V12.0
cover
Front cover
Notebook
Fundamentals of IBM Process Mining
Course code WB846 / ZB846 ERC 1.0
IBM Training
March 2022 edition
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V12.0
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TOC
Contents
Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
Course description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Unit 1. Project planning and process analysis with IBM Process Mining . . . . . . . . . . . . . . . . . . . . 1-1
1.1.
1.2.
1.3.
1.4.
Unit objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2
Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3
Overview of process mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4
Overview of process mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
What is process mining? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6
How is process mining used? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7
What is task mining? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-10
Benefits of task mining and process mining integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-12
Process mining use cases (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-13
Process mining use cases (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-14
The Digital Twin of an Organization (DTO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-15
The Digital Twin of an Organization (DTO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-16
The Digital Twin of an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-17
From Process Mining to the Digital Twin of an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-19
Planning a process mining project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-21
Planning a process mining project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-22
Team composition – customer members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-23
Team composition – IBM members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-24
Typical project journey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-25
Typical project journey – with team members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-27
Process scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-28
Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-30
Process validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-31
Process analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-32
Typical insights in a procure-to-pay project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-33
Process mining and Business Process Management (BPM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-34
Overview of IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-35
Overview of IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-36
Overview of IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-37
IBM Process Mining capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-38
Continuous process improvement and automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-40
How is process discovery performed (1 of 2)? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-41
How is process discovery performed (2 of 2)? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-42
How is task mining performed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-43
Unit summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-44
Review questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-45
Review answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1-46
Unit 2. Evaluating a process for RPA candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1
Unit objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2
Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3
2.1. Overview of Robotic Process Automation (RPA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4
Overview of Robotic Process Automation (RPA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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2.2.
2.3.
2.4.
2.5.
2.6.
2.7.
What is Robotic Process Automation (RPA)? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6
Software robots are similar to physical robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7
Benefits of Robotic Process Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8
Challenges of Robotic Process Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9
RPA and process mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-10
RPA and process mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-11
How RPA fits with process mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-12
Using task mining data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-13
Analyzing a process by using IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-14
Analyzing a process by using IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-15
Data extraction and process setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-16
Discover frequencies, variants, rework, durations, costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-17
Check conformance with reference models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-18
Generate a BPMN model from real processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-19
Generate DMN decision tables with rules mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-20
Set and monitor KPIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-21
Monitor processes with dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-22
Simulate your business with digital twin before engaging development costs . . . . . . . . . . . . . . . . . .2-23
Getting started with IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-24
Getting started with IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-25
Navigating IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-26
Creating a new process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-27
Upload your data source (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-28
Upload your data source (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-29
Map relevant data columns (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-30
Map relevant data columns (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-31
Backups and reference models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-32
Using IBM Process Mining to evaluate RPA candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-33
Using IBM Process Mining to evaluate RPA candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-34
The Frequency model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-35
Performance views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-37
Rework view . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-38
Duration views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-39
KPI Analysis view . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-40
Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-41
Cost views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-42
Understanding the cost model (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-43
Understanding the cost model (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-44
Performing a path analysis (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-45
Performing a path analysis (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-46
Evaluating model conformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-47
Performing root cause analysis on a deviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-48
Performing task mining on an activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-50
Using dashboards to evaluate KPIs (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-51
Using dashboards to evaluate KPIs (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-52
The project overview dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-54
The project overview dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-55
Project overview dashboard (1 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-56
Project overview dashboard (2 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-57
Project overview dashboard (3 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-58
Project overview dashboard (4 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-59
Introduction to simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-60
Introduction to simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-61
Introduction to simulation (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-62
Introduction to simulation (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-63
Simulation settings (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-64
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Simulation settings (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-65
Simulation header . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-67
Activity parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-68
Simulating automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-69
Gateway parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-70
Simulation warnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-71
2.8. Comparing simulation results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-72
Comparing simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-73
Creating a new simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-74
Performing a process diff (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-75
Performing a process diff (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-76
Unit summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-78
Review questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-79
Review answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-80
Exercise 1: Evaluate a process for RPA candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-81
Exercise objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-82
Unit 3. Advanced data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1
3.1.
3.2.
3.3.
3.4.
Unit objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2
Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3
Data preparation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4
Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5
Activities involved in data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6
Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7
Why Data Quality matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8
Data quality issues: an example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9
Data Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-10
Data Profiling common analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-11
Event log creation – classic approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-12
Event log creation – multi-level approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-13
Best practices to generate and map a data source for multilevel process mining analysis . . . . . . .3-14
Multi-level process example (1 of 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-16
Multi-level process example (2 of 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-17
Multi-level process example (3 of 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-18
Data relationships in a multi-level process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-19
Evaluating unexpected flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-20
Evaluating unexpected flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-21
Identifying unexpected flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-22
Filtering unexpected flows (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-23
Filtering unexpected flows (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-24
Path filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-25
Path time filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-26
Applying filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-27
Analyzing maverick buying using custom dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-28
Analyzing maverick buying using custom dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-29
Example: A custom dashboard for maverick buying (1 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-30
Example: A custom dashboard for maverick buying (2 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-31
Example: A custom dashboard for maverick buying (3 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-32
Example: A custom dashboard for maverick buying (4 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-33
Creating custom dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-34
Creating custom dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-35
Creating custom dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-36
Configuring widgets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-38
Card widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-39
Line chart widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-40
Bar chart widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-41
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Contents
TOC
Bubble chart widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-42
Table widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-43
Unit summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-44
Review questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-45
Review answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-46
Exercise 2: Evaluating maverick buying in a multi-level process . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-47
Exercise objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3-48
Unit 4. Using simulation and the BPA tool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1
4.1.
4.2.
4.3.
4.4.
Unit objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2
Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3
Introduction to the Business Process Analysis (BPA) module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4
Introduction to the Business Process Analysis (BPA) module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5
The Business Process Analysis (BPA) module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6
Process landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7
Application landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8
Organization landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9
Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-10
Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-11
DMN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-12
Introduction to Business Process Model and Notation (BPMN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-13
Introduction to Business Process Model and Notation (BPMN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-14
About BPMN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-15
BPMN elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-16
BPMN model example (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-17
BPMN model example (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-18
Introduction to Blueworks Live (BWL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-19
Introduction to Blueworks Live (BWL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-20
IBM Blueworks Live . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-21
Exporting a Blueworks Live process (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-22
Exporting a Blueworks Live process (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-23
Creating and running a simulation of a BlueWorks Live process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-24
Creating and running a simulation of a BlueWorks Live process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-25
Importing a BPMN model into IBM Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-26
Field mapping from BWL to IBM Process Mining (1 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-27
Field mapping from BWL to IBM Process Mining (2 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-28
Field mapping from BWL to IBM Process Mining (3 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-29
Field mapping from BWL to IBM Process Mining (4 of 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-30
Create a simulation scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-31
Running a simulation (1 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-32
Running a simulation (2 of 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-33
Unit summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-34
Review questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-35
Review answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-36
Exercise 3: Simulating a Blueworks Live BPMN process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-37
Exercise objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4-38
Unit 5. Course summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1
Unit objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2
Course objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3
IBM credentials: Badges and certifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4
Learn more about this product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5
Additional resources (1 of 5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6
Additional resources (2 of 5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7
Additional resources (3 of 5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8
Additional resources (4 of 5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-9
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
vi
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Contents
TOC
Additional resources (5 of 5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5-10
Unit summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5-11
Course completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5-12
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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V12.0
Trademarks
TMK
Trademarks
The reader should recognize that the following terms, which appear in the content of this training
document, are official trademarks of IBM or other companies:
IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International
Business Machines Corp., registered in many jurisdictions worldwide.
The following are trademarks of International Business Machines Corporation, registered in many
jurisdictions worldwide:
Resource®
Think®
Social® is a trademark or registered trademark of TWC Product and Technology, LLC, an IBM
Company.
Other product and service names might be trademarks of IBM or other companies.
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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V12.0
Course description
pref
Course description
Fundamentals of IBM Process Mining
Duration: 1 day
Purpose
This course introduces you to IBM Process Mining and how to use it to perform process and data
analysis. You learn the differences between process mining and task mining, the different types of
process mining, use cases, and how process mining is performed. You learn how to use IBM
Process Mining to import a data source, map data, and visualize a process. You learn how to plan a
process mining project. You learn how to evaluate a process for potential candidates for Robotic
Process Automation. You learn advanced data preparation and transformation concepts and how
to evaluate a multi-level process for maverick buying patterns. You also leverage the simulation
capabilities of the product to simulate a Blueworks Live BPMN process.
The lab environment for this course uses a trial environment that is based on IBM Process Mining
version 1.12.0.3I. Access to the trial environment is strictly limited to 30 days with no possibility
of an extension. Before you enroll, make sure that you can complete the lab within the 30-day
period.
Audience
This course is intended for business process analysts, data analysts, or technical analysts that use
the IBM Process Mining product.
Prerequisites
• None
Objectives
• Visualize a process and generate the event log
• Understand data quality and data quality issues
• Evaluate maverick buying patterns of a multi-level process
• View the frequency, duration, and cost models of a process
• Import a reference model and perform conformance checking
• Create custom filters and dashboards
• Perform a Diff comparison of two simulation scenarios
• Analyze a process for potential RPA candidates
• Import a BPMN model into IBM Process Mining
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Course description
pref
• Configure and run simulations on a Blueworks Live BPMN process
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Agenda
pref
Agenda
Note
The following unit and exercise durations are estimates, and might not reflect every class
experience.
Day 1
(00:15) Course introduction
(01:00) Unit 1. Project planning and process analysis with IBM Process Mining
(01:00) Unit 2. Evaluating a process for RPA candidates
(01:30) Exercise 1. Evaluating a process for RPA candidates
(01:00) Unit 3. Advanced data analysis
(01:30) Exercise 2. Evaluating maverick buying in a multi-level process
(01:00) Unit 4. Using simulation and the BPA tool
(01:00) Exercise 3. Simulating a Blueworks Live BPMN process
(00:15) Course summary
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Unit 1. Project planning and process analysis with IBM Process Mining
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Unit 1. Project planning and process
analysis with IBM Process Mining
Estimated time
01:00
Overview
This unit introduces you to process mining, why it is performed, use cases for process mining, and
the different types of process mining. It also discusses the differences between process mining
and task mining and how each is performed in the IBM Process Mining tool. An overview of IBM
Process Mining is provided along with instructions on how to create a new process, import data
and perform data mapping. The project planning process is also introduced, including the
standard phases of a project, scope, roles involved, and typical duration.
How you will check your progress
• Review
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Unit objectives
• Define process mining and task mining
• Describe use cases for process mining
• List the different types of process mining
• Explain how task mining and process mining are performed
• Describe the Digital Twin of an Organization and its purpose
• Describe the typical timeline of a process mining project
• List the different roles involved in a process mining project
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Figure 1-1. Unit objectives
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Topics
• Overview of process mining
• The Digital Twin of an Organization (DTO)
• Planning a process mining project
• Overview of IBM Process Mining
Project planning and process analysis with IBM Process Mining
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Figure 1-2. Topics
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1.1. Overview of process mining
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Overview
w off processs
mining
Figure 1-3. Overview of process mining
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What is process mining?
• Process Mining discovers, monitors, and optimizes business processes.
• It converts a company’s system data into accurate process models, giving actionable insights
into process behavior and inefficiencies.
The customer view
of the organization
and processes
Sources
Mobile
IBM
Process
Mining
Desktop
Cloud
Project planning and process analysis with IBM Process Mining
Data
storage
Knowledge
base
© Copyright IBM Corporation 2021, 2022
Figure 1-4. What is process mining?
• Enterprises execute their business processes across several different stakeholders, such as
enterprise resource planning (ERP) systems, customer resource management (CRM) systems,
customer care applications, and ticketing systems, among others.
• When business processes don’t belong to one single application, it can become difficult for a
business user to have a clear, end-to-end view of the process. Process users often feel like the
process is too complex and difficult to be managed or understood.
• Process Mining can automatically streamline the end-to-end process, starting from the
available data of the applications that the process is running on.
• In the gray box in the center of the illustration, the business activities and processes are
shown together with their sources (such as desktop applications, mobile applications, and
cloud applications).
• IBM Process Mining extracts this digital footprint, uploads it into the platform (Data storage)
and then a knowledge base is created by using IBM Process Mining’s algorithms.
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How is process mining used?
• Information systems, such as
Enterprise Resource Planning
(ERP) or Customer Relationship
Management (CRM) tools,
provide an audit trail of
processes with their respective
log data.
Customer
Service
Account
Administration
Finance
and Ops
Executive
Management
Invoice
Reconciliation
Teams
• Process mining uses data from IT
systems to create a process
model, or process graph. This is
where end-to-end process is
examined and the details of it
and any variations are outlined.
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-5. How is process mining used?
To fully understand a process from the human and service workflow layer can be confusing. Using
process mining, you only deal with actual data that is recorded to systems of record to paint a
picture of real-world transactions that feed activities and processes.
6 Differences Between Traditional Process Analysis and Process Mining
1. How Processes Get Discovered
With traditional business process analysis, the process is typically discovered by reaching out to
all the process stakeholders. Whether by individual interviews, group workshops, employee
shadowing or even sending out a questionnaire, the goal is to piece together the information from
the stakeholders to create a process model.
Today more processes leave a digital trace in the form of event logs. Process Mining gathers data
from these event logs taken from a business’s systems or a data warehouse. The minimum data
requirements needed to map a process are the activity name, a unique case ID, and a timestamp
for each case. Once process mining software has the data requirements, then it uses
sophisticated algorithms to automatically discover the process and create an end-to-end model
that displays all activities, the paths between the activities, and the frequency of those paths. You
can then compare your as-is model to an uploaded reference model for an instant comparison.
Another noteworthy fact, not only can processes be discovered and modeled but business rules
and organizational models can also be automatically discovered with advanced process mining
software.
2. The Quality of the Information
One of the main challenges with interview and workshop methods is the accuracy of the
information collected. Interviews and workshops rely on employees to remember every activity
they’re involved in with perfect detail but that’s easier said than done. Human bias,
disagreements between employees or even as much as one employee that is having an “off day”
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all affect the accuracy of process discovery. Employee shadowing can cause the worker to feel
pressure to “perform”. In contrast, the process model that is created with process mining is
transparent and accurate because it is derived from fact-based data rather than subjective and
often siloed employee knowledge. The quality of the insights makes for better decision-making
and decreases risks when developing new business strategies.
3. The Time Commitment
Because process mining automatically discovers, maps, and analyzes business processes, the
time that is spent is drastically reduced not just for process discovery but throughout the entire
process management lifecycle. A standard process with an enterprise resource planning system
for instance can be discovered in just a few hours. Collecting information through manual means
is a more complicated matter. It can be difficult to get the proper time commitment from workers
who need to juggle priorities and switch between activities at a moment’s notice. Stakeholders all
need to come to an agreement to verify the process. After that comes the modeling and analysis.
These steps alone can take several weeks to complete. Then, a high-level analysis of the expected
behavior of the reference model can take from a few weeks to several months. You haven’t even
monitored deviations and inefficiencies.
4. Process Improvement Capabilities
The fact that process mining automatically discovers and analyzes processes makes it the perfect
tool for continuous process improvement. Anytime you make changes to a process you can use
the newly generated data to create an updated process map with new sets of insights to measure
process improvement. Advanced process mining capabilities go beyond measuring process
improvement and can be applied as a solution for business process improvement strategy.
Simulation engines offer a way to create and test what-if scenarios and analyze them before
selecting the appropriate changes for process improvement. This includes testing the results of
potential RPA in a process before adding in any automation. When a new process model is created
after process automation is implemented, you’ll see the performance of the bots and immediately
see whether they are working as expected. Measuring process improvement gets a lot more
complicated without process mining. If you spent months to manually discover a process, you
must undergo the same ordeal every time you change the process, making it nearly impossible to
compare the current process performance with past performances in a timely manner.
5. Dynamic Process Models
The different ways a process workflow is discovered and then modeled are now covered. So, what
are the qualities of the process models? Do the qualities of the process model change based on
how the process is discovered? The answer is yes. Because all it takes for process mining to
create a process model is a data upload, you can update your process model with the most
current available data as often as necessary. With every new data upload, you can perform a
process analysis to find new inefficiencies in the process and changes in process operations. The
timing, accuracy, and comprehensiveness of these process models make them dynamic process
models. They are interactive models that you can zoom in and out of to get a highly defined or
broader view depending on whatever level of context you want. In contrast, static process
models are the result of process discovery where the process steps are collected and evaluated
from employee interviews. The procedures that are captured during interviews represent a
specific timeframe. The static process model gets outdated quickly whether due to seasonality,
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new or updated policies, new employee onboarding, or changing business strategy. In order to
update a static process model with traditional process analysis techniques, you’d need to conduct
a whole new round of interviews.
6. User Interaction Data Integration
Tasks like matching information between documents are essential components of a process
activity. Leaving people-completed work out of the process model leaves holes throughout the
process. Businesses without process mining or task mining need to resort to the same traditional
process analysis methods. Process mining combines with task mining, making it possible to have
a truly end-to-end process model. Task Mining is the discovery, monitoring, and analysis of user
interaction data on a desktop. Task mining is covered on the next slide. It enriches the information
that is gathered from system event logs by adding in all the steps users perform in the front-end
applications or applications.
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What is task mining?
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-6. What is task mining?
Task Mining is the discovery, monitoring, and analysis of user interaction data on desktops
through the collection of front-end activities. While business data in your operational systems
describes a process by showing you which and when steps have occurred, user interaction data is
everything done by people to accomplish those steps. The main insights obtainable from task
mining are the following insights:
• Productivity 
You can discover how much time users are allocating on the process and how much time the
activities are idle because of context switches.
▪ Precisely calculate the costs of your process based on the productive time of your
resources on the process.
▪ Understand on which applications users are working the most.
• Working Patterns 
You can discover the main patterns of performing a business activity and the most efficient
ways to complete the activity by identifying deviations and inefficiencies.
▪ Set the most efficient patterns as best practice for the employees.
▪ Understand root-causes of inefficiencies and take actions to solve them.
• Automation
You can discover the working patterns to be automated, with the best tradeoff between
benefits and complexity.
▪ Simulate the automation of the most suitable working patterns and verify performance and
cost benefits.
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▪ Complete picture of the process: The combination of business data and user interaction
data creates the full picture of the process, which can be analyzed from both business
level and task level.
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Benefits of task mining and process mining integration
• Perform analyses at different levels of detail for your process, providing you with a complete
picture of your process
• Use the insights to design the best automation strategy
• Automate frequent paths of the process, and repetitive and unproductive tasks
• Design strategies to reduce the most critical process deviations by focusing on the related root
causes
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-7. Benefits of task mining and process mining integration
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Financial
institutions use
process mining
software to improve
inter-organizational
processes, audit
accounts, increase
income, and
broaden its
customer base.
Software
Process mining can
help identify
effective course
curriculum by
monitoring and
evaluating student
performance and
behaviors, such as
how much time a
student spends
viewing class
materials.
Finance
Education
Process mining use cases (1 of 2)
Project planning and process analysis with IBM Process Mining
Process mining can
help to identify a
clearly documented
process. It can also
help IT
administrators
monitor the process,
allowing them to
verify that the
system is running as
expected.
© Copyright IBM Corporation 2021, 2022
Figure 1-8. Process mining use cases (1 of 2)
This slide and the next cover popular use cases for process mining.
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Project planning and process analysis with IBM Process Mining
Process mining can
provide insight into
buyer behaviors and
provide accurate
recommendations to
increase sales.
Manufacturing
Process mining
provides
recommendations
for reducing the
treatment
processing time of
patients.
E-commerce
Healthcare
Process mining use cases (2 of 2)
Process mining can
help to assign the
appropriate
resources depending
on case (such as
product attributes),
allowing managers
to transform their
business operations.
© Copyright IBM Corporation 2021, 2022
Figure 1-9. Process mining use cases (2 of 2)
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1.2. The Digital Twin of an Organization (DTO)
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The
e Digitall Twin
n off
an Organization
n
(DTO)
Figure 1-10. The Digital Twin of an Organization (DTO)
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The Digital Twin of an Organization
• The Digital Twin of an
Organization is a perfect
digital copy of your
process that uses the
most current business
data.
• Unlimited changes can be
tested in the virtual model
before implementation in
the real process, giving
businesses a risk-free
way to evaluate the best
process change
initiatives.
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-11. The Digital Twin of an Organization
Digital Twin of an Organization
• While Process Mining delivers an accurate, end-to-end view of a business process, Digital
Twin of an Organization (DTO) goes a step further, providing additional information about a
process to transform the process model into a precise, dynamic process model.
• DTO is a perfect digital copy of your process model that uses the most current business data
to show companies how the process is running in almost real time. Unlimited changes can be
tested in the virtual model before implementation in the real process. This gives businesses a
quick and risk-free way to find the best process change initiatives that are relevant to the
market and guarantee added value.
• In short, the biggest difference between traditional process mining models and DTO models is
the level of insights the solution is able to derive from the data and therefore, an increased
understanding of the process.
Process mining capabilities that enable Digital Twin of an Organization include:
• Task Mining - Task Mining discovers, monitors, and analyzes users’ interaction data on a
desktop. After collecting user interaction data, an event log is generated with the fine-grain
tasks occurring within a business activity, discovering the related end-to-end user flow. The
same drill-down analysis that is performed on system data can be done on task data, giving a
more complete view of the process.
• Business Rules Miner - Automatically derive Business rules according to business data, to
reveal not only the probability but also why a process follows a specific path. This insight lets
you create precise scenarios as part of your change initiatives that you can later simulate to
test for effectiveness.
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• Simulation - Run a simulation of your To-Be process. Businesses can create what-if scenarios
by using a BPMN and Decision Rules Miner in order to identify the Return on Investment
before moving forward with the change implementation, making it incredibly fast and simple
to see what benefits you are getting from the changes.
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From Process Mining to the Digital Twin of an Organization
Frequency
Performance
Cost
Business
Rules
Conformance
Checking
Automation
Reference
Model
Activities
Manual
activities
Contextual
data
Simulation
Chat
Bots
What-if
scenarios
DTO
Process
Mining
Task Mining
ROI
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-12. From Process Mining to the Digital Twin of an Organization
The journey from process mining to creating a digital twin of an organization can be depicted as
follows:
1. Frequency The traditional process mining approach derives the process model from the data,
a model based on activities and transitions. The first area to analyze is frequency of activities
that make up the process. Most processes involve manual activities without a digital footprint,
in IBM Process Mining, these manual process can be added to the process also providing
frequency information.
2. Performance A key indicator of the process is its performance in areas of duration. It’s
important to identify critical activities, resources, and roles involved in the process. From
there, you can identify the most relevant influencer from a performance perspective.
3. Costs After evaluating performance, costs need to be considered. This includes costs that are
related to resources, activities, and roles in the process. Using this information, you can
identify the most relevant influencer from a cost perspective.
4. Business Rules IBM Process Mining automatically derives the BPMN model of the process
and identifies the decision rules that are related to each transition of the process.
5. Conformance Checking With conformance checking, a model of the process can be compared
with the real process you derived from the data identifying non-conformances and deviation
and its relevance in terms of time and costs.
6. Automation Once a digital twin of your organization is created, you can monitor your
automation journey identifying the actual level of automation, resource allocation,
compliance, and conformance.
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7. Simulation Simulation capabilities allow you to define what-if scenarios and evaluate future
operational changes to your organization before implementing them. You can then evaluate
your return on investment before moving forward with the implementation.
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1.3. Planning a process mining project
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Planning
g a processs
mining
g project
Figure 1-13. Planning a process mining project
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Team composition – customer members
Project
Governance
Solution Manager
•
Oversees all the
aspects of the project
Process
Analysis
Process Owners
•
Is responsible for
managing and
overseeing the
objectives and
performance of the
process through KPI
Data
Management
IT Expert
•
Supports with data
extraction and
transformation
•
Data Expert for the
different application
Technical
Governance
Tech Leader
•
Supports with product
and ETL installation,
security and
architectures
Process Users
•
Has functional
experience over the
process
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-14. Team composition – customer members
The team composition can change based on the project complexity. Sometimes roles might not be
required, sometimes there might be overlaps. For instance, if you need to run a Proof-Of-Concept
by using one of the standard packages, only the IBM Business Analyst might be required to get
together with a Customer Process Owner.
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Team composition – IBM members
Project
Governance
Project Manager
•
Oversees all the
aspects of the project
Process
Analysis
Business Analyst
•
Has deep product
knowledge and
guides the customer
with analysis
Project planning and process analysis with IBM Process Mining
Data
Management
Data Analyst
•
Responsible for the
entire ETL pipeline
•
Analyze, prepare, and
transform data
Technical
Governance
Application Tech
•
Supports with product
installation, security,
and architectures.
© Copyright IBM Corporation 2021, 2022
Figure 1-15. Team composition – IBM members
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Typical project journey
Timeline
1
2
3
4
Stages
Process Scope
Days
Data preparation
2 - 40
1 - 10
2 - 10
2-5
2 - 20
2- 5
2-5
•
Number of
stakeholders
•
•
•
Kick-off
Inception
Workshop
Technical
deep dive
•
•
•
•
Process Analysis
2 - 10
Influencers • Process complexity • Complexity of source data
Activities
Process Validation
Number of IT application systems
Data extraction
Data Transformation
Data loading and setup
•
Number of
stakeholders
•
Customizations
•
Process
Validation
Dashboard
Customizations
•
Process
Analysis
•
Time elapsed: 45 days on average
Total working time: 20 days on average
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-16. Typical project journey
The typical project journey is composed of the following main phases:
1. Process Scope:
This phase starts with the project kick-off and ends as soon as the tool is configured and ready
to be used. The scope is part of an iterative process with the customer in the middle. It is
composed of multiple steps:
a. Kickoff:
The Kickoff is the opportunity to meet the customer and to share together all the standard
practices and to define the best methodology
b. Inception:
The Inception includes the sharing of functional and nonfunctional requirements based on
customer needs and goals. To get all is needed, you start from a pre-defined checklist.
This phase is usually run through one workshop
c. Tech Deep Dive:
The goal is to understand and define from a data perspective, functional requirements that
allow you to get what has been defined during the first Workshop.
2. Data Preparation:
This is the critical phase in terms of effort required. It includes the Environment
installation (if On-Premises or Private Cloud), the ETL setup and execution. 
The last step of the data preparation is the data loading and the project configuration.
Configure the process means to apply standard configurations if a standard package exists
or to apply custom configurations (mapping, filter templates, new dashboards, KPIs, costs,
reference model) together with the customer if a standard approach is not yet available.
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3. Process Validation
The process validation consists in a walk-through of the results in order to validate the outcomes.
This is crucial in order to verify the correctness of the results and to gain the customer’s trust. The
results need to be confirmed and validated by the customer, both from a business perspective
(the results are in line with the expectations) and from a technical perspective (what is in the
systems’ data is correctly represented by IBM PM).
During this phase, new requests could come out, which would lead to new Data Preparation
tasks.
4. Process Analysis:
The process analysis is flexible, depending on what the customer is looking for. Typically, it’s
composed by the following four main steps:
a. First Step Analysis: first-step analysis to investigate the performance and the compliance
of the process.
b. Root-cause and Advanced Analysis: drill-down on KPI and behaviors in order to discover
root-causes by using contextual data
c. Improvement Recommendation: find improvement opportunities such as Automation,
define what-if scenarios and analyze simulation results
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Typical project journey – with team members
Timeline
1
2
3
4
Stages
Process Scope
Days
IBM
Members
Customer
Members
Data preparation
Process Validation
Process Analysis
2 - 10
2 - 40
1 - 10
2 - 10
2-5
2 - 20
2- 5
2-5
•
•
Project manager
Business analyst
•
•
Data analyst
Business analyst
•
Business
Analyst
•
Business
analyst
•
•
•
•
Solution manager
Process owners
Process Users
IT Expert
•
IT Expert
•
•
Process owners
Process users
•
Process
owners
Process users
•
Time elapsed: 45 days on average
Total working time: 20 days on average
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-17. Typical project journey – with team members
The stages are discussed in more detail in the next four slides.
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Process scope
• The Scope is the first step, and it’s critical since it allows you to get from the customer the
requirements (business functional and not functional) that drive the entire analysis.
• This phase usually requires the following steps:
ƒ
ƒ
ƒ
Kickoff
í
Build/Share the project team (roles and responsibilities)
í
Share the methodology and the project plan
í
Schedule the business workshops
Inception
í
This activity usually takes four hours and just one meeting.
í
The goal of this workshop is to define the business requirements, so it is important to have all the
team on-board (customer process owner and customer process user are mandatory).
Tech Deep Dive
í
The second workshop is more technical and less business.
í
The Goal is to understand and define from a data perspective, functional requirements that allow
you to get what is defined during the first Workshop.
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-18. Process scope
The process scope is usually the main time-consuming phase, it usually takes 20 days on average.
The project duration can always change based on several variables:
• The customer commitment
• The process complexity
• The number of applications and customization involved
• The data availability and quality
• The advanced requests/needs from the customer (data transformation, custom dashboards)
First, you want to ask the customer to give a general process description about the process flow.
What they describe it is usually the Reference Model, so the flow they expect to be.
During the Inception stage, you begin defining the business requirements based on your analysis.
Things to keep in mind when analyzing:
• The process definition:
▪ Project Objectives and Pain Points: why this process? Goals? What are the commonly
known issues?
▪ Project Boundaries and Events: events that you need to include in the analysis, so the
ones you need to extract transform and load into IBM PM. You start from the events they
have described in the beginning and then you integrate with additional events (such as
deviations)
▪ Contextual data: business data that you need to include in the dataset they are going to
be used as dimensions for KPIs and business filters
▪ Actual KPI: Performance indicators that are currently being used to monitor the process
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▪ New KPI: New Indicators expected to see inside the tool (based on PM capability)
▪ Process Instance: How many process instances you need to consider (Multilevel or Flat
process? For instance, three business entities in your P2P “Orders/Goods/Invoices”)
▪ Applications involved: How many applications are used to handle the process and events
(are the applications used as standard? Any customizations?)
• Volumes:
▪ Period of the analysis: What’s the typical Lead Time? Is there any seasonality? Have been
any process change in the last period that is not made any sense to include?
▪ Data Volumes: An estimation of the data volumes based on events and number of
instances (Monthly, Yearly)
▪ Data Load: Do you need an incremental extraction/Loading or just one shot? What’s the
frequency?
▪ Number of Users: How many users are going to use the tool
• Automation
▪ Initiatives: Is there any automation initiatives on going? Is the process all human based?
Is there any automation objective?
▪ Automation task: Is there the capability to get track if an activity is executed by a human
or by a robot?
• Constraints
▪ Obfuscation: Is the resource available? Is the “user-code” ok or do you need to
obfuscate?
▪ Segregation: Is there any data segregation constraint?
When performing the Tech Deep Dive, it typically involves: Data Tech and Process User from the
customer and the Business Analyst and Data Analyst from IBM. The Goal is to understand and
define from a data perspective, functional requirements that allow you to get what is defined
during the first Workshop. Things to consider when performing the Tech Deep Dive include:
• Is it everything tracked in the application? Is there any activity you cannot get?
• How can you extract data? Is data ready to be loaded into the IBM PM tool?
• How can you link process instances between different applications (if needed)?
• How can you transform application raw data in events with the required PM format?
At the end of this phase, you have all the requirements you need, and you are ready to start the
next tech phase: the Data Preparation.
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Data preparation
• If the customer requires an On-Premises installation or a Cloud version in a private cloud,
the first step is to set up and configure the environment.
• Then, you can move to the ETL phase:
ƒ
ETL setup: install a standard package if exists or to install the preferred ETL tool
ƒ
Transform data: Customize the standard package or develop from scratch in order to prepare the
event log based on requirements that are defined in the 1st and 2nd Workshop
• Once the data is ready the next step is the Data Loading and Configuration that is composed
as follows:
ƒ
Admin configurations: Users, Permissions
ƒ
Data loading and mapping
ƒ
Standard dashboards, reference model, filters, and settings configuration
ƒ
Configuration of new Analytics dashboard and KPI
ƒ
Process Cleaning
• Remove outliers and check End activities
• Set up the model details (tradeoff between readability and insightfulness)
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-19. Data preparation
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Process validation
A customer’s process expert should validate the outcome
• Business testing: focus on specific cases to check whether their behavior matches with the data.
ƒ
Flow (activities and timestamps)
ƒ
Attributes
ƒ
Level of automation
• Resources & Roles validation
ƒ
Check if ERP’s roles are insightful from a business perspective
• Reference model
ƒ
Check whether the reference model is in line with the expectations or eventually modify it.
• Costs definition
ƒ
Define Hourly Rate for the Roles and eventually for specific Resources
ƒ
Define activity standard cost and investment costs for existing Automations
• Working Times validation
ƒ
What is the average time to complete each (main) activity?
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-20. Process validation
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Process analysis
• First-Step Analysis
ƒ
Paths and Variants analysis
ƒ
Performance analysis (time, reworks, and costs)
ƒ
Conformance and Compliance checking
ƒ
Resource monitoring and Social Network analysis
• Root Cause and Advanced Analysis
ƒ
Root-cause analysis on the pain points
ƒ
Root cause analysis on conformance checking
ƒ
Process Intelligence that uses the Analytics Dashboards (KPIs)
ƒ
Process Diff to compare different scenarios
ƒ
Decision Rules discovery
• Improvements Recommendation
ƒ
Automation Recommendations
ƒ
Define what-if scenarios and analyze simulation results
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-21. Process analysis
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Typical insights in a procure-to-pay project
This slide lists some typical insights gained in a procure-to-pay project
USD 2.4M
Maverick buyings
Purchase
request
Purchase
order
Approve
and send
PO
USD 169k
Automation
Goods and
services receipt
USD 735k
Deviations
USD 2.4M
Cash flow deficits
Invoice
Payment
USD 63k
Discount losses
Project planning and process analysis with IBM Process Mining
© Copyright
IBM
Corporation2021,
2020, 2022
2021
© Copyright
IBM
Corporation
Figure 1-22. Typical insights in a procure-to-pay project
You have an opportunity to analyze a procure-to-pay process in Exercise 2.
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Process mining and Business Process Management (BPM)
• Process mining
occurs during the
BPM descriptive
modeling phase.
This is also known
as the as-is model
• This is accomplished
during playback zero
in the IBM Playback
methodology.
Playback zero
Definition
1 - 3 weeks
Descriptive modeling
Discovery
As-is model
• Process goals
• Current state
model in
• Critical
various
success factors
formats
• Scoping
• Captured
• Process
information:
capture and
RACI, SIPOC,
documentation
and issues
Validate
Analytical modeling
Analysis
• Refine the
current state
process model
• Added value
analysis
• Root cause
analysis
• Opportunity
prioritization
• Process
simulation
Project planning and process analysis with IBM Process Mining
To-be model
Final Playback
• Business data
• Business case
with estimated
potential value
and impact
• Scope and
effort
assessment
• Process model
diagram
(BPMN)
© Copyright IBM Corporation 2021, 2022
Figure 1-23. Process mining and Business Process Management (BPM)
Business Process Management (BPM) is an integrated approach to aligning the key activities of an
organization into processes you can consistently measure to optimize value to your organization
and its end customers. Process mining can be used as part of a larger BPM project to aid in the
process discovery phase. When process mining is part of a larger BPM implementation, it is a
fundamental technology that is used during the descriptive modeling phase in which the as-is
model is created.
Process mining technology can be used throughout the BPM project to continue analysis of the
as-is process based on actual data of how the company is operating. It can also be used to
monitor the performance of the implemented automations. Processes can be rediscovered,
leveraging the new data that is created by automation, to find new inefficiencies and opportunities
for further automation.
To learn more about the BPM and process modeling, it is suggested that one explore the following
courses: Developing Workflow Solutions using IBM Business Automation Workflow V20.0.0.1 and
Developing Case Management Solutions using IBM Business Automation Workflow V20.0.0.1
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1.4. Overview of IBM Process Mining
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Overview
w off IBM
M
Processs Mining
Figure 1-24. Overview of IBM Process Mining
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Overview of IBM Process Mining
• IBM Process Mining is shipped with Cloud Pak for
Automation foundation
• It is available on any Cloud Pak offering
• The software can be deployed on-premises via the Red Hat
OpenShift operator or with a traditional installation, as well
as in the cloud with a software-as-a-service (SaaS) model
Connection
points examples
Available on
• Red Hat OpenShift (operator)
• On-premises
(traditional installer)
• Software-as-a-Service
•
•
•
•
•
Workflow interoperability
Business Rules to Decisions
RPA recommendations
Data integration for Automation
AI ingestion
Project planning and process analysis with IBM Process Mining
Process mining
From real events logged by enterprise
apps
• Discover processes from real work
• Discover lead time, cost, rework,
and conformance issues
• Discover automation opportunities
Task mining
From human actions
• Discover real work done during an activity
• Automate most frequent action paths with RPA
• Combine process and task mining to analyze
different levels of details, and get a complete
picture of your process
© Copyright IBM Corporation 2021, 2022
Figure 1-25. Overview of IBM Process Mining
• IBM Process Mining enables clients to discover and analyze business processes by using two
complimentary approaches: from the application event logs (process mining) and recording
the user behavior on desktop machines (task mining). These approaches are seamlessly
integrated and together make up one of the pillars of AI-powered automation. As you see in
the upcoming slides, they can help you to leverage automation in several different ways.
• This solution is offered as part of the Automation Foundation layer and can be used through
any available Cloud Pak offering (Business Automation, Watson AIOps, Integration, and
Network Automation). IBM Process Mining offers several connection points with different
Cloud Pak capabilities. For example, the workflow export/import features (by using the
Business Process Management Notation - BPMN), the RPA Bot recommendations, and the
data Integration in order to trigger events for automation.
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•
•
•
•
KPI and cost
Compliance
Automation
Root cause
analysis
Project planning and process analysis with IBM Process Mining
Optimizing
• Process
discovery
• Task mining
• Business rules
mining
• Multi-level
process mining
Monitoring
Discovery
IBM Process Mining capabilities
• What-if
scenarios
• Simulation
• Automation
recommendatio
n:
RPA and
Workflow
• ROI calculator
© Copyright IBM Corporation 2021, 2022
Figure 1-26. IBM Process Mining capabilities
• Looking at the process mining domain, the journey can be broken down into three steps:
discovery, monitoring, and optimizing.
• Starting with the first step, discovery, IBM’s process mining solution provides process
discovery. IBM Process Mining has three distinctive capabilities that allow organizations to
create a precise process model that reflects the present state of the business process. This
process model is called a Digital Twin of an Organization, or DTO.
• These three capabilities that make the DTO possible are:
a. Task mining, another foundational technology in the discovery phase of the automation
journey that uses an agent to record the user desktop interactions to understand the
process at the task level.
b. Business rules mining, a feature that shows not only the frequency of following a specific
path, but also uncovers why the process follows that path.
c. Multi-level process mining, a capability that allows businesses to analyze complex
processes in a single comprehensive analysis.
• A procure-to-pay process is a good example of a complex process as it involves several
different entities, like purchase requisition, purchase order, good receipt, and invoice. With
multi-level process mining, you don’t need to split the process into different analyses because
all the process parts belonging to each entity are streamlined in a single view.
• The second step is monitoring. Here, the DTO can be used to monitor business key
performance indicators (KPIs) and costs, check compliance by comparing the expected
process behavior with the real behavior that is derived from the data and uncover the root
causes of the process deviations. You can even monitor the current process automation level.
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• The last step is optimization, where you can use the DTO to create what-if scenarios of your
to-be process. The simulation feature then lets you test all future actions that you are planning
to implement on your process, check the automation recommendation that comes out of the
integrated analysis between process and task mining, and easily calculate the expected return
on investment (ROI).
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Continuous process improvement and automation
Starting from the
different
stakeholders of a
business process
Continuous process
monitoring
Process discovery
and mining
Automatically
discover the as-is
process and discover
where automation
can be implemented
With the simulation,
create a to-be model
of the discovered
process and
generate different
“what-if” scenarios
Simulation and
“What If” scenarios
Project planning and process analysis with IBM Process Mining
AI powered
automation
IBM Process Mining
is part of the Cloud
Pak for Automation
that powers AI based
solutions
© Copyright IBM Corporation 2021, 2022
Figure 1-27. Continuous process improvement and automation
• Companies can use IBM Process Mining with the execution capabilities of the Cloud Pak, like
Business Automation Workflow and Robotic Process Automation (RPA) to act on their
processes.
• Companies can implement a continuous improvement approach that can be applied to any
business process, starting from the discovery phase, then generating the as-is process
analysis, and finally by using simulation and what-if analyses to generate a to-be model to
calculate the expected return on investment (ROI).
• Then, you can select the best automation capability to improve your process based on the
analysis carried out with IBM Process Mining.
• Finally, after taking action on the process, you can continue to monitor the process with IBM
Process Mining, getting data from the new stakeholders to see whether the actions are
behaving as expected, and seeing where to take further action to improve the process.
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How is process discovery performed (1 of 2)?
Data: Logs from information systems
Data:
User’s desktop
recordings
Digital twin
organization
Analytics
Process
mining
Task
mining
Event logs (Data sources)
Discovery
Project planning and process analysis with IBM Process Mining
Scope:
Actual
work done
by
employees
© Copyright
IBM
Corporation
© Copyright
IBM
Corporation2021,
2020, 2022
2021
Figure 1-28. How is process discovery performed (1 of 2)?
• Process Mining is a technology that uses data in the form of event logs, extracted from the
applications that a process is running on, to automatically generate a visual representation of
the process.
• Process Mining requires only three pieces of information to create process visualizations:
a. The event ID
b. The activity name
c. A timestamp
• Additionally, with added task mining capabilities, it is possible to use an agent recorder to
capture user desktop interactions for a deep comprehension of the process at the task level.
• The event logs are analyzed by using process mining algorithms to discover the real picture of
a process, starting from its digital footprint, allowing you to run process analysis and
monitoring based on the real data.
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How is process discovery performed (2 of 2)?
• Depending on its complexity, a process can be defined as:
ƒ
Flat, when its activities are related to one main process entity (for example an order, a ticket)
ƒ
Multilevel, when its activities are related to multiple main process entities, with possible complex manyto-many relations between each other.
• To get more insight, it is possible to add
more information:
ƒ
Activity end time so that the system provides
an analysis of the activity service time.
ƒ
Resource carrying out the activity and the
related role so that the system automatically
provides an activity map and a social network
analysis of the resources
ƒ
Contextual data which can enhance the
perspectives of analysis (such as vendor, plant,
material).
Customer
Service
Account
Administration
Financ
e and
Ops
Executive
Management
Invoice
Reconciliation
Teams
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-29. How is process discovery performed (2 of 2)?
Case
• A case is an instance of a process.
• In flat processes, each different process-id defines a new case. For example, in a ticketing
process a case can be related to a specific ticket#.
• In multilevel processes, each different combination of process-ids defines a new case. For
example, consider a process in which two orders are received in two different moments but
registered in a unique invoice: this is considered as one single case.
Activity and event
• An event occurs every time an activity of the process is executed.
Process variant
• A process variant is a unique path that a case takes to cross the process (from the start to the
end). Being able to analyze different variants is important to identify, for example, common
deviations (regarding a normative model) and inefficiencies.
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How is task mining performed?
Task Mining is made up of many components, from the user perspective two of them are used:
1. TM Pattern Analysis and Classification: This component is a web application that runs server side
and allows the configuration of the user’s activities in terms of tasks and relevant data.
2. TM Agent: This front-end component runs on the client workstations to track the user's activities
according to the configured monitoring list. The tracked data is sent in encrypted format to the TM
Data
ata Collector to be stored in a reserved file system.
Project planning
ning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-30. How is task mining performed?
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Unit summary
• Define process mining and task mining
• Describe use cases for process mining
• List the different types of process mining
• Explain how task mining and process mining are performed
• Describe the Digital Twin of an Organization and its purpose
• Describe the typical timeline of a process mining project
• List the different roles involved in a process mining project
© Copyright IBM Corporation 2021, 2022
Figure 1-31. Unit summary
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Review questions
1. True or False: Process Mining is the discovery, monitoring, and analysis of user interaction
data on desktops through the collection of front end activities.
2. True or False: A digital twin of an organization is a software model of an organization that
relies on operational data to understand how changes to operational processes can be made.
3. Typical phases in a process mining project include (select all that apply):
a. Scope
b. Data preparation and extraction
c. Event log load
d. Automation development
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-32. Review questions
Write your answers here:
1.
2.
3.
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Review answers
1. True or False: Process Mining is the discovery, monitoring, and analysis of user interaction
data on desktops through the collection of front end activities.
The answer is False. This refers to Task Mining.
2. True or False: A digital twin of an organization is a software model of an organization that
relies on operational data to understand how changes to operational processes can be made.
3. Typical phases in a process mining project include (select all that apply):
a. Scope
b. Data preparation and extraction
c. Event log load
d. Automation development
The answer is a, b, c.
Project planning and process analysis with IBM Process Mining
© Copyright IBM Corporation 2021, 2022
Figure 1-33. Review answers
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Unit 2. Evaluating a process for RPA
candidates
Estimated time
01:00
Overview
Evaluating a process for robotic process automation candidates is a typical purpose behind
process mining. In this unit, you are introduced to the analysis functions of IBM Process Mining
that enable you to evaluate a process for potential candidates for robotic process automation.
How you will check your progress
• Review
• Exercise
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Unit objectives
• Define Robotic Process Automation (RPA)
• Explain the benefits and challenges of RPA
• Describe how RPA and process mining fit together
• Describe how to upload and map data
• List the required data fields to create the event log
• List the steps required to visualize a process
• Use IBM Process Mining to analyze a process for potential
candidates for RPA
• Use simulation to evaluate the impact of automation on the process
© Copyright IBM Corporation 2021, 2022
Figure 2-1. Unit objectives
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Topics
• Overview of Robotic Process Automation (RPA)
• RPA and process mining
• Analyzing a process by using IBM Process Mining
• Getting started with IBM Process Mining
• Using IBM Process Mining to evaluate RPA candidates
• The project overview dashboard
• Introduction to simulation
• Comparing simulation results
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-2. Topics
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2.1. Overview of Robotic Process Automation
(RPA)
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Overview
w off Roboticc
Processs Automation
n
(RPA))
Figure 2-3. Overview of Robotic Process Automation (RPA)
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What is Robotic Process Automation (RPA)?
• Automation of a wide range of administrative tasks
• Uses specific software algorithms that interact with multiple applications and
computer-centric processes
• Runs transactional processes at the user interface (UI) level
Software that mimics human
actions
Software robots (bots)
•
•
•
Software that provides a “Virtualized full-time
equivalent (FTE)”
More functionality than a desktop script or macro
Can:
ƒ Manipulate, operate, and orchestrate other
applications
ƒ Follow business rules
ƒ Run transactions
ƒ Execute program APIs and other program objects
Evaluating a process for RPA candidates
•
•
5
Taught to “drive” applications the way that a human
does through the UI
“Robot” is programmed to:
ƒ Follow predetermined computer pathways
between the screen and data repositories
ƒ Move or populate data between locations
ƒ Conduct calculations
ƒ Perform actions
ƒ Trigger downstream activities
© Copyright IBM Corporation 2021, 2022
Figure 2-4. What is Robotic Process Automation (RPA)?
RPA (Robotic Process Automation) uses digital bots to automate simple, repetitive tasks so that
humans can work on higher added value activities.
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Software robots are similar to physical robots
Physical Robots
Software Robots
Perform repetitive physical tasks
Perform repetitive software tasks
Example:
• Pick eight chocolates from assembly line
• Assemble a finished box of chocolates
Example:
• Log in to four different systems
• Browse the UI of established
applications to retrieve data
• Use data to open new account in
system of record
Evaluating a process for RPA candidates
6
© Copyright IBM Corporation 2021, 2022
Figure 2-5. Software robots are similar to physical robots
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Benefits of Robotic Process Automation
Accelerate
Time-to-value
Reduce
human error
Increase
throughput
Create, test, and
deliver new
automations in
days or weeks
Eliminate copy and
paste mistakes
that are introduced
by swivel chair
integration
Fulfill automated
tasks in seconds or
minutes, 24x7
Evaluating a process for RPA candidates
7
Decrease
development
costs
Develop bots
quickly with simple
record and
playback functions
© Copyright IBM Corporation 2021, 2022
Figure 2-6. Benefits of Robotic Process Automation
© Copyright IBM Corp. 2021, 2022
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Challenges of Robotic Process Automation
Fragility and
exception handling
• Bots are designed to work
•
•
in a specific way
Bots are not designed for
change or agility
30% of bots need
exception handling
Requires process
analysis
• Must understand
•
•
• Business and IT must
processes and select the
right activities to automate
Bots do not have human
task, case, or complex
rule capabilities
RPA is not always the
correct way to address a
process automation
problem
Evaluating a process for RPA candidates
Requires business
and IT sponsorship
8
•
•
work together
Governance of new
business and IT changes
is required
Must have a Center of
Excellence (CoE) for all
automation technologies,
not just RPA
© Copyright IBM Corporation 2021, 2022
Figure 2-7. Challenges of Robotic Process Automation
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2.2. RPA and process mining
© Copyright IBM Corp. 2021, 2022
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2-10
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RPA
A and
d processs
mining
Figure 2-8. RPA and process mining
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How RPA fits with process mining
As Is Process
Analysis and
RPA processes
selection
Automation
Level &
Compliance
Monitoring
Recorder and
Listener
(Task Mining)
IBM
Process
Mining
Available
BOTS
Business
Transactions
(Process Mining)
RPA ROI and
Performance
Monitoring
Implement
Robotic
Process
Automation
What-If and
Simulation
Analysis
Covered by IBM
Process Mining
Evaluating a process for RPA candidates
Covered by RPA
© Copyright IBM Corporation 2021, 2022
Figure 2-9. How RPA fits with process mining
IBM Process Mining can be used to evaluate candidate activities for the implementation of
Robotic Process Automation. It can also monitor the performance of the RPA implementation and
be used to increase the level of automation over time. With IBM Process Mining, you can:
• Test RPA strategies before you implement them to guarantee that every automation initiative
results in the expected impact on your end-end-process.
• Create what-If automation scenarios that specify the number of bots you want to implement,
how many hours you want your bot to run per day, the number of staff, and more.
• Monitor bot behavior post-implementation to immediately spot non-compliance and
deviations and keep track of the evolution of your automation level in your overall organization
for controlled and predictable scaleup.
• Identify inefficiencies and drive the changes that are needed for improvement. Pursue new
automation opportunities discovered by IBM Process Mining, identify where you get the
biggest ROI, and accelerate your digital transformation.
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Using task mining data
• You can use the insights that are obtained from Task Mining to automatically identify the ideal
activities for RPA within your process through complete and comparable ROI calculations based
on your target automation and complexity level.
• You can also use the insights
to gain a better understanding
of how your current bots are
meeting your automation
needs.
• Monitor bot behavior postimplementation to
immediately spot
noncompliance and deviations
and keep track of the evolution
of your automation level in
your overall organization.
Evaluating a process for RPA candidates
Desktop (Silos)
Core Business Processes
Recorder & Listener
(Task Mining)
Business Transactions
(Process Mining)
Purchase Req
Creation
BOT STORE
Requisition
Check
Purchase Req
Release
Purchase
Order Creation
Purchase
Order Release
Match invoices
w/ POs
Goods
Receipt
Create & Release Purchase
Requisition
Create Purchase Order, Goods
receipt & Invoice using SAP
Digital SAP Accounts
Payable Clerk
Upload invoices
to ERP
Payment
details to
suppliers
User View
What-If &
Simulation Analysis
Business View
© Copyright IBM Corporation 2021, 2022
Figure 2-10. Using task mining data
Task mining adds a considerable amount of data when assessing activities for RPA candidates.
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2.3. Analyzing a process by using IBM Process
Mining
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Analyzing
g a processs
by using
g IBM
M
Processs Mining
Figure 2-11. Analyzing a process by using IBM Process Mining
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Data extraction and process setup
The first step is data extraction and preparation. In this phase of the process mining analysis,
the event logs are extracted from the various applications where the process is executed.
ERP
CRM
Transactions
HR
IBM Data
Stage, App
Connect…
Data source, data mapping
Extract
Transform
Load (ETL)*
BAW
BAI
IBM Process
Mining
Task Mining
User desktop
actions
* ETL contribution packages available for P2P/SAP and O2C/SAP
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-12. Data extraction and process setup
• The first step, necessary in all process mining projects, is data extraction and preparation. In
this phase of the process mining analysis, the event logs are extracted from the various
applications where the process is executed.
• Once the data is extracted, extract-transform-load (ETL) software aggregates and transforms
the data to create a dataset that is compliant with the format that is needed by IBM Process
Mining. It is also possible to use connectors from solutions like IBM Data Stage or App
Connect to retrieve the data in an easier way. Relevant business data related to a specific
domain of the process data can enrich the dataset for a better analysis. In a procure to pay
process, for example, this business data might refer to the order type, vendor or material
group.
• Once the dataset is ready, it is time to upload it to IBM Process Mining to automatically
visualize the process.
• Task Mining has its own platform that collects the data that is recorded by the user desktop
agent. After the data is configured, it is automatically sent to IBM Process Mining to be
visualized.
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Discover frequencies, variants, rework, durations, costs
• Instantly visualize
the real processes
1
2
3
4
• Most frequent
activities, reworks,
and variants can be
good automation
candidates for RPA
or Workflow
• Highest wait-time
and durations show
potential
bottlenecks
• Identify costly
activities based on
resource cost
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-13. Discover frequencies, variants, rework, durations, costs
With the dataset uploaded and visualized by process mining, you can start analyzing the process.
The visual representation of the process that IBM Process Mining creates is intuitive. Activities are
shown as boxes and the transitions between the activities are shown as arrows.
1. You can immediately identify the most frequent activities
2. Or you can identify the most critical activities in terms of waiting or execution time
3. You can understand the most frequent process paths. In this example, there is a variant that
covers 27.8% of the total cases.
4. You can also identify the most expensive activities based on the resource and activity cost
All of this can help you identify the critical activities that need to be monitored, improved, and
possibly automated.
The slide refers to the terms “rework” and “variant”. Rework refers to an activity executed twice
in the same case. Deviation is an activity that was executed but was not expected.
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Check conformance with reference models
• Quickly identify
nonconformant cases
• Import as-designed
BPMN reference models
from any Business
Process Automation
Tool that supports
BPMN
• Discover cost of
nonconformance,
fitness level
List of deviations
and statistics
Nonconformant activity
and transitions
• Run a root-cause
analysis
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-14. Check conformance with reference models
• The next step is conformance checking. This feature compares the expected process behavior
with the actual data-derived behavior. With full interoperability of IBM Process Mining with the
Business Process Management Notation (BPMN) standard, the reference model can be
imported from Blueworks Live, IBM Business Automation Workflow (BAW), or any tool that
supports the BPMN standard.
• The conformance check provides information about the conformant cases, a list of deviations,
and statistics that show the impact that the deviation has on the process in terms of
performance and cost. Root cause analysis reveals which business data is the biggest
influencer of a specific deviation.
• A deviation is an activity that was executed but was not expected. A non-conformant activity is
an activity that has been executed, even if it is not included in the reference model.
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Generate a BPMN model from real processes
• BPMN model generated
from events, filters can
be applied (exclude
running cases, exclude
nonconformant cases,
keep most frequent
variant, and so on)
• Export BPMN to
any Business Process
Automation tool that
supports BPMN for
finer modelling
Process roles
Process activity
Process gateway
• Export BPMN to Business Process Management tool that supports BPMN for implementing
workflows
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-15. Generate a BPMN model from real processes
• Because IBM Process Mining fully supports Business Process Management Notation (BPMN),
it is possible to generate the BPMN model starting from the data-derived process model, using
it as an entry point for a modelling analysis on IBM Blueworks Live (or any kind of Business
Process Analysis tool) or as a starting point for a workflow implementation on IBM Business
Automation Workflow (BAW).
• The picture shows an example of a BPMN model that is derived from the data in IBM Process
Mining.
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Generate DMN decision tables with rules mining
• Rules mining discovers the business logic
behind the gateways
Statistics of
coverage and
precision of
each business
rule discovered
• This feature uses the uploaded business
data together with the event log to show
why the process follows a specific route
• You can set the business data to
consider the details level
• You can implement the business logic in
any Business Rules Engine software
Business rule discovered
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-16. Generate DMN decision tables with rules mining
• A key differentiator of IBM Process Mining is the Decision Rules Mining capability. This feature
uses the uploaded business data together with the event log to show why the process follows
a specific route, as well as the frequency that the route is followed.
• In this way, you can understand the logic behind the gateways of a process; in other words,
you can discover the implicit business rules and understand why the process is following one
path rather than another.
• With the newly discovered rules, you can leverage new automation, implementing it on the
Operation Decision Manager (ODM) or on the Automation Decision Services (ADS).
• Decision Model and Notation (DMN) is a modelling language and notation for the precise
specification of business decisions and business rules.
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Set and monitor KPIs
• A crucial step during
process analysis is defining
and monitoring key
performance indicators
(KPIs) for a process.
• Set KPIs based on case
lead-time and cost, activity
throughput, activity waittime, resource allocation
and cost
Critical
activities
• Monitor in process views
and dashboards
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-17. Set and monitor KPIs
IBM Process Mining can define a full stack of KPIs based on time and resource allocation to
instantly show which activities are not performing as expected (red boxes) and to get a high-level
picture of the overall process.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Monitor processes with dashboards
• Generic overview dashboards
• Customizable business dashboards built
from widgets with low-code UI
• Out-of-the box Procure to Pay and Order
to Cash dashboards such as Maverick
purchases
30+ widgets like
bar chart, bubble
chart, line chart
to be customized
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-18. Monitor processes with dashboards
• Most of the time, standard key performance indicators (KPIs) are not enough to answer the
business needs of the client or to provide a better understanding of business process pain
points.
• When performing a process analysis, it is important to check for insights that are related to the
business domain of the process. Think about a procure to pay process. Crucial insights might
include the amount that is ordered with each vendor, or to see the distribution over the time of
the total amount.
• To answer these kinds of questions, the Advanced Analytics tool in IBM Process Mining allows
the creation of custom business dashboards, by using a low-code user interface. The features
provide more than 30 different widgets with a configuration similar to Excel.
• The slide refers to the term “Maverick purchases“. This describes the purchasing situation in
which services and materials are purchased without following the purchasing and
procurement policies and best practices.
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Simulate your business with digital twin before engaging
development costs
• Create scenarios from
as-is process: allocating
resources, adding RPA
bots, and changing
processes
Compare the
simulation with the
data derived
analysis
• Run simulations and
compare as-is and to-be
side by side: processes,
KPIs, costs
• Load BPMN models
from any tool that
supports BPMN and run
simulations to verify
achievement of
business goals
Select the expected
level of automation
of a specific activity
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-19. Simulate your business with digital twin before engaging development costs
• After the analysis is complete, it is time to act on the insights, by using the Cloud Paks for
Automation capabilities.
• Common questions include:
▪ Is there a way to test automation initiatives before implementing them?
▪ And how can you calculate the expected return on investment (ROI) from specific actions
on the process?
• Using the Digital Twin of an Organization (DTO) model derived from the data, along with the
simulation capability, you can play with the model, testing the actions that you have in mind.
For example, you can add more full-time equivalent (FTE) resources working on a specific
activity, or you can simulate an RPA Bot implementation on a specific task where you found
numerous repetitive reworks.
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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2.4. Getting started with IBM Process Mining
© Copyright IBM Corp. 2021, 2022
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Getting
g started
d with
h
IBM
M Processs Mining
Figure 2-20. Getting started with IBM Process Mining
© Copyright IBM Corp. 2021, 2022
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Navigating IBM Process Mining
• When you start IBM Process Mining, you are presented with the workspace.
• After you open a project, IBM Process Mining presents a menu bar for all the central features
• You start with defining the data source.
• After importing, you are taken to the Model view.
• In the Model view, you can analyze your process.
• The remaining views perform the following:
ƒ
BPMN: view your process by using Business Process Model and Notation (BPMN)
ƒ
Dashboard: process statistics including dashboards
ƒ
Diff: allows you to compare two different process behaviors identified by filter templates
ƒ
Activity map: which resources and roles are involved in which activities
ƒ
Social net: allows you to visualize relationships within the process
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-21. Navigating IBM Process Mining
© Copyright IBM Corp. 2021, 2022
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Creating a new process
To create a new project:
1
2
1. Sign in to your account.
2. In the workspace, click
Create process.
3. Provide a
title and
organization
and click
Create
process.
3
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-22. Creating a new process
In the workspace, you can create, navigate, delete, and organize your processes and
organizations.
Each box represents a process. Each process belongs to an organization.
You can share organizations with others in your company.
After scoping the project and creating the new process in the workspace, you can begin importing
data.
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Upload your data source (1 of 2)
• When you open the process, you are presented with the data source feature.
• This is where you upload the data.
• Click Select data source file to upload the data to be mapped.
• You can select Append to existing to upload multiple data sources.
• If you have a
reference model
from a prior
implementation, you
can use that to help
visualize differences
and variations.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-23. Upload your data source (1 of 2)
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Upload your data source (2 of 2)
The data that is uploaded is known as an
event log where each row is an event.
• Three fields are required to upload data:
Process ID, Activity type, Activity start time
The following fields are not necessary,
but provide more insight:
• Activity end time – to see activities’
duration in your process
• Resource – will show the relationship
between activities and resources
• Role – will group resources in relation to
activities
• Any other field that you might require can
be tagged as a custom field
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-24. Upload your data source (2 of 2)
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Map relevant data columns (1 of 2)
• After you upload the data, you must
map it.
• You need to define what each field
represents.
• You do this by highlighting the
column, then selecting the mapped
data field.
• Use the clear button to undue.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-25. Map relevant data columns (1 of 2)
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Map relevant data columns (2 of 2)
• IBM Process Mining recognizes
patterns in the data automatically
and allow you to select it.
• After completing all the data
mappings, click Visualize your
process to start analyzing the
process.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-26. Map relevant data columns (2 of 2)
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Backups and reference models
Backups
• When importing a data source, if you have a backup model, you can import that instead of
performing the data mapping.
• Backup files are saved with
an .idp extension.
Reference models
• The reference model can be
imported directly or as part of
a backup file.
• A reference model is required
to perform conformance
checking.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-27. Backups and reference models
• A backup is a package which contains a set of configurations to be automatically applied on
the project, possibly including data mapping, analytics dashboards, cost settings and
reference model. IBM Process Mining provides backups for worldwide industry processes
such as procure-to-pay and order-to-cash.
• The reference model depicts the process as it is understood, not as it is implemented. A
reference model is compared with the derived model to perform conformance checking and to
determine activities being performed that are not forecasted in the reference model.
Conformance checking is covered in more detail later in this unit.
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2.5. Using IBM Process Mining to evaluate RPA
candidates
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Using
g IBM
M Processs
Mining
g to
o evaluate
e
RPA
A candidates
Figure 2-28. Using IBM Process Mining to evaluate RPA candidates
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The Frequency model
The
Frequency
view is the
default
model view
of the
process. In
the
Frequency
view, you can
immediately
see the most
frequent
activities and
transitions
the process
took.
1
3
2
4
5
6
7
8
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-29. The Frequency model
In the Frequency view, the process is represented with boxes and arrows. Each box represents an
event or an activity that happened in the process. The arrows represent the various transitions
that the process took based on the event log. Within each box, the roles that are involved in the
activity are listed along with the frequency it is executed. The dark blue boxes represent the most
frequent activities. Refer to the lower right for the color mapping to the frequency number. The
darker the arrows, the higher the frequency. While in the model view, you can perform the
following:
1. You can navigate throughout the user interface by using the top menu selecting the main
features:
- Datasource, dashboard, Diff, Model, BPMN, Activity map, and Social net.
2. The toolbar allows you to:
- Create new filters, edit activity aliases, export the process and data, navigate between
BPA and Analytics, and edit the settings.
3. You can navigate back to the workspace to view your processes by clicking the breadcrumb
trail at the top.
4. While in the model view, you can choose to view it in either Portrait or Landscape mode.
5. You can open and close the left navigation panel by clicking the arrow.
6. You can check the model’s conformance by opening one of two panels on the right:
- Case Variants panel (arrows): detects the most frequent paths to perform the process
and their efficiency in terms of completion time.
- Conformance Check panel (eye): compare the data-derived model with the reference
model and automatically detect deviations and their impact on process time and cost.
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7. You can select different perspectives for the model view including Frequency (shown on the
slide), Rework, Average duration, Median duration, Minimum duration, Maximum duration,
Weighted average duration, Cost, and Overall cost.
8. You can view different information regarding the process by navigating the panel on the left.
While in the Model view, you can filter what is viewed. You can also get information regarding
model details and log statistics.
In the Frequency view, the most frequent paths between activities of the process can be
identified.
• The numbers next to the lines shows how many times that specific process flow has been
followed.
• The numbers within the rectangles shows the number of times that the activity is performed
• The description in the rectangles indicates the name of the activity and the roles by which the
activity is carried out. They could be more than one (multiple dots are displayed).
• The green circle at the lower right corner of the activity rectangle indicates the Model coverage
(100% indicates that the Model details cover all the possible relationships of that activity. The
percentage indicates how many possible relationships you are currently visualizing. The level
of relations is adjustable)
Note: beside the frequency of a transition, a number in parentheses could be present: that
represents the number of events of the same case, which was executed in parallel on multiple
activities.
When evaluating activities for Robotic Process Automation, investigate the most frequent
activities with the highest duration.
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Performance views
• Apart from the Frequency view, IBM Process
Mining offers performance views based on:
ƒ
Rework: The Rework view highlights the activities
and transitions with rework and displays the
automation level for each activity.
ƒ
Duration: View the process by median, minimum,
maximum, or weighted average duration.
ƒ
Cost: The average or overall cost of each activity
is displayed based on previously defined cost
settings.
• The cost of an activity depends on its standard cost, the cost of the resources involved and
its working time, according to the IBM Process Mining cost model:
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-30. Performance views
The next six slides cover these three views in more detail.
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Rework view
• There are two kinds of rework
ƒ
If you can see an arrow that goes
out and falls into the same activity,
it is called a self-loop
ƒ
When for the same case, the
activity is repeated several times
but passes by other activities, this
is also called rework
• Within the small red rectangle
with the gear icon, it is indicated
the “Automated instance ratio”
(the % of automation of the
selected activity)
• Within the activity rectangles, you can find the number of cases that cause rework, and
you can see the average times that the activity is repeated during each case
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-31. Rework view
Note that, in multilevel processes:
• If an activity related to one process-id (such as a process entity) is repeated, this will be
considered a rework (for example, the same order has many changes on the delivery date)
• If an activity related to multiple process-ids is repeated, this is not always a rework (for
example, the same order has been received in separate moments, this is not a rework).
© Copyright IBM Corp. 2021, 2022
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Duration views
• IBM Process Mining has performance views that provide information on:
ƒ
Service time also called Activity Duration (only if End-Time information is available): Each activity is
provided with its duration time (average, minimum, maximum or median). According to the lower
right legend, dark red represents the longest one.
ƒ
Waiting time: All the transitions between the activities are accompanied by the waiting time
between the activities (average, minimum, maximum or median). The longer the waiting time, the
thicker the line.
• Sliding the KPI palette button activates the KPI analysis view, based on previously defined
KPI Settings.
• Also, in the performance view, it is possible to run the Animation analysis. Each activity has
a type of traffic light signal that allows you to have real-time information about the
performances.
Throughput time = Service time + Waiting time
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-32. Duration views
Throughput time of an activity is the aggregate of its Service and Waiting time: it expresses the
total time required to pass through the activity.
Note that, if only Start Time is mapped in the data source, the activity Throughput time will be
totally expressed by its Waiting time (this happens, for example, when the data source is
extracted from SAP).
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KPI Analysis view
• Green box: activity with service time in line with the
defined KPIs
• Yellow box: activity with risky service time
• Red box: activity with critical service time
• Green arrow: transition with waiting time in line with the
defined KPIs
• Yellow arrow: transition with risky waiting time
• Red arrow: transition with critical waiting time
Evaluating a process for RPA candidates
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Figure 2-33. KPI Analysis view
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Animation
The Animation section allows you to
visually replay the history of your
process based on the analysis period
you uploaded. IBM Process Mining can
reproduce the path of each instance
(token).
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-34. Animation
The Animation section allows you to visually replay the history of your process based on the
analysis period you uploaded. IBM Process Mining can reproduce the path of each instance
(token). You can choose to visualize a more or less detailed time granularity from the Resolution
scroll bar. You can also choose the calendar day from which to start the animation.
• By moving the slider, you can reach a specific time in the process history
• By using the “Play” button, you can start the animation
• The “Tokens” toggle allows you to hide process instances (red bullets) from the view
• The Resolution slider allows you to increase or reduce the time granularity of the animation
• By clicking the lower right, you can choose the exact date at which you want to start the
animation.
Animation is typically performed using one of the duration views.
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Cost views
• All the input parameters that are relative to the costs can
vary according to the duration and the type of activity
(automatic or manual) and to the resources that carry it
out.
ƒ
Activity standard cost = standard cost of the execution of an
activity
ƒ
Work time = working time for the activity
ƒ
Resource cost = hourly cost that is applied to a specific
resource
ƒ
Role cost = an hourly cost that is applied to a specific role
• The cost of an activity depends on its standard cost, the
cost of the resources involved and its working time,
according to the IBM Process Mining cost model:
Activity Cost = Activity Standard cost + (Avg(Work Time) * Avg(Resource or Role Cost)
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-35. Cost views
The cost model is discussed in more detail in the next two slides.
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Understanding the cost model (1 of 2)
• All the input parameters that are relative to the costs can vary according to the duration
and the type of activity (automatic or manual) and to the resources that carry it out.
• The work time is the assumed time to execute an activity without interruptions. It should
always be minor or equal to the relative service time, which includes interruptions.
• The activity work time can vary depending on the manual or robotic execution of the
activity.
• As work time
for an activity can
vary over time, you
can define the End
date in which the
specified work
time has to be
applied to the
activity.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-36. Understanding the cost model (1 of 2)
A cost model is manually entered. It can also be located in a backup file. Cost values are not
created as result of creating the derived model. When entering a cost model, it is possible to:
• Define a default value for all activities
• Define a cost value for a specific activity
• Define whether the cost value refers to the manual activity, the automatic activity or both
• Define the date until which the specified cost has to be applied to the activity (an activity
standard cost might vary over time).
Resource and role cost might also vary over time: you can define the date until which the specified
cost has to be considered for the resource or role.
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Understanding the cost model (2 of 2)
• Using the cost values entered in the cost model, you can
calculate activity costs based on work time.
ƒ Activity standard cost = standard cost of the execution of an
activity
ƒ Work time = working time for the activity
ƒ Resource cost = hourly cost that is applied to a specific
resource
ƒ Role cost = an hourly cost that is applied to a specific role
ƒ Service time = total time per activity including interruptions
Work Time
Working on other tasks
Unproductive
Activity timeline
Service Time
Activity
completed
Activity
started
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-37. Understanding the cost model (2 of 2)
Recall the cost of an activity depends on its standard cost, the cost of the resources involved and
its working time, according to the IBM Process Mining cost model:
Activity Cost = Activity Standard cost + (Avg(Work Time) * Avg(Resource or Role Cost)
When evaluating activities for potential replacement with Robotic Process Automation, you want
to look at the costliest manual activities.
Hint
To introduce human costs into the model, the most suitable way is to set the Working time for
“manual” activities, plus role costs for the different user roles.
To introduce automation costs (such as maintenance and depreciation) into the model, the best
way is to set Activity Standard costs for automatic activities.
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Performing a path analysis (1 of 2)
• Selecting the case variants tab on the right opens the Model
conformance summary and Case variants panels.
• The case variants panel displays event log statistics aggregated by
dimensions such as case structure, activity, resource, and role.
• The event log statistics can be sorted by either frequency or throughput.
By default, they are sorted by frequency in descending order.
Evaluating a process for RPA candidates
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Figure 2-38. Performing a path analysis (1 of 2)
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Performing a path analysis (2 of 2)
• Selecting a path highlights the activities in the model that participate in
that path.
• Notice the activities that are not
involved are not highlighted and only
one path is displayed.
• This allows you to evaluate the path
in terms of performance.
• You can also evaluate which
activities are involved in most of the
paths.
• Activities with high frequency are
typically involved with the main
process paths and are good
candidates for Robotic Process
Automation
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-39. Performing a path analysis (2 of 2)
When working on process and business improvements you typically want to work with the top 3-5
most frequent process variants and exclude running cases. To do this you can create and save
filters that satisfy these or any other requirements you might have.
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Evaluating model conformance
• Selecting the conformance tab on the right opens the Model
conformance summary and the conformance statistics.
• A case is called conformant when every activity and process flow
is compliant to the reference model.
• IBM Process Mining changes the model conformance from Dataderived to Compare. Both the derived and reference models are
displayed. Differences between the model are highlighted in red
and yellow.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-40. Evaluating model conformance
When performing conformance checking, keep in mind:
• Similarity is calculated through the comparison of the reference model with the derived
model; 1 means that the models are 100% compliant, 0% means that the models are
completely different.
• Fitness defines how actual cases “fit” in the data-derived model. Some paths might be hidden
in the model, depending on the model details, therefore the paths of some cases might not be
represented.
The Reference pane (as displayed on the right of the slide), displays model deviations, which can
be unexpected activities or process flows. The Reference pane:
• Indicates in what percentage the deviation occurs in the process variants
• Displays the number of cases in which it occurs
• Shows the steps involved (how many activities does the process have on average when the
deviation occurs)
• Displays the average cost of the cases in which the deviation is involved
• Displays the average throughput time of the cases in which the deviation is involved
You can filter on a specific deviation to visualize only the cases on which it occurs, or you can filter
it out by selecting "exclude".
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Performing root cause analysis on a deviation
• While the conformance tab is open, you can click any of the
unexpected flows or activities to perform a root cause analysis
on it.
• IBM Process Mining provides more details regarding the
deviation that you can use to better understand why it occurred.
• Based on the custom fields entered, you might have information
about the main influencers of the deviation.
• Depending on the
influence of the rootcause, it might be
colored red, yellow,
or green.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-41. Performing root cause analysis on a deviation
Root cause analysis is based on advanced AI algorithms which allow to recognize complex
correlations between events and contextual data. Moreover, you can also select a specific
unexpected activity or process flow, obtaining some information about the potential root cause.
Based on the custom fields entered, you can have information about how many cases (where the
deviation is present) involve a particular resource, role, supplier, product, company, and so on.
For every detected root-cause condition, you can see:
• Percent of cases where the root-cause is verified, out of the number of cases where the
deviation occurs
• In the example screenshot, the condition on Supplier is always (100%) verified when the
deviation occurs - but the deviation occurs only on 20% of cases.
• Strength of the correlation (Strong, medium, weak).
The Model conformance pane can be used to indicate:
• Number of conformant and non-conformant cases
(A case is called conformant when every activity is compliant to the reference model)
• Avg throughput time of both
• Avg steps per case of both
• Avg case cost of both
The Reference model violations pane shows the reference model deviations, which can be
unexpected activities or process flows.
• It indicates in what percentage the deviation occurs in the process variants.
• Number of cases in which it occurs.
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• Steps (how many activities does the process have on average when the deviation occurs).
• Average cost of the cases in which the deviation is involved.
• Average throughput time of the cases in which the deviation is involved.
You can filter on a specific deviation to visualize only the cases on which it occurs, or you can filter
it out by selecting "exclude".
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Performing task mining on an activity
• Task Mining is the discovery, monitoring, and analysis
of user interaction data on a desktop.
• While business data in your operational systems
describes a process by telling you which and when
steps have occurred, user interaction data is
everything done by people to accomplish those steps.
• The integration between Process Mining (business data) and Task Mining
(user interaction data) allows you to discover:
ƒ
What happens during the execution or waiting time of a business activity?
ƒ
What each employee does, the actual time spent on each task, and idle time.
• Task Mining can also manage your process analysis on its own. User
interaction data can be used to discover, monitor, and analyze a process
that is not recorded in any of your operational systems.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-42. Performing task mining on an activity
The environment that you are using must have Task Mining enabled to perform task mining
functions. The trial environment does not support task mining.
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Using dashboards to evaluate KPIs (1 of 2)
• For each process
loaded in IBM
Process Mining, a
Project overview
dashboard is
generated
automatically for
you.
• Dashboards visualize
process data (event
logs) by using one or
more widgets
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-43. Using dashboards to evaluate KPIs (1 of 2)
Dashboards are used to explore, represent, and filter process data by using up to eight widgets
(for a single dashboard).
Widgets are fundamentally a query with one or more configuration flags. The widget displays the
query in one of various ways (such as line chart, bubble chart, and bar chart).
Important
It is important to note that Dashboard filters are applied on top of the filters that are applied on
the event log (from the IBM Process Mining application).
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Using dashboards to evaluate KPIs (2 of 2)
• You can incorporate KPIs on various models including the average duration
model.
• Activities are colored according to whether they are meeting the KPI.
• KPIs consist of two thresholds that are manually entered.
• The color appears green (ok) if the actual value is lower than the first
threshold, yellow (warning) if it is between the two thresholds and red
(danger) if it is higher than the second threshold.
• Activities that are not meeting expected KPIs are potential candidates for
Robotic Process Automation.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-44. Using dashboards to evaluate KPIs (2 of 2)
To access process Settings, click the Settings icon in the navigation bar in the upper right and
select Settings.
You can set the following KPIs for the Performance analysis:
• Case duration thresholds
Acceptable time limit that can elapse between the start and the end of the process.
• Case cost thresholds
Acceptable cost limit that can occur between the start and the end of the process according to
the defined cost model.
• Activity
Possibility of setting KPIs for all (default) or a specific activity.
• Activity throughput thresholds
Acceptable time limit that can elapse when it passes through an activity (waiting time +
execution).
• Activity wait queue thresholds
Acceptable time limit of the waiting queue.
• Activity duration thresholds
Acceptable time limit of activity duration
• Resource allocation thresholds
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Acceptable limit of resources allocation for the activity; useful in the Animation, it compares
the current number of allocated resources with the maximum number available for the
activity.
By defining the KPI settings, you can visualize which activities are risky or critical for the process
KPIs.
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2.6. The project overview dashboard
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The
e projectt
overview
w dashboard
Figure 2-45. The project overview dashboard
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Project overview dashboard (1 of 4)
KPI SUMMARY
CASE STATUS
CASE LIST
PROCESS MODEL
CASE VARIANTS
ADD FILTER
SUMMARY
LEAD TIME DISTRIBUTION
LEAD TIME INFLUENCERS
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-46. Project overview dashboard (1 of 4)
You can access the Project overview dashboard from the Applications menu.
You have an opportunity to work with the Project overview dashboard in the exercise at the end of
this unit.
You can use the KPI Summary widget to further filter the activities by the various KPI limits. When
the filter is applied, the widgets of the dashboard are updated.
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Project overview dashboard (2 of 4)
Case status widget
• The Case status widget is a pie chart representing the number
of running and completed cases.
• By clicking a segment, you can filter the dashboard.
Case list widget
• The Case list widget displays the top 50 cases that are sorted
by Lead time.
Filter widget
• When you apply a filter to the dashboard, it is listed here.
Evaluating a process for RPA candidates
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Figure 2-47. Project overview dashboard (2 of 4)
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Project overview dashboard (3 of 4)
KPI Summary
• The KPI summary widget shows the number of cases
that fall within the specified KPIs. By selecting a type,
all widgets are updated.
Leadtime influencers
• This widget displays the percentage of influence for
the top influencers. By clicking one of the influencers,
the widget displays the corresponding number of
cases and average case lead time.
Leadtime distribution
• The Lead time distribution widget displays the lead
time distribution of cases. This is useful in identifying
outliers.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-48. Project overview dashboard (3 of 4)
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Project overview dashboard (4 of 4)
Process model
• The Process model widget displays the frequency, duration,
and cost models.
• Select and compare multiple variants from the Case variants
widget, by activating the Analyze variants toggle.
• You can also apply KPIs when viewing the duration.
Case variants
• This widget displays the case variants.
• You can select multiple variants in order to compare them in
the model. Selected variants are not used as filters in the
dashboard unless you click the filter button.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-49. Project overview dashboard (4 of 4)
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2.7. Introduction to simulation
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Introduction
n to
o
simulation
Figure 2-50. Introduction to simulation
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Introduction to simulation (1 of 2)
• The Simulation feature makes future predictions by simulating the Return on Investment (ROI)
before you implement any process improvement initiative, such as Robotic Process Automation
(RPA).
• You can create or
manage simulation
scenarios from the
BPMN feature. You
can also access
existing scenarios by
using the BPA tool.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-51. Introduction to simulation (1 of 2)
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Introduction to simulation (2 of 2)
• You can obtain simulation scenarios that are based on your real process from the derived
BPMN. You can also create simulations from scratch by starting from the BPA where you can
use the BPMN diagram
from external sources
that integrate with
IBM Process Mining.
• Every simulation
scenario is based on
a BPMN diagram.
The BPMN diagram
can be automatically
derived from your
data.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-52. Introduction to simulation (2 of 2)
The BPA module is covered in more detail in Unit 4.
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Simulation settings (1 of 2)
• After you create a new simulation scenario or choose an existing scenario, IBM Process Mining
automatically generates a pre-calculated Simulation specification based on historical and
contextual
data of the
process.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-53. Simulation settings (1 of 2)
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Simulation settings (2 of 2)
• In the Simulation Settings, you set a historical arrival distribution and specify the actual
working hours by inserting “8-18” and excluding weekends.
• With these settings, the results obtained closely resemble reality.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-54. Simulation settings (2 of 2)
In the Simulation specification section, you can make any necessary adjustments to run the
simulation:
• Version
Simulation version’s name
• Description of the Simulation version
• Number of instances
Number of cases that is generated by the Simulation
• Generate Relevant-Data
The Simulation generates relevant data that will reflects the distribution of the real process.
Underlying correlations are discovered and replicated in the simulation.
• Adapt Staff Availability to Full-Time Equivalent (FTE)
Since the "Staff Availability" is gathered from the real process, the measure might be
inconsistent when the "Resource" field is not corresponding to a specific resources (but
maybe to an office). By enabling this option, when "Staff Availability" is lower than the FTE
calculated by IBM Process Mining, the FTE is used as "Staff Availability".
• Arrival Distribution
The distribution of the generation of cases, which can be generated by the following
Distribution Algorithms:
▪ Uniform
▪ Exponential
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▪ Logarithmic
▪ Historical (reproduces the reality from the actual process)
• Base time unit
By default, the time is precise to minutes. By selecting seconds, it can be increased to
seconds.
• Index
Considers the median or the average time of the actual process to set the default of the
Simulation specification. According to this choice, IBM Process Mining automatically sets in
the simulation all the activities service and waiting times that are retrieved from the data
derived model.
• Start date
Date from which to start the simulated events.
• Business hours (Exclude weekends)
The working time in which the service and waiting times are considered. By setting 0-24, you
are considering the time referred to the entire day (24 h). By setting, for example, 8-18, you
are considering only this timeframe to evaluate service and waiting times. By checking
Exclude weekends, you are not considering weekends in the times evaluation.
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Simulation header
The fixed Simulation header automatically updates when any adjustments are made to the
configuration of the scenario. The Simulation header includes the following actions:
• Run Simulation
to start the simulation scenario and generate the event logs from the current configurations
• Versions
ƒ
Compare scenarios
ƒ
Make a copy of a scenario
ƒ
View simulation results of the last run of the scenario (the relative simulation must have been run at least
once)
ƒ
Delete the scenario.
• Save, Edit, or Delete the simulation
• View Results of the last simulation that was run
Evaluating a process for RPA candidates
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Figure 2-55. Simulation header
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Activity parameters
• In the Settings of each activity, you can change different performance indicators to run the
simulation:
ƒ
FTE (fixed and pre-calculated): the number of full-time resources that could have been allocated to
an activity in order to obtain its “As-Is” performance, based on the activity working time. Consider
that this is not a suggested FTE for the activity, as the “As-Is” performance might not be optimal.
ƒ
Staff availability: the number of resources available to carry out an activity; this value is
automatically retrieved from the data source.
ƒ
Service time: the activity’s service time; this value is present only if the service time is available in the
data source.
ƒ
Working time: the time that it
took a resource to carry out an
activity without interruptions;
this value can be previously set
in the process settings.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-56. Activity parameters
Each activity can be configured for its settings, scheduling, potential automation, and waiting
times. The slide displays the Settings tab. You can configure the remaining tabs as follows:
• Scheduling
To force the instant of the start of an activity based on the user’s configuration. In the
Scheduling of each activity, you are able to change different performance indicators to run the
simulation:
Business hours: The activity can be carried out only during specific hours.
Enable calendar: Sets a calendar frequency.
Enable scheduler: Sets a scheduled frequency.
• Waiting time
The Waiting time is the time that is elapsed between the end of the last activity and the
beginning of the current activity.
In the Waiting times of each activity, you can change the waiting time of both manual and
robotic tasks for any incoming activities to run the simulation. The default values are
automatically retrieved from the data source.
The next slide covers the configuration settings for the RPA tab.
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Simulating automation
• In the RPA tab of each activity, you can configure the data from the activity in the case that it is
carried out automatically through a robot.
• To evaluate the impact of using Robotic Process Automation to execute activities, you can
update the simulation configuration to use bots instead of people.
• This can aid in determining potential candidates for Robotic Process Automation.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-57. Simulating automation
• You can change the following performance indicators to evaluate the use of robotic process
automation on a process:
▪ Robotic quote: the percentage of the activity that will be managed by robots
▪ Business hours: the business hours of the robots
▪ Number of robots: the number of robots that work in parallel with the staff to carry out the
activity
▪ Service time (of the robot): the service time of the robots
• Note that the service time of a robot is equal to its working time, as no interruptions should
occur in the robotic activities.
• If an automatic activity attribute is configured in the project settings, the RPA default values
(excluding business hours) are automatically retrieved from the data source.
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Gateway parameters
• For each Gateway, IBM Process Mining automatically generates a pre-calculated Simulation
specification based on all the historical and contextual data of the process.
• The specification can include the conditions which are the decision rules that are discovered
before creating the new scenario or linked to a decision table while editing the BPMN model.
• For each path out of the gateway, you can configure its probability of execution.
• You can edit the following parameters:
ƒ
Probability of the Activity
ƒ
Probability of the Gateway
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-58. Gateway parameters
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Simulation warnings
• IBM Process Mining automatically reveals configuration issues that might affect the simulation
results. The warnings that it provides include:
ƒ
Warnings list: a list
of activities that
might have
configuration
issues. When
clicking Check, you
are directed to the
issue
ƒ
Yellow warning
triangle: undefined
or missing data
ƒ
Red warning
message:
parameter
limitations
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-59. Simulation warnings
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2.8. Comparing simulation results
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Comparing
g
simulation
n results
Figure 2-60. Comparing simulation results
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Creating a new simulation
• IBM Process Mining Simulation allows
you to create multiple scenarios by using
historical and contextual data from the
process.
• If there are no simulation scenarios that
are linked to the project, the panel
displays the option to create a new
scenario.
• It is also possible to create a new version of an
existing simulation.
• You can then make changes to the configuration
and compare the results of the two simulations.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-61. Creating a new simulation
• If an existing simulation is already associated with an IBM Process Mining project, the new
version is linked to the same project.
• If a new version of the same simulation is created by starting from the same BPMN model, the
new version of the simulation inherits the configuration of the previous version.
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Performing a process diff (1 of 2)
• When you run a new simulation based on process mining
data, it compares the results of the simulation (To-Be) with
the original process statistics (As-Is)
• When you run a new version of a simulation (not based on
process mining data), IBM Process Mining performs a
comparison of the original (A) and the new simulation
scenario (B)
• You can view the comparison on the Diff tab in IBM Process
Mining
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2020, 2021
Figure 2-62. Performing a process diff (1 of 2)
After running the updated simulation,
• If you are not satisfied with the generated data, you can update the scenario and rerun the
simulation to evaluate the parameter changes.
• If you are satisfied with the results, you can click the Import button to add the generated
events to the main model.
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Performing a process diff (2 of 2)
• The Diff comparison provides the following statistics:
ƒ
Process overview
ƒ
Case duration and count
ƒ
ƒ
Compare derived model
Performance and KPI
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-63. Performing a process diff (1 of 2)
The Diff comparison provides the following statistics:
• Process overview
▪ Number of Cases
▪ Average Lead Time
▪ Average and Total Case cost
• Case duration and count
▪ Purple Line: Average remaining duration of active cases in the selected date for A
▪ Blue Line: Average remaining duration of active cases in the selected date for B
▪ Blue Area: Number of active cases in the selected date for A
▪ Orange Area: Number of active cases in the selected date for B
• Performance and KPI
▪ Light blue bar: Average queue waiting time in A
▪ Dark blue bar: Average service time in A
▪ Blue line: Frequency in A
▪ Light Purple bar: Average queue waiting time in B
▪ Dark Purple bar: Average service time in B
▪ Purple line: Frequency in B
• Compare derived model
▪ Blue Transition: Transition that appears only in the model relative to A
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▪ Purple Transition: Transition that only appears in the model relative to B
▪ Gray Transition: Transition that appears in both the models
▪ Activities with a blue and purple rectangle: Activities that are present in both models
▪ Activities with a blue rectangle only: Activities that are only present in the model relative to
A
▪ Activities with a purple rectangle only: Activities that are only present in the model relative
to B
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Unit summary
• Define Robotic Process Automation (RPA)
• Explain the benefits and challenges of RPA
• Describe how RPA and process mining fit together
• Describe how to upload and map data
• List the required data fields to create the event log
• List the steps required to visualize a process
• Use IBM Process Mining to analyze a process for potential
candidates for RPA
• Use simulation to evaluate the impact of automation on the process
© Copyright IBM Corporation 2021, 2022
Figure 2-64. Unit summary
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Review questions
1. True or False: To perform conformance checking on a process, a reference model is required
to be uploaded.
2. True or False: The Digital Twin of an Organization is a perfect digital copy of your process that
uses the most current business data.
3. Activities that are potential Robotic Process Automation (RPA) candidates include (select all
that apply):
a. Activities high in duration
b. Costly activities
c. Manual activities
d. Activities that meet KPIs
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-65. Review questions
Write your answers here:
1.
2.
3.
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Review answers
1. True or False: To perform conformance checking on a process, a reference model is required
to be uploaded.
2. True The Digital Twin of an Organization is a perfect digital copy of your process that uses the
most current business data.
3. Activities that are potential Robotic Process Automation (RPA) candidates include (select all
that apply):
a. Activities high in duration
b. Costly activities
c. Manual activities
d. Activities that meet KPIs
The answer is a, b, c.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-66. Review answers
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Exercise 1: Evaluate a process for RPA candidates
• Invenio Bank wants to improve its
business processes.
• Specifically, it is having issues with
the Bank Account Closure process.
• As a Business Process Analyst, you
analyze the bank accounting
process for RPA candidates and
make a recommendation based on
your analysis.
Evaluating a process for RPA candidates
© Copyright IBM Corporation 2021, 2022
Figure 2-67. Exercise 1: Evaluate a process for RPA candidates
Use Case Description
When a customer of the bank requests the closure of their account, the bank must take a series of
actions to ensure that the closure takes place correctly. As this process is totally human-based, as
a Business Process Analyst, you want to evaluate the benefit of automating activities within this
critical process. With IBM Process Mining, you can do this by starting from the real process data.
By analyzing the process, you determine a potential activity that could be automated through
Robotic Process Automation.
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Exercise
objectives
• Import a reference model to perform conformance checking
• Create and visualize a process
• Analyze the process for bottlenecks
• Review activity durations in a process
• Evaluate manual rework
• Evaluate high-cost activities
• Perform a path analysis
• Review model deviations
• Import a reference model for conformance checking
• Compare performance values against KPI expectations
• View the frequency, duration, and cost models of a process
© Copyright IBM Corporation 2021, 2022
Figure 2-68. Exercise objectives
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Unit 3. Advanced data analysis
Estimated time
01:00
Overview
In this unit, a deeper dive into understanding and preparing data for a process mining project is
undertaken. Activities involved in data preparation are highlighted along with a deeper analysis of
data quality issues and data profiling. The multi-level approach to event log creation is compared
to the standard approach. You learn how to identify maverick buying patterns and how to use IBM
Process Mining dashboards to add insight. You also learn how to create custom dashboards.
How you will check your progress
• Review
• Exercise
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Unit objectives
• Identify the activities involved with data preparation
• Understand data quality and data quality issues
• Explain the difference between the classical and multi-level
approach to event log creation
• Understand the data relationships in a multi-level process
• Explain how to identify maverick buying patterns
• Describe how to apply custom filters
• Explain how to create custom dashboards
© Copyright IBM Corporation 2020, 2021
Figure 3-1. Unit objectives
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Topics
• Data preparation
• Evaluating maverick buying
• Analyzing maverick buying by using custom dashboards
• Creating custom dashboards
• The procure-to-pay process
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-2. Topics
© Copyright IBM Corp. 2021, 2022
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3.1. Data preparation
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Data
a preparation
n
Figure 3-3. Data preparation
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Activities involved in data preparation
DATA PREPARATION
PROCESS SCOPE
DATA
UNDERSTANDING
DATA
TRANSFORMATION
PROCESS
CONFIGURATION
DEFINE PROJECT
COLLECT
COLLECT
INITIAL
DATA
DATA
SELECT DATA
SELECT
DATA
UPLOAD DATA
IDENTIFY BUSINESS
OBJECTIVES
DESCRIBE DATA
CLEAN DATA
MAPPING DATA
EXPLORE AND VERIFY
EXPLORE DATA
DATA QUALITY
TRASFORM DATA
DEFINE DATA
VERIFY DATA QUALITY
TRASFORMATION LOGICS
FORMAT DATA
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-4. Activities involved in data preparation
Data preparation is the first step in the project after defining the project scope.
It first involves understanding the data, then transforming the data.
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Data sources
• How to load data into IBM Process Mining?
ƒ
Scripts in Linux shell, PERL, Python
ƒ
Hardcoded jobs in Java, C#, C
ƒ
In-house built ETL tools
ƒ
Off-the shelf ETL tools
• Aspects to keep in mind:
ƒ
Maintainability
ƒ
Manageability
ƒ
Transparency
ƒ
Complexity
ƒ
Scalability
ƒ
Flexibility
ƒ
Auditing
ƒ
Testing
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-5. Data sources
IBM Process Mining can receive data many ways. You can manually import files with extensions:
.csv, .xes, or .zip.
The level of effort to prepare the data can vary and is based on many factors.
• Process complexity
• Number of Applications involved
• Input Data Quality
• Data Transformation complexity
• Number of staging areas
• ETL jobs production-ready
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Why Data Quality matters
• Technology heterogeneity:
ƒ
Different technologies
ƒ
Different platforms
ƒ
Large amount of data being generated everyday
• Typical problems with data
ƒ
Duplicated
ƒ
Inconsistent
ƒ
Ambiguous
ƒ
Incomplete
• Not caring about Data Quality is the perfect recipe forr a disaster:
ƒ
Poor data Æ poor analyses Æ poor decision making
ƒ
Poor data quality propagates from application to application
tion
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-6. Why Data Quality matters
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Data quality issues: an example
Multiple bills or invoices might refer to the same individual.
• Invoice 1
• Invoice 2
• Invoice 3
• Invoice 4
Bill no
CustomerName
SSN
101
Mr. Aleck Stevenson
ADWPS10017
Bill no
CustomerName
SSN
205
Mr. S Aleck
ADWPS10017
Bill no
CustomerName
SSN
314
Mr. Stevenson Aleck
ADWPS10017
Bill no
CustomerName
SSN
316
Mr. Alec Stevenson
ADWPS10017
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-7. Data quality issues: an example
To model an accurate representation of your data, you must qualify the data. In the example on
this slide, the customer name is different for each invoice (bill no) but the SSN is the same. This
indicates these are all referring to the same customer.
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Data Profiling
Data profiling is the process of examining the data available from an existing information source
(such as a database or a file) and collecting statistics or informative summaries about that data.
• Main benefits
ƒ
Helps understand content, structure, relationships
ƒ
Assesses data quality, including whether the data conforms to particular standards or patterns
ƒ
Assists the discovery of anomalies in data
ƒ
Understanding data challenges early in any data intensive project so that late project surprises are avoided.
Finding data problems late in the project can lead to delays and cost overruns.
• How?
ƒ
Applying statistical algorithms
ƒ
Providing reports on data distribution
ƒ
Automatically analyzing (logical) foreign key constraints
• How to apply it
ƒ
Writing SQL queries on sample data extracts
ƒ
Using data profiling tools
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-8. Data Profiling
Because it’s impossible to manually review every record and relation on a modern enterprise
application, data profiling becomes increasingly important.
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Data Profiling common analysis
• NULL values
ƒ
Look out for the number of NULL values in an attribute
• Candidate keys
ƒ
Such as analysis of the extent to which certain columns are distinct
• Primary key selection
ƒ
To check whether the candidate primary key violates the basic requirements of not having NULL or
duplicate values
• Empty string values
• String length
• Identification of cardinality
• Data format
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-9. Data Profiling common analysis
This slide lists some common analysis as a result of performing data profiling.
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Event log creation – classic approach
• Mandatory fields: Necessary. Never empty.
MANDATORY FIELDS
ATTRIBUTES
ATTRIBUTES
• Event attribute: Resource and role involved
• Custom event attributes: Additional information about the event
CASE ID
ACTIVITY
DATETIME
RESOURCE
CUSTOMER
203011
Order Line Creation
10/01/17 15:00
Pete Scott
Union inc.
229010
Order Line Creation
10/01/17 16:00
Pete Scott
Plastic
203011
Header Block Removed
10/01/17 15:30
Sarah Jones
Union inc.
203011
Lgst Block Removed
10/01/17 15:40
Sarah Jones
Union inc.
229010
Lgst Block Removed
10/01/17 17:00
Carol Hope
Plastic
229011
Delivery Creation
12/01/17 12:00
Pete Scott
Plastic
229011
Good Issue
13/01/17 16:00
Robert Knox
Plastic
203010
Delivery Creation
12/01/17 13:00
Carol Hope
Union inc.
214012
Order Line Creation
15/01/17 12:00
Sarah Jones
Meridian
214012
Lgst Block Removed
15/01/17 13:00
Pete Scott
Meridian
MANDATORY FIELDS
Advanced data analysis
CONTEXTUAL DATA
© Copyright IBM Corporation 2020, 2021
Figure 3-10. Event log creation – classic approach
The traditional approach of process mining considers a process case as a sequence of activities
that are related to the lifecycle of single entity. Using this approach, a process case is uniquely
identified by the ID of the business entity whose lifecycle is described by the process.
Unfortunately, this approach fails if applied to a multi-level process, due to data
divergence/convergence problem.
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Event log creation – multi-level approach
• Mandatory fields: Necessary. Never empty.
MANDATORY FIELDS
ATTRIBUTES
ATTRIBUTES
• Event attribute: Resource and role involved
• Custom event attributes: Additional information about the event
SUBCASE ID
(PURC. REQ)
SUBCASE ID
(PURC. ORDER)
SUBCASE ID
(RECEIPT)
X
SUBCASE ID
(INVOICE)
ACTIVITY
DATETIME
RESOURCE
CUSTOMER
Requisition Line Created
12/12/16 15:00
Pete Scott
Union inc.
Requisition Released
14/12/16 16:00
Pete Scott
Plastic
Order Line Created
02/01/17 15:30
Sarah Jones
Union inc.
4500150844_20
Order Approved
02/01/17 15:40
Sarah Jones
Union inc.
4500150844_20
Order Changed: Delivery Date
02/01/17 17:00
Carol Hope
Plastic
4500150844_20
Order Changed: Delivery Date
02/01/17 12:00
Pete Scott
Plastic
10144725_10
10144725_10
10144725_10 4500150844_20
4500150844_20
5000531890_1
Goods Line Registered
02/01/17 16:00
Robert Knox
Plastic
4500150844_20
5000531890_2
Goods Line Registered
18/01/17 13:00
Carol Hope
Union inc.
5000531890_1
3017000643_2017
Invoice Registered
08/02/17 12:00
Sarah Jones
Meridian
5000531890_2
3017000643_2017
Invoice Registered
08/02/17 12:00
Sarah Jones
Meridian
3017000643_2017
Invoice Cleared
15/03/17 13:00
Pete Scott
Meridian
PRIMARY ID
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-11. Event log creation – multi-level approach
The solution that is provided by IBM Process Mining starts from a rethinking of the concept of a
process case for a multi-level process. No case ID should be provided, only subcase IDs, one for
every business entity involved in the process. During the discovery phase, IBM Process Mining
considers that a single event can belong to one or more cases. This means that it’s considered just
one time to avoid data divergence and data convergence. At the end of process discovery, IBM
Process Mining normalizes the set of events belonging to the same process case in order to obtain
the right multiplicity between subprocesses, so that throughput time, resource allocation and cost
allocation are correctly calculated. In this sense, IBM Process Mining can present in a single
model, several derived processes.
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Best practices to generate and map a data source for
multilevel process mining analysis
• The generation and mapping of a data source for multilevel process mining analysis should
follow specific best practices in order to have a correct and consistent outcome.
• A multilevel data source contains a different column for each entity (ProcessID) involved in
the process. For example, a simple procure-to-pay process might contain four different
columns, related to purchase requisition ID, purchase order ID, receipt ID, and invoice ID
• In the displayed example, the
behavior is the following:
ƒ
ƒ
ƒ
One order is created from one
requisition and then released
The order is received in two
different goods receipts
The two goods receipts are
registered and paid in one
single invoice.
Advanced data analysis
SUBCASE ID
(PURC. REQ)
SUBCASE ID
(PURC. ORDER)
SUBCASE ID
(RECEIPT)
SUBCASE ID
(INVOICE)
ACTIVITY
DATETIME
10144725_10
Requisition Line Created
12/12/16 15:00
10144725_10
Requisition Released
14/12/16 16:00
Order Line Created
02/01/17 15:30
4500150844_20
Order Approved
02/01/17 15:40
4500150844_20
Order Changed: Delivery Date
02/01/17 17:00
4500150844_20
Order Changed: Delivery Date
02/01/17 12:00
10144725_10 4500150844_20
4500150844_20
5000531890_1
Goods Line Registered
02/01/17 16:00
4500150844_20
5000531890_2
Goods Line Registered
18/01/17 13:00
5000531890_1
3017000643_2017
Invoice Registered
08/02/17 12:00
5000531890_2
3017000643_2017
Invoice Registered
08/02/17 12:00
3017000643_2017
Invoice Cleared
15/03/17 13:00
© Copyright IBM Corporation 2020, 2021
Figure 3-12. Best practices to generate and map a data source for multilevel process mining analysis
Data source generation
• On the data preparation side, in addition to correctly populating the columns with the
respective entityID (ProcessID), you must make sure to create the relationship between
those entities: to do so, you must identify "bridge activities" within the process. A bridge
activity is typically representing the creation of an entity and should never have reworks on the
same entityID. For example, in a procure-to-pay process, typical bridge activities are:
▪ Order Creation: it represents the creation of the Order entity, which may be linked to one
Requisition entity. The same OrderID is never created twice (no reworks expected).
▪ Goods Receipt: it represents the registration of a Receipt entity, which is always linked to
at least one Order entity. The same ReceiptID is never created twice (no reworks
expected).
▪ Invoice Registration: it represents the registration of an Invoice entity, which may be
linked either to a Receipt or to an Order. The same InvoiceID is never created twice (no
reworks expected).
• Once identified the bridge activities, you must correctly populate the corresponding records in
the data source:
▪ The bridge activity must contain the respective entityID (that is, Invoice registration must
have InvoiceID populated)
▪ Generate one record of the bridge activity for every linked entityID (for example, an
InvoiceID is linked to four different ReceiptID’s . Four invoice registration activities must
be created; InvoiceID keeps the same value, while ReceiptID is always different).
- If multiple records are generated for the same bridge activity, they must have the same
timestamp.
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- Process Mining will recognize the bridge activity and will manage it accordingly with
frequency 1, even if the record is repeated.
▪ Never populate more than two entityIDs in the same record (in other words, you cannot
populate InvoiceID, ReceiptID and OrderID in the same Invoice registration activity
record).
• It's important to follow the functional/logical flow of the process while populating the bridge
activities: for example, in a procure-to-pay process, we never populate the InvoiceID in the
Order creation bridge activity, because the Invoice is supposed to be generated after the
Order. If an expected flow occurs (such as invoice activities before order creation), IBM
Process Mining will be able to handle it autonomously.
• All the non-bridge activities should contain only the respective ID (no links with other entities).
For example, in a procure-to-pay process, Order release activity is referring only to Order
entity.
Data source mapping
Even during the mapping of the ProcessID’s, it's important to follow the functional / logical flow of
the process. For example, in a procure-to-pay process, you must map the entities with the
following orders:
1. RequisitionID as ProcessID;
2. OrderID as ProcessID2:
3. ReceiptID as ProcessID3;
4. InvoiceID as ProcessID4.
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Multi-level process example (1 of 3)
• Scenario:
ƒ
Four items are purchased through two distinct requisitions, to a single vendor.
ƒ
The procurement department processes these requisitions and creates a single order, with one line for
each item.
ƒ
After a while, the goods are received in the warehouse and registered in a single good receipt with one
line for each good.
ƒ
A few days later, the accounting department receives and registers the invoice, and proceeds to the
payment.
• If the multi-level process is imported incorrectly, cost
figures are not accurate.
• The next three slides use the example above to
demonstrate what is provided with and without using
multi-level importing.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-13. Multi-level process example (1 of 3)
Even more critical, multilevel processes create an accurate model from which we can simulate
changes in one subprocess and measure the impact on other subprocesses. Increasing the
efficiency of requisitions/order could create new bottlenecks at the invoice level that we would be
able to detect. Without multilevel processes no such detection would be possible since we would
just model a 1:1 relationship between each subprocesses.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Multi-level process example (2 of 3)
• With a multilevel process import, the events are associated to a single case
since they all end-up with a single invoice. This way the correct statistics are
obtained: when cost is computed, the invoice is only counted once.
• In the process mining project settings, activity cost is declared to be $20
(simplified). The costs are calculated correctly since the invoice activity only
occurs once.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-14. Multi-level process example (2 of 3)
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Multi-level process example (3 of 3)
• Without a multilevel process import, you would have a
case for each requisition, leading to as many invoices.
Statistics would be wrong as you would count the invoice
cost four times.
Frequency View
Cost View
• You would then need to add a caseID column that
identifies uniquely each combination of subprocesses.
You would have to associate the single invoice to each
combination of requisition, order, and goods, resulting in
duplication of data.
• The model frequency view would show the expected
wrong four invoices.
• Looking at the cost view, you would obtain the wrong cost
as each invoice-related activity is counted four times.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-15. Multi-level process example (3 of 3)
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Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
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Data relationships in a multi-level process
• For a multi-level process, the process case is dynamically identified by discovering the
correlation among the business entities that take part in a single occurrence of the process.
• The business entities are mapped in the event log according to
their mutual relationships, so that it’s possible to build their actual
correlation while parsing the event log.
• When you import a multi-level process, a Case statistics panel
appears to display the colors that are associated with each
subcase ID.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-16. Data relationships in a multi-level process
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3.2. Evaluating unexpected flows
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Evaluating
g
unexpected
d flows
Figure 3-17. Evaluating unexpected flows
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Identifying unexpected flows
• Unexpected flows are processes not following the company's policies and best practices. This
makes the coordination with the process stakeholders more difficult, which can also worsen
the performance.
• Unexpected flows typically generate higher human costs for managing the process because
they usually require more human effort and exceptions block any automation flow.
• For example, in a procure-to-pay process, unexpected flows usually generate maverick
buying, including off-contract methods of procurement, and nonauthorized purchases.
• You can use IBM Process Mining to help you identify any unexpected flow.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-18. Identifying unexpected flows
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3-22
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Filtering unexpected flows (1 of 2)
Invoices without an order
• To filter invoices without a purchase order, create the following filter:
ƒ
Include cases that include the activity Invoice Registered. Set the filter
operation to include matched cases at the start of the case.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-19. Filtering unexpected flows (1 of 2)
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Filtering unexpected flows (2 of 2)
Orders without a purchase requisition
• To filter orders without a purchase requisition, create three separate filters:
1.
Exclude cases that include the
activity Requisition Created
í
2.
Include cases that include the
activity Order Item Created
í
3.
Set the filter operation to exclude
matched cases.
Set the filter operation to include
matched cases.
Exclude running cases
í
Set the filter operation to include
matched cases.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-20. Filtering unexpected flows (2 of 2)
The combination of the three filters can be saved as a filter template. The prior filter and the one
listed on this slide have been created for you to review in the lab at the end of this unit.
You use these filters to view dashboards in the next section.
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Path filters
• Path filters enable the selection of two or more activities in sequence. They filter
the processes where the flows follow that sequence: this makes it easy to filter an
unexpected flow.
• Choose the flow precedence conditions and the rework boundaries, based on a
selected attribute (for example, Activity), by using the following syntax:
• Depending on which Event attribute is selected, different flow precedence
conditions become available. You can also select the path to be filtered by
selecting the activities on the process map
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-21. Path filters
Path Filters are one of the most used features of IBM Process Mining.
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Path time filters
• After selecting a Process flow pattern based on Activity attribute, you can filter it in terms of
path duration.
•
Over X days
Filters cases in which the selected path has a duration of more than X days.
•
Under X days
Filters cases in which the selected path has a duration of less or exactly X days.
• In multilevel processes, in which the path can be covered multiple times. The Path time filter
is applied if at least one path matches with the duration specification.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-22. Path time filters
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Applying filters
• To load an existing filter, access the filter
template menu, select the filter, and
click Load.
• The filters are applied to the model and
cases updated.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-23. Applying filters
The filters that are discussed are used in dashboards to represent case statistics for each of the
two maverick buying scenarios. These are discussed in the next section.
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3.3. Analyzing maverick buying using custom
dashboards
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Analyzing
g maverickk
buying
g byy using
g
custom
m dashboards
Figure 3-24. Analyzing maverick buying using custom dashboards
In this topic, a custom dashboard built for identifying maverick buying is used to evaluate the
maverick buying patterns of a process. You get a chance to review this dashboard in the exercise
at the end of this unit. Maverick buying is an example of an unexpected flow. They are typically
found in procure-to-pay processes.
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Example: A custom dashboard for maverick buying (1 of 4)
CARD
LINE CHART
PROCESS
MODEL
BAR CHART
BUBBLE CHART
TABLE
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-25. Example: A custom dashboard for maverick buying (1 of 4)
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Example: A custom dashboard for maverick buying (2 of 4)
• The filters that are discussed are used in
dashboards to represent statistics and KPIs for a
maverick buying scenario.
• You can access these dashboards by opening the
Analytics portion of the tool under the Application
menu.
• You can apply a filter template to the dashboard
by selecting it from the filter dropdown.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-26. Example: A custom dashboard for maverick buying (2 of 4)
When the Order-Maverick template can is selected, the combination of three filters are applied:
• Exclude cases that include the activity Requisition Created
• Include cases that include the activity Order Item Created
• Exclude running cases
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Example: A custom dashboard for maverick buying (3 of 4)
By applying the OrderMaverick filter
• Navigation bar: From here you can select other projects, dashboards, and filters.
template, the custom
You can also import, export, delete, and create new dashboards.
dashboard displays.
• Order Details: Displays the count of distinct maverick orders and order items
along with the sum of maverick order amounts. Expected savings is a benchmark
assuming part of the maverick orders can be avoided, which can lead to better
price and discount conditions and reduced order management costs
• Trend Order Maverick: Displays case volumes and dates of maverick orders
versus total orders.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-27. Example: A custom dashboard for maverick buying (3 of 4)
The Edit function is available in the navigation bar because the Order Maverick dashboard is a
custom dashboard.
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Example: A custom dashboard for maverick buying (4 of 4)
• Order statistics by
Vendor: Displays the
count of order items and
the associated average
lead time.
• Bubble chart: Displays
the average lead time
duration and sum of order
amount.
• Order Details: Displays a
table of order items and
their contextual
information.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-28. Example: A custom dashboard for maverick buying (4 of 4)
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3.4. Creating custom dashboards
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Creating
g custom
m
dashboards
Figure 3-29. Creating custom dashboards
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Creating custom dashboards
• When you click
Edit in the
navigation bar,
the dashboard
editor is opened.
• You can change
the settings or
delete any of the
widgets on the
dashboard.
• You can add up
eight widgets per
dashboard.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-30. Creating custom dashboards
Any custom dashboard can be edited directly. It’s a good practice to make a copy of a dashboard
before making changes to it.
From the dashboard menu, you can:
• Modify the dashboard information including the dashboard name and its privacy settings
• Set the dashboard as default, which means that the current dashboard opens when you open
analytics of the process
• Create a new dashboard from scratch
• Create a copy of the current dashboard to use it as a template for a new one
• Import a dashboard
• Export the current dashboard
• Delete the current dashboard (this cannot be done for Process overview)
Once the Dashboard is created, the owner can set the visibility of the custom Dashboard as:
• Private
• Shared with users within the same organization, both readable and editable
The Dashboard widgets are categorized in the following boxes:
• Filter summary
• Process widget
• Histogram widget
• Table
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• Card
• KPI
• AI widget
• Custom widget
There are one or more widgets for each category. In the screen capture, the Process widget is
selected.
In general, there are three types of widgets:
• Configurable widgets: these are the standard widgets you can configure with a low-code
SQL-like approach.
▪ Bar chart, Pie chart, Table, Card and many others.
• Preconfigured widgets: these are widgets made available by IBM Process Mining –every
preconfigured widget has its own configuration, but generically speaking you don’t have total
control of what the widget will display.
▪ Process model, Lead time distribution, Case KPI summary and many others.
• Custom widgets: these are widgets created by the end users, who have the possibility to
design both backend and frontend logics and the graphical representation, using JavaScript
code.
▪ The widgets used in the Maverick dashboards are examples of custom widgets.
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Configuring widgets
When you
click settings
for a widget, it
displays the
measures,
filters, and
configuration
settings for
the widget.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-31. Configuring widgets
This slide shows the configuration details for the Order Details card widget.
When configuring widgets, keep in mind:
• Measures are the quantitative statistics and KPIs you want to measure.
• Dimensions are the elements you want to focus on while you measure the KPIs(for example,
vendors, plants, materials), for comparison and correlation purposes.
• Filters are expressions you use to focus only on specific process events, while measuring the
KPIs.
Note: always consider the filtering hierarchy: process mining filters > dashboard filters >
widget filters.
All the widgets of Analytics are based on the Event Log as a unique source of data. While you are
configuring a widget, always consider the Event Log as a table on which you are performing
queries.
The next four slides cover the configurations for the widgets in the Maverick buying dashboard.
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Card widget
• The card widget provides you
the ability to display one or
more indicators.
• The card widget can apply
different filters at measure
level through the “Measure
Filtering” toggle.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-32. Card widget
There are three configuration settings for the Measure Filtering toggle:
• Apply All: all filters are applied on the measure
• Ignore All: no filters are applied on the measure, except for the ones on the process mining
side.
• No Template: the measure doesn't consider the filter template applied on the dashboard but
consider the other dashboard filters (and of course the filters applied on the process mining
side).
Hint
With "Measure Filtering" toggle, you can just insert the same indicators twice within the same
card, half with "Apply All" option and half with "Ignore All" option.
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Line chart widget
• The line chart widget displays the
evolution of one or more values over a
time span.
• You can add up to five measures.
• It can apply different filters at measure
levels.
• Measures are represented on the y-axis
• Dimensions are represented on the x-axis.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-33. Line chart widget
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Bar chart widget
• The bar chart widget displays selected
data in a bar chart.
• It is possible to filter the dashboard by
selecting one or more bars.
• You can add up to two measures and can
apply different filters at the measure level.
• Measures are represented on the y-axis
• Dimensions are represented on the x-axis.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-34. Bar chart widget
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Bubble chart widget
• The bubble chart widget is a scatterplotlike visualization that lets you represent
up to two measures (bullet color and size)
over two dimensions.
• You can apply a filter on both dimensions
by clicking a bubble.
• Bubble sorting is executed based on the
first measure.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-35. Bubble chart widget
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Table widget
• The table widget displays a tabular
representation of your data.
• You can add up to five measures.
• Measures are names of the columns.
• Dimensions are represented by rows.
• You can apply a filter on the dashboard by
clicking a row.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-36. Table widget
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Unit summary
• Identify the activities involved with data preparation
• Understand data quality and data quality issues
• Explain the difference between the classical and multi-level
approach to event log creation
• Understand the data relationships in a multi-level process
• Explain how to identify maverick buying patterns
• Describe how to apply custom filters
• Explain how to create custom dashboards
© Copyright IBM Corporation 2020, 2021
Figure 3-37. Unit summary
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Review questions
1. True or False: IBM Process Mining considers that a single event can belong to one or more
cases so that it is considered just one time to avoid data divergence and data convergence.
2. True or False: In a procure-to-pay process, maverick buying is a term describing purchase
orders with a requisition or invoices with a purchase order.
3. True or False: Filters cannot be shared between the model view and dashboards.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-38. Review questions
Write your answers here:
1.
2.
3.
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Review answers
1. True or False: IBM Process Mining considers that a single event can belong to one or more
cases so that it is considered just one time to avoid data divergence and data convergence.
2. True or False: In a procure-to-pay process, maverick buying is a term describing purchase
orders with a requisition or invoices with a purchase order.
Maverick buying is a term describing purchase orders without a requisition or invoices without a
purchase order.
3. True or False : Filters cannot be shared between the model view and dashboards.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-39. Review answers
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Exercise 2: Evaluating maverick buying in a multi-level
process
• Invenio Auto Sport needs to better
understand the maverick buying
patterns of a multi-level process.
• As a Data Analyst, you evaluate a
multi-level process for
inefficiencies and analyze maverick
buying patterns.
Advanced data analysis
© Copyright IBM Corporation 2020, 2021
Figure 3-40. Exercise 2: Evaluating maverick buying in a multi-level process
In this exercise, you evaluate a procure-to-pay, multi-level process for data discrepancies and to
identify maverick buying patterns.
For this lab exercise, the data has been extracted for you as an export from the company’s
enterprise resource planning system.
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Exercise
objectives
• Analyze the model of a multi-level process
• Create a custom filter
• Evaluate rework and a self-loop
• Evaluate the cost of deviations by using custom dashboards
• Evaluate cash discount losses by using custom dashboards
• Evaluate maverick buying by using custom dashboards
• Create a custom dashboard and set it as the default dashboard
© Copyright IBM Corporation 2020, 2021
Figure 3-41. Exercise objectives
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Unit 4. Using simulation and the BPA
tool
Estimated time
01:00
Overview
This unit introduces you to simulation and the BPA module. You are introduced to the Business
Process Model and Notation standard and how it is used in Blueworks Live and IBM Process
Mining. You use the BPA module to import a Blueworks Live process and perform simulations on
the BPMN model.
How you will check your progress
• Review
• Exercise
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Unit objectives
• Explain how to navigate the BPA module
• Understand process, application, and organization landscapes
• Explain how to manually draw BPMN and DMN diagrams
• Define Business Process Model and Notation (BPMN)
• Describe how to use Blueworks Live (BWL) to export BPMN models
• Understand how the fields are mapped from BWL to IBM Process
Mining
• Understand how simulations can be used to evaluate automation
• Explain how to import BPMN models into IBM Process Mining
• Describe how to run simulations on BPMN models
• Describe how to perform a diff comparison between two simulation
scenarios
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Figure 4-1. Unit objectives
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Topics
• Introduction to the Business Process Analysis (BPA) module
• Introduction to Business Process Model and Notation (BPMN)
• Introduction to Blueworks Live
• Creating and running a simulation of a Blueworks Live process
• Comparing simulation results
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-2. Topics
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4.1. Introduction to the Business Process
Analysis (BPA) module
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Introduction
n to
o the
e
Businesss Processs
Analysiss (BPA))
module
e
Figure 4-3. Introduction to the Business Process Analysis (BPA) module
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The Business Process Analysis (BPA) module
• The Business Process Analysis module lets you incrementally represent the
processes, roles, and systems relevant for your organization.
• The elements of the BPA module are grouped by IBM Process Mining
organization and shareable with the members of the same organization.
• You can also incrementally derive
your model from your data.
• Each element can be crossreferenced to others to obtain a
unique and consistent view of
your company.
• The web tooling is provided by
bpmn.io.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-4. The Business Process Analysis (BPA) module
IBM Process Mining is made up of four different modules:
• Process mining
• Analytics
• BPA
• Admin
Up to this point, you have used the Process mining and Analytics modules. The Admin module
allows you manage users, groups, and authorizations. This unit covers the BPA module.
You can create process models, application landscapes, organizational landscapes, or process
landscapes by using the BPA editor tool. These are covered in the next slides.
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Process landscape
• The Process
landscape allows
you to define the
layout and the
connection
between process
models.
• Processes that
are defined in a
process
landscape can
reference the
actual process
model, dataderived or they
can be manually
defined.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-5. Process landscape
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Application landscape
• The Application
landscape
allows you to
define the
layout and the
connection of
the systems
that are used in
your company.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-6. Application landscape
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Organization landscape
• The Organization
landscape
shows the
structure of an
organization and
the relationships
and relative
ranks of its
roles, positions,
and jobs.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-7. Organization landscape
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Process
• You can
manually draw
the process
model in
standard BPMN
2.0 notation by
using the BPA
model editor.
• The Process
model can be
exported in
standard BPMN
2.0.
• You can also import process models that use the BPMN 2.0 standard from tools such as
Blueworks Live.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-8. Process
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Simulations
• You can access
all previously
created
simulation
processes and
scenarios.
• You can also
create a
simulation
scenario from
scratch.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-9. Simulations
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-11
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Unit 4. Using simulation and the BPA tool
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DMN
• You can
manually draw
the decision
rules model in
the standard
Decision Model
and Notation by
using the DMN
model editor.
• You can also
create a decision
table from the
decision model.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-10. DMN
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-12
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Unit 4. Using simulation and the BPA tool
Uempty
4.2. Introduction to Business Process Model
and Notation (BPMN)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-13
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Unit 4. Using simulation and the BPA tool
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Introduction
n to
o
Businesss Processs
Modell and
d Notation
n
(BPMN)
Figure 4-11. Introduction to Business Process Model and Notation (BPMN)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-14
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Unit 4. Using simulation and the BPA tool
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About BPMN
• The standard flow chart-based notation for defining business processes
• Creates a standardized bridge for the gap between business process design
and process implementation
• IBM Business Automation Workflow's Process Designer uses several core
elements from BPMN
ƒ
Lane
ƒ
Activity
ƒ
Gateway
ƒ
Event
ƒ
Annotation
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-12. About BPMN
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-15
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Unit 4. Using simulation and the BPA tool
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BPMN elements
• Flow Objects
Events
Activities
Gateways
• Connecting Objects
Sequence Flows
Message Flows
Associations
• Pool and lanes
Pool
Lanes (within the Pool)
• Artifacts
Data Object
Annotation
Using simulation and the BPA tool
Grouping
© Copyright IBM Corporation 2020, 2021
Figure 4-13. BPMN elements
At a high level, focusing on the flow objects helps to understand the specification.
• Events: Each process begins and ends with an event. No process exists independently and is
normally linked to other processes or services through events.
• Activities: Activities are the tasks that are performed by humans and systems.
• Gateways: Gateways are similar to decision diamonds in flow chart notation. They direct the
sequence of the process based on an evaluation.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-16
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Unit 4. Using simulation and the BPA tool
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BPMN model example (1 of 2)
• Order processing BPMN model
Customer
Order Processing
No
Completed order form
Skip order
Vendor
Receive order from
customer
Fill order
Close order
Yes
Order accepted?
Send invoice
Make payment
Accept payment
Invoice
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-14. BPMN model example (1 of 2)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-17
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Unit 4. Using simulation and the BPA tool
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BPMN model example (2 of 2)
• Order processing BPMN model
Start Event
Data Object
Gateways
End Event
Sequence Flow
Swimlanes (with at least one pool)
Using simulation and the BPA tool
Activities
© Copyright IBM Corporation 2020, 2021
Figure 4-15. BPMN model example (2 of 2)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-18
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Unit 4. Using simulation and the BPA tool
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4.3. Introduction to Blueworks Live (BWL)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-19
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Unit 4. Using simulation and the BPA tool
Uempty
Introduction
n to
o
Blueworkss Live
e
(BWL)
Figure 4-16. Introduction to Blueworks Live (BWL)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-20
V12.0
Unit 4. Using simulation and the BPA tool
Uempty
IBM Blueworks Live
• IBM Blueworks
Live is a Software
as a Service (SaaS)
process modeling
tool where
customers pay a
monthly fee to use
the website
• No installation is
necessary
• All that you need is
a paid subscription
and a web browser
pointed to:
blueworkslive.com
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-17. IBM Blueworks Live
• IBM Blueworks Live is a cloud-based software that provides a dedicated, collaborative
anywhere environment to build and improve business processes through process mapping. It
enables teams to work together through an intuitive and easily accessible web interface to
document and analyze processes to help make them more efficient.
• While IBM Blueworks Live supports all aspects of process modeling, it provides no simulation
capabilities. However, IBM Process Mining provides simulation capabilities useful to establish
return on investment associated with automation initiatives.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-21
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Unit 4. Using simulation and the BPA tool
Uempty
Exporting a Blueworks Live process (1 of 2)
• When exporting a
Blueworks Live
process, it must
not contain the
following
elements when
importing into IBM
Process Mining:
ƒ
Message events
ƒ
Subprocesses
ƒ
Multiple outgoing
sequence flows
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-18. Exporting a Blueworks Live process (1 of 2)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-22
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Unit 4. Using simulation and the BPA tool
Uempty
Exporting a Blueworks Live process (2 of 2)
• When exporting a Blueworks Live process to be used in IBM Process Mining, perform the
following steps:
1.
Use the standard process export
2.
Select Business Process Model and Notation (BPMN 2.0)
3.
Click Export.
• This creates a
compressed file that
contains the BPMN
representation of the
model
• Extract the .bpmn file
from the compressed file
• This file is imported into
IBM Process Mining
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-19. Exporting a Blueworks Live process (2 of 2)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-23
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Unit 4. Using simulation and the BPA tool
Uempty
4.4. Creating and running a simulation of a
BlueWorks Live process
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-24
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Unit 4. Using simulation and the BPA tool
Uempty
Creating
g and
d
running
g a simulation
n
off a Blueworkss Live
e
process
Figure 4-20. Creating and running a simulation of a BlueWorks Live process
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-25
V12.0
Unit 4. Using simulation and the BPA tool
Uempty
Importing a BPMN model into IBM Process Mining
To import a BPMN model into IBM
Process Mining:
1. Access the BPA module in the
Application menu
2. Click Create new and select
Process
3. Provide a name and select an
organization
4. Browse to the .bpmn file and click
Open
5. Click Create
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-21. Importing a BPMN model into IBM Process Mining
As stated earlier, you can manually draw the process model in standard BPMN 2.0 notation by
using the BPA model editor or you can import process models by using the BPMN 2.0 standard
from tools such as Blueworks Live.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-26
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Unit 4. Using simulation and the BPA tool
Uempty
Field mapping from BWL to IBM Process Mining (1 of 4)
• After the model is
imported, you can use
IBM Process Mining to
analyze the process.
• The swimlane titles are
mapped to roles.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-22. Field mapping from BWL to IBM Process Mining (1 of 4)
The titles from the swimlanes are mapped to roles in IBM Process Mining. These roles can be
analyzed by using the Activity Map or Social Net.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-27
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Unit 4. Using simulation and the BPA tool
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Field mapping from BWL to IBM Process Mining (2 of 4)
• When you create a new simulation,
IBM Process Mining maps the Cycle
Time from BWL to Working time.
• Because it does not
include wait time, you
need to set the Service
time manually.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-23. Field mapping from BWL to IBM Process Mining (2 of 4)
• The BPMN import maps the Cycle Time (60 minutes) to Working time (1 hour) but does not use
Wait time (20 minutes).
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-28
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Unit 4. Using simulation and the BPA tool
Uempty
Field mapping from BWL to IBM Process Mining (3 of 4)
• To make the simulation more
accurate, you need to set Service
Time in IBM Process Mining to the
sum of Work Time and Wait Time
(from IBM Blueworks Live).
• For example, the 80minutes total time in
BWL equals 1-hour
20-minutes Service
Time in IBM Process
Mining.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-24. Field mapping from BWL to IBM Process Mining (3 of 4)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-29
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Unit 4. Using simulation and the BPA tool
Uempty
Field mapping from BWL to IBM Process Mining (4 of 4)
• Activity cost values from
BWL are mapped to activity
costs in IBM Process Mining.
• These values are used by
default in the simulation for
calculations.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-25. Field mapping from BWL to IBM Process Mining (4 of 4)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-30
V12.0
Unit 4. Using simulation and the BPA tool
Uempty
Create a simulation scenario
To create a new simulation scenario:
1. Access the BPA module in the Application menu
2. Click Simulation
3. Provide a name
4. Click Confirm
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-26. Create a simulation scenario
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-31
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Unit 4. Using simulation and the BPA tool
Uempty
Running a simulation (1 of 2)
• By clicking Run
Simulation, IBM Process
Mining runs the
simulation according to
the parameters entered.
• The results of the
simulation are displayed
as a table.
• For each instance, a
variable number of
activity events (enough
events to complete a
process instance) is
completed.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-27. Running a simulation (1 of 2)
The project that is created from the simulated events can be used to gain business insights and to
discover automation opportunities for improvement of the process modeled in IBM Blueworks
Live.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-32
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Unit 4. Using simulation and the BPA tool
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Running a simulation (2 of 2)
• You can click Return to scenario to make updates to any
simulation parameters and rerun the simulation.
• To create a project from the simulation:
ƒ
Click Create project to create a project based on the simulation
configuration.
ƒ
Enter the name of the project and click Confirm.
• This opens the simulation project in IBM Process Mining tool in
the Model View.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-28. Running a simulation (2 of 2)
By clicking Create project, the simulated data source is used to create an IBM Process Mining
project and visualize the simulation as a real process. In other words, the simulated data source is
considered as a real data source.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-33
V12.0
Unit 4. Using simulation and the BPA tool
Uempty
Unit summary
• Explain how to navigate the BPA module
• Understand process, application, and organization landscapes
• Explain how to manually draw BPMN and DMN diagrams
• Define Business Process Model and Notation (BPMN)
• Describe how to use Blueworks Live (BWL) to export BPMN models
• Understand how the fields are mapped from BWL to IBM Process
Mining
• Understand how simulations can be used to evaluate automation
• Explain how to import BPMN models into IBM Process Mining
• Describe how to run simulations on BPMN models
• Describe how to perform a diff comparison between two simulation
scenarios
© Copyright IBM Corporation 2020, 2021
Figure 4-29. Unit summary
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-34
V12.0
Unit 4. Using simulation and the BPA tool
Uempty
Review questions
1. True or False: Processes that are defined in a process landscape can reference the actual
data-derived process model or they can be manually defined.
2. True or False: When you run a new version of a simulation, you can view the comparison on
the Diff tab in IBM Process Mining.
3. You can manually draw the following models in IBM Process Mining (select all that apply):
a. XML Process Definition Language
b. Business Process Model and Notation (BPMN)
c. Business Process Execution Language (BPEL)
d. Decision Model and Notation (DMN)
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-30. Review questions
Write your answers here:
1.
2.
3.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-35
V12.0
Unit 4. Using simulation and the BPA tool
Uempty
Review answers
1. True or False: Processes that are defined in a process landscape can reference the actual
data-derived process model or they can be manually defined.
The answer is True.
2. True or False: When you run a new version of a simulation, you can view the comparison on
the Diff tab in IBM Process Mining.
The answer is True.
3. You can manually draw the following models in IBM Process Mining (select all that apply):
a. XML Process Definition Language
b. Business Process Model and Notation (BPMN)
c. Business Process Execution Language (BPEL)
d. Decision Model and Notation (DMN)
The answer is b and d.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-31. Review answers
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-36
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Unit 4. Using simulation and the BPA tool
Uempty
Exercise 3: Simulating a Blueworks Live BPMN process
• Invenio Bank is heavily invested in
IBM Blueworks Live and wants to
understand how IBM Process
Mining can be used to simulate
Blueworks Live processes.
• As a Technical Analyst, you import
a Blueworks Live process into IBM
Process Mining and perform
multiple simulations on the
process.
• You evaluate the data that is
mapped from Blueworks Live into
IBM Process Mining and perform a
Diff comparison on the results of
multiple simulations.
Using simulation and the BPA tool
© Copyright IBM Corporation 2020, 2021
Figure 4-32. Exercise 3: Simulating a Blueworks Live BPMN process
Use Case Description
In this exercise, you take on the role of a Technical Analyst to import a Blueworks Live BPMN
model into IBM Process Mining. You use the BPA module to import a Blueworks Live process and
perform simulations on the BPMN model.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-37
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Unit 4. Using simulation and the BPA tool
Uempty
Exercise
objectives
• Import a Blueworks Live BPMN model into IBM Process Mining
• Configure and run a simulation
• Perform a Diff comparison of multiple simulations
© Copyright IBM Corporation 2020, 2021
Figure 4-33. Exercise objectives
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
4-38
V12.0
Unit 5. Course summary
Uempty
Unit 5. Course summary
Estimated time
00:15
Overview
This unit summarizes the course and provides information for future study.
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-1
V12.0
Unit 5. Course summary
Uempty
Unit objectives
• Describe the course objectives and what you learned
• Identify and describe product certifications that are related to this
course
• Identify resources that can help you learn more
© Copyright IBM Corporation 2021
Figure 5-1. Unit objectives
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-2
V12.0
Unit 5. Course summary
Uempty
Course
objectives
• Visualize a process and generate the event log
• Understand data quality and data quality issues
• Evaluate maverick buying patterns of a multi-level process
• View the frequency, duration, and cost models of a process
• Import a reference model and perform conformance checking
• Create custom filters and dashboards
• Perform a Diff comparison of two simulation scenarios
• Analyze a process for potential RPA candidates
• Import a BPMN model into IBM Process Mining
• Configure and run simulations on a Blueworks Live BPMN process
© Copyright IBM Corporation 2021
Figure 5-2. Course objectives
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-3
V12.0
Unit 5. Course summary
Uempty
IBM credentials: Badges and certifications
• Certify your skills with IBM digital credentials
ƒ
https://www.ibm.com/training/credentials
Get certified
Take an exam
Search badges
News
Search IBM certification
Search exams available for
Find IBM badges for skill
Catch up on the latest IBM
offerings across a broad
the IBM Professional
development activities and
credential news.
range of technology areas.
Certification program.
other achievements.
https://ibm.biz/BdqW6Z
https://ibm.biz/BdqW6Y
https://ibm.biz/BdqW62
Course summary
https://ibm.biz/BdqW6z
© Copyright IBM Corporation 2021
Figure 5-3. IBM credentials: Badges and certifications
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-4
V12.0
Unit 5. Course summary
Uempty
Learn more about this product
• What is Process Mining?
ƒ
Provides an overview of the features of IBM
Process Mining along with resources for further
education.
ƒ
https://www.ibm.com/cloud/cloud-pak-forbusiness-automation/process-mining
• IBM Process Mining Documentation
ƒ
Official IBM Documentation for IBM Process
Mining.
ƒ
https://www.ibm.com/docs/en/cloudpaks/1.0?topic=foundation-process-mining
Course summary
© Copyright IBM Corporation 2021, 2022
Figure 5-4. Learn more about this product
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-5
V12.0
Unit 5. Course summary
Uempty
Additional resources (1 of 5)
• IBM Cloud Education course information
ƒ
View and download course materials and
course corrections.
ƒ
http://ibm.biz/CourseInfo
• IBM Developer
ƒ
IBM's official developer program offers access
to software trials and downloads, how-to
information, and expert practitioners.
ƒ
https://developer.ibm.com/
© Copyright IBM Corporation 2021
Figure 5-5. Additional resources (1 of 5)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-6
V12.0
Unit 5. Course summary
Uempty
Additional resources (2 of 5)
• IBM Automation Community
ƒ
Learn about Blockchain, Blueworks Live, BPM,
Workflow, Case, Content Management,
Decision Management, Robotic Process
Automation, Platform, and Cloud Pak for
Automation
ƒ
https://community.ibm.com/community/user/
automation/home
• IBM Middleware User Community
ƒ
Learn about API Connect, App Connect, MQ,
DataPower, Aspera, Event Streams, and Cloud
Pak for Integration
ƒ
https://community.ibm.com/community/user/
middleware/communities/cloud-integrationhome
© Copyright IBM Corporation 2021
Figure 5-6. Additional resources (2 of 5)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-7
V12.0
Unit 5. Course summary
Uempty
Additional resources (3 of 5)
• IBM Training
ƒ
Search the IBM Training website for courses
and education information.
ƒ
https://www.ibm.com/training
• Learning Journeys
ƒ
Learning Journeys describe a recommended
collection of learning content to acquire skills
for a specific technology or role.
ƒ
https://www.ibm.com/training/journeys/#tabibm-cloud
© Copyright IBM Corporation 2021
Figure 5-7. Additional resources (3 of 5)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-8
V12.0
Unit 5. Course summary
Uempty
Additional resources (4 of 5)
• IBM Redbooks
ƒ
IBM Redbooks are developed and published by
the IBM International Technical Support
Organization (ITSO). Redbooks typically provide
positioning and value guidance, installation and
implementation experiences, typical solution
scenarios, and step-by-step "how-to" guidelines.
ƒ
http://www.redbooks.ibm.com/
• IBM Knowledge Center
ƒ
IBM Knowledge Center is the primary home for
IBM product documentation.
ƒ
https://www.ibm.com/support/knowledgecenter
© Copyright IBM Corporation 2021
Figure 5-8. Additional resources (4 of 5)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-9
V12.0
Unit 5. Course summary
Uempty
Additional resources (5 of 5)
• IBM Marketplace
ƒ
Learn about IBM offerings for Cloud, Cognitive,
Data and Analytics, Mobile, Security, IT
Infrastructure, and Enterprise and Business
Solutions.
ƒ
https://www.ibm.com/products
• IBM Training blog, Twitter, and Facebook
ƒ
Official IBM Training accounts provide
information about IBM course offerings,
industry information, conference events, and
other education-related topics.
ƒ
https://www.ibm.com/blogs/ibm-training
ƒ
https://twitter.com/IBM
ƒ
https://www.facebook.com/groups/IBMTrainin
gandSkills
© Copyright IBM Corporation 2021, 2022
Figure 5-9. Additional resources (5 of 5)
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-10
V12.0
Unit 5. Course summary
Uempty
Unit summary
• Describe the course objectives and what you learned
• Identify and describe product certifications that are related to this
course
• Identify resources that can help you learn more
© Copyright IBM Corporation 2021
Figure 5-10. Unit summary
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-11
V12.0
Unit 5. Course summary
Uempty
Course completion
You have completed this course:
Fundamentals of IBM Process Mining
Do you have any questions?
Course summary
© Copyright IBM Corporation 2021
Figure 5-11. Course completion
© Copyright IBM Corp. 2021, 2022
Course materials may not be reproduced in whole or in part without the prior written permission of IBM.
5-12
V12.0
backpg
© Copyright International Business Machines Corporation 2021, 2022.