r/processmining

The term digital twin for business operations keeps coming up more with AI and automation but I'm still trying to figure out what the actual value is.

Sometimes it sounds like process mining. Sometimes workflow visibility. Sometimes simulation. Other times it just sounds like another analytics layer with a nicer name.

So where does it actually become useful for making operational decisions?

For teams that have evaluated this stuff, what could you actually do with a digital twin that you couldn't do before?

Hello,

I have a PhD in Operations Research and Machine Learning, and I recently accepted a new job as an R&T scientist at a private research lab. I'll be working on a project for a government agency involved in logistics, and it seems the main part of the job will be process mining. My future manager sent me van der Aalst's Process Mining book to study before I officially start.

I'm currently working through the core topics (Petri nets, WF-nets, C-nets, etc.), though to be fair, my background and stronger interest lie in optimization and classical ML/DL. I'm quite proficient in Python, but I've never used Disco, SAP, Celonis, or other commercial process mining tools.

1. What should I realistically expect to be doing day-to-day? Which topics deserve the most focus as I study process mining from scratch — discovery, conformance checking, something else?

2. Will I have room to bring in ML or OR methods eventually? If so, how and when does that usually happen in practice — and are teams generally more receptive to interpretable models (decision trees, simple heuristics) over more complex ones (deep learning, black-box optimization) in this kind of setting?

What actually makes one process mining software better than another?

A lot of workflows look simple until you start digging into them. Then you find email, spreadsheets, different teams doing the same thing differently, and documentation that hasn't been touched since the last system change.

Some tools seem to rely heavily on clean event logs, while others try to pick up work happening across different applications. Then there’s the whole setup and data preparation side, which probably doesn't get enough attention during demos.

Celonis, Skan AI, Mimica, KYP ai, and ABBYY all seem capable on paper. But what becomes obvious once a company actually starts evaluating them?

For anyone comparing these tools, what mattered most in the end?

Hi r/processmining,

Have you ever looked at a conformance-checking result and thought, “I understand the process model… but what exactly is this visualization trying to tell me?”
That is essentially what our research is about.

We are a team of master’s students at the University of Mannheim conducting an academic experiment on how conformance-checking results should be visualized.

During the study, you will complete a series of short conformance-checking tasks using different visualization idioms and rate how useful and easy to use you find them. No advanced conformance-checking expertise is required—we introduce the key concepts before the tasks begin.

Your participation will help us understand which visualizations genuinely support users and which ones merely look impressive.

• Duration: Approximately 20–30 minutes
• Device: Laptop or desktop computer required
• Background: Some familiarity with process management, process models, Business Process Management, or Process Mining is recommended
• Cost: A little bit of your time and possibly a few moments of intense staring at process visualizations

Take part here:
https://0c6d5248-f6a7-4d49-bc79-a6e8c34de13d.ma.bw-cloud-instance.org/welcome

Thank you very much for supporting our research. Sharing the study with colleagues, students, or fellow Process Mining enthusiasts would also be greatly appreciated!