Outline

Overview

The amount of data generated within organisations is growing rapidly and the ability of supply chains to harness emerging opportunities and respond to issues of sustainability and resilience relies on the ability of managers to make effective decisions based on the information provided by careful analysis of data. Through this unit students develop a strong understanding of the basic techniques underpinning quantitative analysis for logistics and supply chain management and 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 and students are guided through the basic theories used in decision making. This unit emphasises how the theories are applied in practice, drawing on real world experience in quantitative analysis for logistics and supply chain management. The unit covers demand forecasting, spreadsheet modelling, optimisation of production and distribution using linear programming, simulation and quantitative performance management. The unit also introduces basic statistics and linear regression techniques.

Unit details and rules

Unit code	ITLS5050
Academic unit	Transport and Logistics Studies
Credit points	6
Prohibitions ?	TPTM6495 or ITLS5200 or ITLS6203 or MMGT6012
Prerequisites ?	None
Corequisites ?	ITLS5020 or ITLS5000 or TPTM5001
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Geoffrey Clifton, geoffrey.clifton@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Multiple choice, short answer and essay style questions.	40%	Formal exam period	2 hours
Outcomes assessed:
Supervised test ?	Computer exam Computer exam on campus testing material from first half of Semester.	30%	Week 07 Due date: 13 Apr 2024 at 13:20	2 hours
Outcomes assessed:
Assignment	Individual report The individual report topic will be available on Canvas	30%	Week 10 Due date: 02 May 2024 at 23:59 Closing date: 16 May 2024	1000 words
Outcomes assessed:

Assessment summary

Individual report: Write a report to a business manager on how quantitative analysis can address an issue in LSCM. The exact topic will be provided on Canvas. The report should include reference to academic and professional literature, evidence of your own data analysis, as well as tables and charts of your own creation. The word limit covers the content of the report (but not the Executive Summary, Table of contents, reference list and not the appendices) and the word limit must not be exceeded.

Computer exam: The computer exam will test your ability to apply the techniques covered in class and to interpret your results. The date of the computer exam will be confirmed closer to the time.

Final exam: The final exam will include multiple choice questions, short answer questions and a reflective essay style question covering the entire Semester.

Detailed information for each assessment will be available on Canvas.

Generative AI (such as Chat GPT) is not permitted in this unit.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late penalties apply for the Individual report as per the Assessment Procedures 2011.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to supply chain modelling	Workshop (3 hr)
Week 02	Spreadsheet modelling	Workshop (3 hr)
Week 03	Linear programming	Workshop (3 hr)
Week 04	Linear programming network models	Workshop (3 hr)
Week 05	Aggregate planning	Workshop (3 hr)
Week 06	Computer exam review	Workshop (3 hr)
Week 07	Data description and presentation	Workshop (3 hr)
Week 08	Correlation and simple regression	Workshop (3 hr)
Week 09	Forecasting with regression	Workshop (3 hr)
Week 10	Demand modelling	Workshop (3 hr)
Week 11	Demand modelling applications	Workshop (3 hr)
Week 12	Simulation modelling	Workshop (3 hr)
Week 13	Simulation modelling applications and course review	Workshop (3 hr)

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

Camm J. D., Cochran J. J., Fry M. J., Ohlmann J. W. (2024). Business Analytics, (5th ed), Cengage Learning US, Boston. ISBN: 9780357902202

https://au.cengage.com/c/business-analytics-5e-camm-cochran-fry-ohlmann/9780357902202/

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. Select the appropriate model to use in unfamiliar contexts and implement the core set of quantitative logistics and supply chain management models in an efficient manner
LO2. Clean, chart and present data and the outputs of quantitative logistics and supply chain analysis and interpret and discuss outputs, identifying limitations and creating recommendations
LO3. Explain analytic logistics and supply chain methods in your own words and how the techniques are implemented in practice and contribute to better management decision making
LO4. Recognize and address issues relating to the ethics and limitations of quantitative logistics and supply chain analysis

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.

As a result of feedback from students, extra material is being added to Canvas. Furthermore, an extra week covering correlation and regression modelling has been added to better prepare students for this material.

Additional information

Business School Policies

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

Support for students

Weekly schedule

Weekly schedule

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

ITLS5050: Introductory Supply Chain Analysis

Semester 1, 2024 [Normal evening] - 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

Support for students

Weekly schedule

Weekly schedule

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