you're looking at on the screen here is a project that we've mined with process optimization that is using our assignment groups as the activities or the different nodes that you're seeing in the map are the different hops from an assignment group perspective that incidents are taking to get to closure you can do the same type of analysis with customer service cases or HR cases we just chose incident for this example now what we're going to do here is we're going to come over and we're going to use our our variation analysis section of the analyst workbench to help us do this analysis what variation analysis does is it shows us all the different routes that in this case these incidents are taken to get to closure now I can scroll through each one of these and look at the individual route and get some details on statistics about the route but I can also use this filter capability to help me isolate the certain scenario that I want to focus in on in this case we want to look at routes that have more than one or more than four steps so route steps greater than four and we also want to make sure that there's some volume to the the number of Records in this case incidence again that are moving through these routes so we're going to say records are greater than 10. and we'll hit apply and what this is going to do is it's going to narrow my list of routes here in the variation analysis down a little bit and I'm going to then start to sort these not by highlights but by most records and then I can see here that this um this route has got five steps the one that's going if you look down at the bottom of the screen my Solana customer service support to Solana billing support back to Solana customer service support that's got the highest volume but its average duration is only one days one day the thing that jumps out to me is the second one on the list so Solana customer service support to Solana tech support back the Solana customer service support there's a decent amount of records that are taking that path but on average they're taking one month to complete and that's a problem for sure let's let's focus in on these so what I can do is I can click on this say show route and that's going to isolate it to just the records that took this path and we can see here that the records took in came in it took them 13 minutes to be assigned to that Solana customer support team and then on average it's taking them about a day to be reassigned to this Solana tech support team and then here's where the problem comes in it's taking this team Solana tech support four weeks on average to then reassign those tickets or incidents in this case back to that custom support team like what's going on there and what we can do is a couple of things one is we can click on this and we get some additional statistics about that portion of the route we can use our histogram to see if there's any outliers that are really skewing the numbers in one way or the other and then of course because we're an in-platform process mining solution we can come over here and we can drill down to the detailed records these 26 incidents and do some further analysis if there happened to be a larger volume of uh incidents moving through these paths we'd have some additional options here that are machine learning based where we could run some machine learning based root cause analysis or run some machine learning based cluster analysis to help us get a better understanding of potentially what is going on with these these incidents that are taking four weeks to be transferred back to that original group so just one quick example or technique that you can use to do some multi-hop or assignment group analysis around your incidents in this case but as I mentioned uh these could be used this technique can be used across different workflows as well the setup instructions for how to create a model like this are going to be in the block the accompanying blog post that's with this video here appreciate your time happy mining