The feature that usually turns out to be the real eye opener for managers to the potential of process mining is the possibility of comparing how the same process is executed by different actors (teams, offices, geographies, or any business unit you can imagine).
It is a straightforward source of improvement that does not require any technical sophistication –the simple observation that elsewhere a process is executed in less time, with lower costs or avoiding certain problems, significantly reduces the ability to put forward excuses and focuses internal deliberation on objective facts and causes.
Using inverbis we can do a number of things.
One is to choose which of the variants of the process that have been identified represent the standard or expected performance… and measure how often this performance is matched, by whom, and under what circumstances. In a data model where you specify which area or unit executes each event, you can filter the comparison by region, business unit, team, etc.
Two is to import a standard model into the system and perform the same comparison.
Not only can we see how many times the expected outcome occurs, but we can also identify what the differences are between the standard scenario and what actually happens.
This is all part of the power of analyzing historical data and discovering the actual nature of processes.
But the true goal is to deliver change. Setting-up the data to be updated periodically within the Inverbis platform, will allow us to build dashboards that will tell us if we are approaching the expected level of compliance.
And this is just the beginning.
Wanna know more? Book a demo with us.
Check some of our videos:
- New ➽ ICU discharges: where time slips away
- Fast Track vs Standard Path: Which is Better for Diagnosing Colorectal Cancer
- What causes discards in parenteral nutrition preparation? Key factors revealed!
- How Process Mining Uncovers Medication Administration Issues
The Prompter.io is our open project to share our experience—and that of others—in integrating language models and data-to-text techniques into process intelligence. Don’t miss it!
