JBON_DATA

AUTOMATION

Process Mining: Discovering Hidden Inefficiencies

Process mining bridges the gap between business process management and data science. It uses event logs to reconstruct how processes actually work—not how they're documented to work.

What is Process Mining?

Process mining analyzes event logs from IT systems to:

  • Discover: Automatically create process models from data
  • Conform: Compare actual behavior to designed processes
  • Enhance: Identify bottlenecks and optimization opportunities

The Event Log

At minimum, you need three data points per event:

  • Case ID: Unique identifier for the process instance
  • Activity: What happened
  • Timestamp: When it happened
| Case ID | Activity          | Timestamp           |
|---------|-------------------|---------------------|
| 1001    | Order Received    | 2024-07-01 09:00:00 |
| 1001    | Credit Check      | 2024-07-01 09:15:00 |
| 1001    | Stock Check       | 2024-07-01 09:20:00 |
| 1001    | Order Shipped     | 2024-07-02 14:30:00 |
| 1002    | Order Received    | 2024-07-01 09:05:00 |
| 1002    | Credit Check      | 2024-07-01 11:00:00 |
...

Discovery Algorithms

Several algorithms can reconstruct processes from event logs:

  • Alpha Algorithm: Simple, good for structured processes
  • Inductive Miner: Handles noise and incomplete logs
  • Heuristic Miner: Focuses on most frequent patterns

What You'll Find

Process mining typically reveals:

  1. Rework loops: Activities repeated unnecessarily
  2. Bottlenecks: Where cases wait longest
  3. Deviations: Non-standard process paths
  4. Automation candidates: High-volume, low-complexity steps

Tools of the Trade

  • Celonis: Enterprise leader, SAP integration
  • Disco: User-friendly visualization
  • PM4Py: Open-source Python library
  • ProM: Academic research platform

Getting Started with PM4Py

import pm4py
from pm4py.objects.log.importer.xes import importer

# Load event log
log = importer.apply('event_log.xes')

# Discover process model
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
net, initial_marking, final_marking = inductive_miner.apply(log)

# Visualize
from pm4py.visualization.petri_net import visualizer
gviz = visualizer.apply(net, initial_marking, final_marking)
visualizer.view(gviz)

Before automating, understand. Process mining provides that understanding.

← Back to Blog