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Make processes visible through data

Process Mining Training

The Process Mining training covers event logs, process discovery, conformance checking and performance analysis. It makes real process variants, waiting times and bottlenecks visible and actionable

Process Mining Training – Stemann Consulting
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Training overview

Reconstruct real process flows from event logs and improve them systematically

The Process Mining training shows how digital traces from ERP, CRM and workflow systems can be transformed into reliable process models. Participants learn process discovery, conformance checking and performance analysis and identify variants, waiting times and rework

Understand the principles of process mining
Interpret event logs
Identify bottlenecks and rework
Derive improvement opportunities
Curriculum

Training content and agenda

  1. 01

    Introduction to process mining

  2. 02

    Data sources and event logs

  3. 03

    Visualization and variants

  4. 04

    Interpretation and next steps

Target group

Who is this training for?

  • Process management and operational excellence teams
  • Data and IT teams
  • Business functions using ERP or workflow systems
  • Lean Six Sigma and automation teams
Training calendar

Upcoming public dates

27Oct

Dortmund

27.10.2026 – 29.10.2026

On-site | 16 seats
Registration openRequest a place
29Mar

Online

29.03.2027 – 30.03.2027

Online | 16 seats
Registration openRequest a place
13Sept

Stuttgart

13.09.2027 – 15.09.2027

On-site | 16 seats
Registration openRequest a place
29Nov

Berlin

29.11.2027 – 01.12.2027

On-site | 16 seats
Registration openRequest a place
Quality & certificate

Quality assurance & certificate

Exercises start with the event log and progress through variant analysis and conformance checking to bottleneck assessment. The training clearly distinguishes what the data proves, what additional process knowledge is needed and how to derive a meaningful improvement approach

FAQ

Frequently asked questions

What are the prerequisites?

A basic understanding of processes and data is helpful. Deep technical expertise is not mandatory