IFN650 Business Process Analytics

Unit Outline: Semester 1 2026, Gardens Point, Internal

Overview

This unit introduces a number of process analysis techniques used during the design, execution, and post-execution stages of the Business Process Management (BPM) life cycle. BPM provides organisations with the ability to save money and time by systematically  documenting, managing, automating, and optimising their business processes. To unlock the true benefits of a process-aware organisation, it is essential that process modelling efforts do not purely remain paper-based but act as the prelude to automated support. The last decade has seen an increased uptake of process automation and workflow technology, which has increased the potential for real, evidence-based analysis associated with the execution of various business processes. By applying techniques such as process verification, process simulation, and process mining, we can gain  insights into both the current and future business operations of an organisation, which in turn can lead to continuous process improvement.

Learning Outcomes

On successful completion of this unit you will be able to:

1. Create executable business process models based on an analysis of a domain.
2. Verify the correctness and other properties of these executable models using BPM tools and techniques.
3. Evaluate the performance of multiple scenarios using process simulation tools.
4. Analyse existing processes by discovering process models automatically and assess the performance using process mining techniques.
5. Recommend and justify data-driven process optimisation opportunities.
6. Work efficiently in a group and individually.

Content

Learning Approaches

This unit offers a blended approach to teaching and learning, designed to maximise your opportunity to learn. The unit emphasises a 'hands-on' approach to learning through the illustration of new concepts through worked examples and demonstrations.

In this unit you will learn through engaging in the following four main learning activities: 

• Reading/watching prescribed materials in preparation for workshops
• Engaging in workshop and practical activities
• Relating workshop content and activities to assessment tasks
• Relating unit content to the wider context via group discussions

The unit is modular, consisting of four discrete blocks. Each block is delivered as lectures and workshops together with your own self-directed study, using provided materials such as videos, recorded lectures, online discussions, and readings.

Block 1: Introduction to Process Analytics - Process Verification

Block 2: Process Simulation and Capacity Planning - Tool-based learning: Design time analysis

Block 3: Process Mining (Discovery) - Process Mining (Conformance and Performance)

Block 4: Process Mining (Case Studies) - Process Mining (Research)

You can expect to spend between 10-15 hours per week on average involved in preparing for and attending all scheduled classes, completing assessment tasks, and undertaking your own independent study to consolidate your learning.

Feedback on Learning and Assessment

The assessments in this unit have been designed to provide you with a scaffolded learning experience. Written assessments will be submitted on the Canvas site at the set deadlines. Additional support for all assessments will be provided throughout the semester. 

Halfway through the semester (after block 2), there will be a written report to model and simulate executable business processes for a business scenario. For the remainder of the semester, the focus will be on applying various process analysis techniques to gain insights into the problem. The intention of this part of the unit is for you to comprehend and explore evidence-based process improvement opportunities using process mining techniques (blocks 3-4). 

Selected supporting resources will be provided on the unit’s Canvas site. A marking guide will be available for the assignments. A detailed marking of and detailed comments on your assignments will be provided. Marks will be allocated based on your participation and engagement with the teaching materials throughout the semester. Teaching staff is available during the workshops and consultation hours to clarify or elaborate on the assignment content and to provide constructive feedback.

Assessment

Overview

You will develop and analyse an executable process model using a real-world scenario. You will use appropriate software tools to model and analyse the behaviour and performance of business processes. You will also make use of various process mining techniques to develop evidence-based process improvement recommendations. The assessment tasks are designed to ensure that they reflect real-world BPM practices. We expect you to demonstrate competency in utilising different process analysis tools and techniques. Students should reflect on their learning outcomes and skills throughout the entire process.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Process Analytics (Design)

This assessment asks you to identify a business problem that could benefit from process analysis techniques, develop executable process models and utilise process simulation and process verification techniques.

You are required to submit a written report (pdf) to present your processes and analysis. 

The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Process mining

This assessment asks you to utilise different process mining techniques to analyse historical process data and propose process improvement ideas.

The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Weekly Reflection

Each week, you are required to write a reflection on your learning, work undertaken and questions that your learning has generated.

The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.

Academic Integrity

Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.

The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.

Resources

Resource Materials

Recommended text(s)

• W.M.P. van der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer, 2011. ISBN 978-3-642-19344-6.

Risk Assessment Statement

There are no unusual health or safety risks associated with this unit. You will be made aware of evacuation procedures and assembly areas in the first few lectures. In the event of a fire alarm sounding, or on a lecturer's instruction, you should leave the room and assemble in the designated area which will be indicated to you. You should be conscious of your health and safety at all times whilst on campus or in the field.

Course Learning Outcomes

IN20 Master of Information Technology

1. Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
   Relates to: ULO1, ULO4, Process Analytics (Design), Process mining, Weekly Reflection
2. Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
   Relates to: ULO1, ULO3, ULO4, ULO5, Process Analytics (Design), Process mining, Weekly Reflection
3. Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
   Relates to: ULO1, ULO2, ULO5, Process Analytics (Design), Process mining, Weekly Reflection
4. Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
   Relates to: ULO6, Process Analytics (Design), Process mining, Weekly Reflection