37345 Quantitative Management Practice
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Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 37242 Introduction to Optimisation
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 35340 Quantitative Management Practice
Description
This subject is concerned with practical aspects of quantitative management and covers recent developments in various areas of application. Applications considered include personnel scheduling, supply chain management (including logistics and inventory control), production planning and control, transportation problems, and financial applications. The subject involves various case studies and study of recent journal publications.
The subject focuses on the development of some of the skills required in professional practice, and the application of skills and knowledge acquired in other quantitative management subjects.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Implement advanced techniques to solve a range of quantitative management problems using industry standard software. |
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| 2. | Assess the mathematical aspects of an industrial application and the appropriateness of different mathematical/statistical approaches to its solution. |
| 3. | Develop high quality written and oral presentations, with depth and comprehensiveness appropriate to a range of stakeholders involved in industry-based management. |
| 4. | Identify different approaches to solving a range of complex quantitative management problems within their industrial contexts. |
| 5. | Understand and apply knowledge needed to solve a complex quantitative management problem, and critically review approaches that other people have taken to solving the problem. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Demonstrate theoretical and technical knowledge of mathematical sciences including calculus, discrete mathematics, linear algebra, probability, statistics and quantitative management. (1.1)
- Evaluate mathematical and statistical approaches to problem solving, analysis, application, and critical thinking to make mathematical arguments, and conduct experiments based on analytical, numerical, statistical, algorithms to solve new problems. (2.1)
- Work autonomously or in teams to demonstrate professional and responsible analysis of real-life problems that require application of mathematics and statistics. (3.1)
- Use succinct and accurate presentation of reasoning and conclusions to communicate mathematical solutions, and their implications, to a variety of audiences, using a variety of approaches. (5.1)
Contribution to the development of graduate attributes
This subject also contributes specifically to the development of the following course intended learning outcomes:
Graduate Attribute 1.0: Disciplinary Knowledge
In this subject, you will develop the ability to explain the principles and concepts of a broad range of fundamental areas, in addition to more specialised knowledge in your potential career pathways. You will learn to apply this knowledge to problems relevant to industry and public policy development through all the assessment tasks.Graduate Attribute 2.0: Research, inquiry and critical thinking
Throughout the semester, you will be given extensive opportunities in the lectures, computer labs and assessment tasks 1 and 3 to Identify, design and implement multiple approaches to model a real world problem and to compare the solutions from those approaches.Graduate Attribute 3.0: Professional, ethical, and social responsibility
The ability to work effectively and efficiently in a team is a key professional skills. This subject involves multiple tasks where team work is required. You will also have the opportunity to develop your skills in using specialist mathematical/statistical/QM Software to implement mathematical approaches to solve problems relevant to industry and public policy development.Graduate Attribute 5.0: Communication
In this subject, you will prepare report and case studies which involve communicating information, problem-solving and conclusions in a variety of modes (both written and oral forms), to diverse audiences (expert and non-expert).Teaching and learning strategies
This subject will be taught using a combination of lectures, tutorials and computer laboratories. Lectures will present and elaborate on examples, terminology, methods, modes of presentation, implementation, and analysis. Students must read online lecture materials and textbook chapters as preparation before attending the class. This subject uses active learning strategies where students have the opportunity to use their preparation before the classroom via group work and discussions to solve more complex problems. Tutorials will provide opportunities for the students to apply the concepts and methods presented in lectures, including opportunities for collaborative
learning to develop communication and group working practice. Students are expected to attempt the tutorial questions provided before attending the corresponding computer lab sessions. In the lab sessions, verbal and written feedback on their progress is provided to the students by the lecturer/tutors from week 1 across the entire semester. During the computer lab sessions, software packages will be demonstrated, and it is expected that students will use these extensively for solving problems and in assignments. Students will collaborate in the lab sessions in small groups around some key focus questions and interact with the whole class and the tutor to receive in-class verbal feedback from tutors and peers. Extensive use of Canvas will be made as a medium for communication and to host learning resources.
Content (topics)
- Professional development through real-life applications and use of spreadsheet modelling;
- Research methodology and practice.
Assessment
Assessment task 1: Case Study I - Deterministic Optimisation
| Intent: | This assessment task contributes to the development of the following graduate attributes: Graduate Attribute 1.0: Disciplinary Knowledge Graduate Attribute 2.0: Research, inquiry and critical thinking Graduate Attribute 5.0: Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 5.1 |
| Type: | Case study |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | 3 weeks. The requirements of the task are detailed in the assessment task sheet |
| Criteria: | Assessment rubric will be uploaded inside Canvas. |
Assessment task 2: Case Study II - Decision Tree Analysis
| Intent: | This assessment task contributes to the development of the following graduate attributes: Graduate Attribute 1.0: Disciplinary Knowledge Graduate Attribute 2.0: Research, inquiry and critical thinking Graduate Attribute 5.0: Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 3 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 5.1 |
| Type: | Case study |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | 3 weeks. The requirements of the task are detailed in the assessment task sheet |
| Criteria: | Assessment rubric will be uploaded inside Canvas. |
Assessment task 3: Research Report - Simulation and Optimisation under Uncertainty
| Intent: | This assessment task contributes to the development of the following graduate attributes: Graduate Attribute 1.0: Disciplinary Knowledge Graduate Attribute 2.0: Research, inquiry and critical thinking Graduate Attribute 3.0: Professional, ethical, and social responsibility Graduate Attribute 5.0: Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1, 3.1 and 5.1 |
| Type: | Project |
| Groupwork: | Group, group and individually assessed |
| Weight: | 40% |
| Length: | 3 weeks. The requirements of the task are detailed in the assessment task sheet |
| Criteria: | Group/Individual 30/10. Assessment rubric will be uploaded inside Canvas. |
Minimum requirements
Class attendance and participation are important parts of the learning experience in this subject. Students are strongly encouraged to attend and participate in learning activities including lectures, tutorials, computer laboratories, workshops and seminars. Failure to attend these activities may disadvantage the student. No special accommodation can be made for students who knowingly enrol in the subject yet are unable or elect not to attend class activities.
Required texts
Practical Management Science, W L Winston and S C Albright, Cengage Learning, 5th edition, 2012
(The 4th edition is also acceptable.)
References
Liberatore M J and R L Nydick, Decision Technology – Modeling, Software and Applications, 2003.
Winston W L, Operations Research-Applications and Algorithms, Cengage Learning, 4th edition.