Business Process Mining

Overview

Data abounds in modern organisations, but data without meaning is useless.

Get the keys to your business data and start making strategic, data-driven business decisions with this practical workshop on Business Process Mining.

This course illustrates the main techniques and tools for process mining, what analytics can be extracted, and how these can be visualised and interpreted to gain better insights on how an organisation actually works. Armed with this knowledge, you will be able to run your first process mining project and gain the required buy-in to put up a case for process improvement based on your process mining results.

Who will benefit from this course?

This course addresses the needs of professionals involved in organisational improvement and operational excellence projects, such as:

Business and process analysts
IT staff such as solution architects
Those who have an analytical mindset to understand, interpret and gain insights from data analytics.

The course will cover:

Introduction to BPM
Introduction to process mining and its role in the BPM Lifecycle
Process mining value proposition
Case studies
Process mining techniques:
- Automated discovery
- Performance mining
- Conformance checking
- Variants analysis
- Predictive monitoring
How to set up a process mining project
Process mining tools

Course duration and delivery

This course can be delivered over two (2) or three (3) days and schedule can be negotiated as required (ie. over multiple weeks).

Both online and in-person course delivery is available.

Fees

$1,900 AUD (GST inclusive)

The course takes approximately 15 participants.

Course enquiries

For further information or to enquire about a corporate course workshop, please click below.

Sample agenda

Course delivered in 15 hours across ten (10) days.

DAY 1	Module 1 - Introduction to Business Process Management (BPM)	BPM principles, main concepts and value proposition Overview of the BPM lifecycle Shortfalls of traditional BPM
DAY 2	Module 2 - Introduction to Process Mining	Process mining value proposition Overview of process mining capabilities Process mining uptake and market analysis Case study (finance)
DAY 3	Module 3 – Event Logs and Process Maps	Anatomy of an event log Ingredients of a process map Abstraction and filtering Visual analytics and results interpretation Practical exercises
DAY 4	Module 4 – Automated Process Discovery (Part 1)	From process maps to BPMN models Quality of automated discovery Intermezzo: core BPMN language Visual analytics and results interpretation Practical exercises
DAY 5	Module 5 – Automated Process Discovery (Part 2) and Performance Mining (Part 1)	Alternative discovery views (social networks, object lifecycle models) Automated process discovery analysis template Practical exercises Process performance metrics (time, cost, quality and flexibility) Overview of main performance mining techniques
DAY 6	Module 6 – Guest Lecture: Case Studies	Case studies from manufacturing, finance and tertiary education Settings, data collection, analysis, results ROI estimation Organisational challenges and lessons learnt
DAY 7	Module 7 – Performance Mining (Part 2) and Conformance Checking	Visual analytics and results interpretation Performance mining analysis template Practical exercises Rule-based conformance checking: compliance checking Visual analytics and results interpretation Compliance checking analysis template Practical exercises Model-based conformance checking
DAY 8	Module 8 – Variant Analysis	Drivers for variant analysis and overview of techniques Visual analytics and results interpretation Variant analysis template Practical exercises
DAY 9	Module 9 – How to Run a Process Mining Project	Types of process mining project Phases of a question-driven project approach and role of stakeholders Data acquisition and preparation Use-cases analysis templates ROI estimation Common pitfalls of process mining projects
DAY 10	Module 10 – Course Recap and Guest Lecture: The Future of Process Mining	Predictive and prescriptive process monitoring Data-driven simulation Robotic process mining Causal process mining