Outline

Overview

This unit gives guidelines for the formulation of management science models to provide practical assistance for managerial decision making. Optimisation methods are developed, and the complexity and limitations of different types of optimisation model are discussed so that they can be accounted for in model selection and in the interpretation of results. Linear programming methods are developed and extended to cover variations in the management context to logistics, networks, and strategic planning. Other topics may include decision analysis, stochastic modelling and game theory. The unit covers a variety of case studies incorporating the decision problems faced by managers in business.

Unit details and rules

Unit code	QBUS3310
Academic unit	Business Analytics
Credit points	6
Prohibitions ?	ECMT3610 or ECMT3710
Prerequisites ?	QBUS2310
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Daniel Oron, daniel.oron@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Written exam	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Mathematical questions	10%	Week 05 Due date: 02 Apr 2021 at 17:00 Closing date: 09 Apr 2021	n/a
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam Written exam	30%	Week 07 Due date: 20 Apr 2021 at 11:00	2 hours
Outcomes assessed:
Assignment	Assignment 2 Mathematical questions	10%	Week 13 Due date: 04 Jun 2021 at 17:00 Closing date: 11 Jun 2021	n/a
Outcomes assessed:
= Type B final exam ? = Type B in-semester exam ?

Assessment summary

Assignment 1: This assignment will focus on inventory management and dynamic programming models. Students will be required to demonstrate sufficient understanding of the theoretical principles in this unit, including model selection and design, application, and the ability to draw meaningful inferences based on the model output. The students will be challenged with a set of problems relating to business contexts and will be required to choose the appropriate optimisation model, state the assumptions of the model, use the methods covered in class to solve the model and provide insight on how to implement their solution.
Assignment 2: This assignment will focus on stochastic processes and game theory models. Students will be required to demonstrate a good understanding of optimisation and decision-making models covered in this unit. They should demonstrate the ability to identify real-life business situation which can benefit from the models covered in the unit. They will be expected to formulate the business problems mathematically and choose the appropriate models and tools to evaluate the different alternatives. Furthermore, they will be required to state the model assumptions, their analytic solution and how their results can be helpful in real-life decision-making contexts.
Mid-semester exam: This exam will examine concepts covered in weeks 1-6 of this unit. The questions will measure students’ knowledge of the major principles in management science and their ability to provide a complete and comprehensible description of their essential characteristics, as well as their ability to complete standard analytical tasks.
Final exam: This exam will assess all aspects of this unit from weeks 1-13. The exam will be marked for correctness, understanding of underlying material and clarity of exposition and presentation.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	EOQ – continuous production model, shortage costs, case study.	Lecture (3 hr)
Week 02	Introduction to EOQ – Economic Order Quantity models. EOQ models with price breaks and power of 2 policies	Lecture (3 hr)
Week 03	Dynamic Programming – introduction, network problems, resource allocation problems. special objective functions.	Lecture (3 hr)
Week 04	DP – inventory problems, equipment replacement applications.	Lecture (3 hr)
Week 05	DP – equipment replacement applications, knapsack problems: greedy algorithms vs. DP.	Lecture (3 hr)
Week 06	Introduction to Markov Chains, steady state probabilities.	Lecture (3 hr)
Week 08	MC- Applications in Marketing, Finance and Biology. Ergodic chains.	Lecture (3 hr)
Week 09	MC – Absorbing chains, mean passage times and probabilities.	Lecture (3 hr)
Week 10	Introduction to Game Theory. Two person zero sum games.	Lecture (3 hr)
Week 11	Randomized strategies, Domination, Graphical solutions, LP and zero sum games.	Lecture (3 hr)
Week 12	Two person Non constant-sum games, Introduction to n-person games, the Shapely Value.	Lecture (3 hr)
Week 13	Revision	Lecture (3 hr)

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Winston, Wayne, Operations Research: Applications and Algorithms, Duxbury, 1994.

All readings for this unit can be accessed on the Library eReserve link available on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. demonstrate a clear understanding of the different mathematical and statistical models and methods that support decision making for management
LO2. apply creative thinking to distil the main stylised facts and relationships from complex interactions between cost variables
LO3. recognise different cost components and operational constraints in modelling business problems
LO4. critically appraise the suitability of different analytic methods for a specific purpose
LO5. apply business analytic methods in practice, to real-life problems and adapt the models to specific business contexts and questions arising in business
LO6. communicate analytical findings using adequate optimisation methods and appropriate technical, as well as layman’s, language.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Additional information

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS3310: Advanced Management Science

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect