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Unit of study_

ITLS5200: Quantitative Logistics and Transport

Semester 2, 2020 [Normal evening] - Camperdown/Darlington, Sydney

Supply chain management, as well as logistics, transport and infrastructure management, relies on the ability to make effective decisions based on the information provided by careful analysis of data. Students undertaking this unit will develop a strong understanding of the basic techniques underpinning quantitative analysis and will develop highly marketable skills in spreadsheet modelling and the communication and presentation of data to support management decision making. This unit emphasises the practical aspects of quantitative analysis with computer-based workshops. Students are guided through the basic theories used in decision making but emphasis is placed on how the theories are applied in practice, drawing on real-world experience in quantitative analysis. The unit covers demand forecasting, spreadsheet modelling, optimisation of production and transportation using linear programming, simulation and basic statistics and linear regression techniques.

Unit details and rules

Unit code ITLS5200
Academic unit Transport and Logistics Studies
Credit points 6
ITLS5000 or TPTM5001 or ITLS5100 or TPTM6241
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Geoffrey Clifton,
Type Description Weight Due Length
Online task Take-home computer exam
Computer exam covering spreadsheet modelling and linear programming.
30% -
Due date: 15 Oct 2020 at 20:00

Closing date: 15 Oct 2020
2 hours
Outcomes assessed: LO1 LO2
Final exam (Take-home extended release) Type E final exam Final exam
Take-home (long release).
40% Formal exam period 48 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Individual report
30% Week 11
Due date: 12 Nov 2020 at 16:00

Closing date: 26 Nov 2020
5 pages
Outcomes assessed: LO2 LO3 LO4
Type E final exam = Type E final exam ?

Assessment summary

  • Take-home computer exam: The computer exam will require you to use the skills and techniques covered in the unit up to that point to solve a series of problems using Excel, and then to interpret and communicate the results. You will write up your findings in an exam booklet.
  • Individual report: Assume you have been asked to prepare a report for the board of a company explaining the impact of an issue of long term importance on the industry of your choice and discussing how data analysis can help the company and industry address the issue. Detailed instructions will be provided on Canvas
  • Final exam: The final exam will test the understanding of all the material covered in the entire unit.

 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


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. 


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.


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.


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. 


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to modelling and quantitative analysis Lecture and tutorial (3 hr)  
Week 02 Spreadsheet modelling Lecture and tutorial (3 hr)  
Data presentation and descriptive statistics Lecture and tutorial (3 hr)  
Week 03 Linear programming 1 Lecture and tutorial (3 hr)  
Week 04 Linear programming 2 Lecture and tutorial (3 hr)  
Week 06 Statistical inference Lecture and tutorial (3 hr)  
Week 08 Relationships between data sets Lecture and tutorial (3 hr)  
Week 09 Forecasting with regression Lecture and tutorial (3 hr)  
Week 10 Forecasting with demand modelling 1 Lecture and tutorial (3 hr)  
Week 11 Forecasting with demand modelling 2 Lecture and tutorial (3 hr)  
Week 12 Simulation modelling Lecture and tutorial (3 hr)  

Attendance and class requirements

Lecture recording: 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

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

  • Camm J. D., Cochran J. J., Fry M. J., Ohlmann J. W., Anderson D. R., Sweeney D. J. and William T. A. (2017). Essentials of Business Analytics, (2nd ed), Cengage Learning, Boston. ISBN-13: 978-1-305-62773-4.

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.

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

  • LO1. select the appropriate model to use in unfamiliar contexts; demonstrate an ability to implement a core set of quantitative analysis techniques in an efficient manner; and demonstrate a familiarity with a broader range of techniques
  • LO2. clean, chart and present data and the outputs of quantitative analysis and interpret and discuss outputs, identifying limitations and creating recommendations
  • LO3. explain methods in your own words and demonstrate an understanding of how the techniques are implemented in practice in the logistics, supply chain management, transport or infrastructure industries and in the broader context of decision making for business
  • LO4. recognise and address issues relating to the ethics of quantitative analysis and data presentation.

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

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

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


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