Overview
In today's dynamic business landscape, organisations strive for operational excellence and data-driven decision-making. Operations management and business analytics offer a comprehensive exploration of the synergies between efficient operations and the strategic use of data analytics. The unit explores the contemporary principles and practices involved in the management of both service and manufacturing operations. Taking a strategic approach to operations, you will examine fundamental concepts including process planning, design, and control; quality management, Lean thinking, and Six Sigma continuous improvement; the role of the supply chain in modern operations; capacity planning, facility location, and project-based activities within operational environments. Particular attention is given to the strategic implications of operational design choices in the context of uncertainty and resilience. You will utilise business data from a variety of organisational sources including financial, economic and market information and develop mathematical and analytical models that support effective operational decision making. The unit also provides opportunities to apply contemporary analytics and visualisation tools to interpret operational performance and communicate insights. In addition, you will engage in independent, evidence-based research to analyse operational challenges and propose viable, data-informed solutions that enhance organisational effectiveness.
Details
Pre-requisites or Co-requisites
There are no requisites for this unit.
Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).
Offerings For Term 2 - 2026
Attendance Requirements
All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).
Recommended Student Time Commitment
Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.
Class Timetable
Assessment Overview
Assessment Grading
This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of 'pass' in order to pass the unit. If any 'pass/fail' tasks are shown in the table above they must also be completed successfully ('pass' grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the 'assessment task' section (note that in some instances, the minimum mark for a task may be greater than 50%). Consult the University's Grades and Results Policy for more details of interim results and final grades.
All University policies are available on the CQUniversity Policy site.
You may wish to view these policies:
- Grades and Results Policy
- Assessment Policy and Procedure (Higher Education Coursework)
- Review of Grade Procedure
- Student Academic Integrity Policy and Procedure
- Monitoring Academic Progress (MAP) Policy and Procedure - Domestic Students
- Monitoring Academic Progress (MAP) Policy and Procedure - International Students
- Student Refund and Credit Balance Policy and Procedure
- Student Feedback - Compliments and Complaints Policy and Procedure
- Information and Communications Technology Acceptable Use Policy and Procedure
This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.
Feedback, Recommendations and Responses
Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.
Feedback from SUTE
This is just a comment. I was pleased that the assessment was an assignment and not an exam. I noticed that there was varied Excel competence amongst the student group in this subject, and I certainly am no expert. By having the analytics as an assignment, I could take my time to work it out and practice the required tasks without feeling under pressure. So, I would say keep the assessment as an assignment. The lecturer was terrific! Professional & very supportive - I really enjoyed this subject.
Continue providing staggered assessment support and materials that build confidence in analytical (including Excel) skills relevant to the assessment.
Feedback from SUTE
Please provide more examples and details for the spreadsheets and case analysis. The A2 is useful but a bit difficult.
Develop more templated example case analysis spreadsheet activities for in-class use.
- Develop an advanced and integrated understanding of operations managements and business analytics
- Critically analyse and reflect on key principles of operations analytics
- Critically apply a complex systems approach to analytically identify, analyse and investigate the management of operational functions
- Synthesise complex data from a variety of sources and develop mathematical and analytical models as part of the analytical process to identify, interpret, and communicate solutions to complex workplace business problems.
- Interpret and successfully apply knowledge related to recent development of operations management in service and manufacturing industries.
Alignment of Assessment Tasks to Learning Outcomes
| Assessment Tasks | Learning Outcomes | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1 - Critical Review - 40% | |||||
| 2 - Report - 60% | |||||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1 - Knowledge | |||||
| 2 - Communication | |||||
| 3 - Cognitive, technical and creative skills | |||||
| 4 - Research | |||||
| 5 - Self-management | |||||
| 6 - Ethical and Professional Responsibility | |||||
| 7 - Leadership | |||||
| 8 - First Nations Knowledges | |||||
| 9 - Aboriginal and Torres Strait Islander Cultures | |||||
Textbooks
There are no required textbooks.
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- computer lab
- Excel 2016 with Add-in Solver
- Microsoft Power BI
All submissions for this unit must use the referencing style: American Psychological Association 7th Edition (APA 7th edition)
For further information, see the Assessment Tasks.
s.danvers@cqu.edu.au
Week 1
Begin Date: 13 Jul 2026Module/Topic
Introduction to Operations Management
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 2
Begin Date: 20 Jul 2026Module/Topic
Process Planning and Design
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 3
Begin Date: 27 Jul 2026Module/Topic
Managing Supply Chains
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 4
Begin Date: 03 Aug 2026Module/Topic
Forecasting, Capacity Planning and Facility Location
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 5
Begin Date: 10 Aug 2026Module/Topic
Process Monitoring and Control
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 6
Begin Date: 17 Aug 2026Module/Topic
Lean Systems, Six Sigma and Continuous Improvement
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Vacation Week
Begin Date: 24 Aug 2026Module/Topic
Chapter
Events and Submissions/Topic
Week 7
Begin Date: 31 Aug 2026Module/Topic
Introduction to Business Analytics
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 8
Begin Date: 07 Sep 2026Module/Topic
Optimisation Modelling and Sensitivity Analysis
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 9
Begin Date: 14 Sep 2026Module/Topic
Network Models
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 10
Begin Date: 21 Sep 2026Module/Topic
Operational Data Analysis
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 11
Begin Date: 28 Sep 2026Module/Topic
Visual Analytics and Dashboard Design
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 12
Begin Date: 05 Oct 2026Module/Topic
Operational Insight and Decision Support
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Exam Week
Begin Date: 12 Oct 2026Module/Topic
Chapter
Events and Submissions/Topic
Vacation/Exam Week
Begin Date: 19 Oct 2026Module/Topic
Chapter
Events and Submissions/Topic
1 Critical Review
This is an individual assessment. In this assessment, the students are required to perform a critical analysis of the literature on the intersections of digital technologies and supply chain resilience and devise a model or framework for resilient supply chain operations. The literature should not be more than three years old and should be sourced from the recommended list of academic journals in the field of operations and supply chain management (the list of journals has been provided in Moodle).
Students are advised to submit their 2500-word (+/- 10%) critical review through Turnitin in Moodle, with a cover sheet showing the unit name and number, assessment number, student name, and ID. Assessment details and guidelines will be provided on the unit website.
This assessment requires students to adhere to the guidelines on the use of artificial intelligence tools as specified in the Artificial Intelligence Assessment Scale (AIAS) Level-1. Any misuse or lack of disclosure regarding the use of AI tools will be considered a breach of academic integrity. AIAS Level-1 states: The assessment is completed entirely without AI assistance in a controlled environment, ensuring that students rely solely on their existing knowledge, understanding, and skills. You must not use AI at any point during the assessment. You must demonstrate your core skills and knowledge.
Week 7 Friday (4 Sept 2026) 11:55 pm AEST
A late penality @ 5% per day applies
Week 9 Friday (18 Sept 2026)
See your marks in Moodle and grade book
The assessment will be marked based on the following criteria:
Developed a succinct introduction of the review: 10%
Performed descriptive statistics: 20%
Critically discuss how different technologies support three main elements (readiness, response, and recovery) of supply chain resilience: 25%
Devised a robust framework linking digital technologies and supply chain resilience: 20%
Evaluated and articulated the viability and limitations of implementing a resilient supply chain framework for the organisation: 10%
Conclusion and recommendations: 10%
Cited at least 20 articles following APA style: 5%
- Develop an advanced and integrated understanding of operations managements and business analytics
- Critically analyse and reflect on key principles of operations analytics
- Critically apply a complex systems approach to analytically identify, analyse and investigate the management of operational functions
- Synthesise complex data from a variety of sources and develop mathematical and analytical models as part of the analytical process to identify, interpret, and communicate solutions to complex workplace business problems.
- Interpret and successfully apply knowledge related to recent development of operations management in service and manufacturing industries.
2 Report
This is an individual assessment. The assessment is designed for students to apply operations management principles and business analytics. The assessment consists of two integrated parts, requiring students to demonstrate both formal analytical modelling and data-driven decision support.
Part A: Optimisation Modelling - In Part A of the assessment, students are required to develop and apply optimisation models to analyse an operations management problem. Students must identify relevant decision variables, constraints, and objective functions, and use spreadsheet-based optimisation techniques (Excel and Solver) to evaluate alternative scenarios and derive optimal solutions for operational efficiency and productivity. As part of the business report Part A, students are expected to interpret and discuss their quantitative results by providing a written discussion in a business report format, accompanied by screenshots of the Excel-based developed mathematical models.
Part B: Power BI Dashboard for Operational Decision Support - In Part B of the assessment, students are required to design and document an interactive Power BI dashboard that supports operational analysis and decision-making using an operations-related dataset. The dashboard should integrate appropriate key performance indicators, diagnostic visualisations, and interactive features (such as filtering or drill-down) to communicate visual insights relevant to operations management. As part of the business report Part B, students are expected to interpret and discuss their insights by providing a written discussion in a business report format, accompanied by illustrative screenshots of the actual Power BI dashboard. Students must include a shared link to their published Power BI dashboard.
Students need to submit a 2500-word (+/- 10%) Business Report, incorporating both Part A (Optimisation Modelling) and Part B (Power BI Dashboard) in Word format, using 1.5 line spacing and Times New Roman, 12-point font. All assessment materials must be submitted through Turnitin and Moodle, with a cover sheet showing the unit name and number, assessment number, student name, and student ID. Further details and submission guidelines will be provided on the unit website.
This assessment requires students to adhere to the guidelines on the use of artificial intelligence tools as specified in the Artificial Intelligence Assessment Scale (AIAS) Level-1. Any misuse or lack of disclosure regarding the use of AI tools will be considered a breach of academic integrity. AIAS Level-1 states: The assessment is completed entirely without AI assistance in a controlled environment, ensuring that students rely solely on their existing knowledge, understanding, and skills. You must not use AI at any point during the assessment. You must demonstrate your core skills and knowledge.
Week 12 Friday (9 Oct 2026) 11:55 pm AEST
A late penality @ 5% per day applies
The result will be released after grade certification
The assessment will be marked following these criteria:
Developed a concise executive summary and introduction for the report: 10%
Developed and evaluated an accurate mathematical model, proposing appropriate solutions to the problems/questions: 20%
Effectively interpreted the outcomes of the model's analytics: 15%
Developed an effective interactive Power BI dashboard that integrates appropriate key performance indicators, diagnostic visualisations, and interactive analytical features to support operational decision-making: 20%
Demonstrated a thorough analysis and interpretation of operational insights derived from the Power BI dashboard, clearly communicating the implications of the visual analytics for operational performance and improvement: 20%
Integrated a minimum of 12 academic journal articles, adhering to APA reference format: 10%
Ensured clarity of written expression, grammar, and spelling: 5%
- Develop an advanced and integrated understanding of operations managements and business analytics
- Critically analyse and reflect on key principles of operations analytics
- Critically apply a complex systems approach to analytically identify, analyse and investigate the management of operational functions
- Synthesise complex data from a variety of sources and develop mathematical and analytical models as part of the analytical process to identify, interpret, and communicate solutions to complex workplace business problems.
- Interpret and successfully apply knowledge related to recent development of operations management in service and manufacturing industries.
As a CQUniversity student you are expected to act honestly in all aspects of your academic work.
Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.
When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.
Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.
As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.
What is a breach of academic integrity?
A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.
Why is academic integrity important?
A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.
Where can I get assistance?
For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.
What can you do to act with integrity?
