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Unit outline_

ITLS5050: Introductory Supply Chain Analysis

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

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

Academic unit Transport and Logistics Studies
Credit points 6
Prerequisites
? 
None
Corequisites
? 
ITLS5020 or ITLS5000 or TPTM5001
Prohibitions
? 
TPTM6495 or ITLS5200 or ITLS6203
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
Final exam (Take-home extended release) Type E final exam Final exam
Written exam
40% Formal exam period 48 hours
Outcomes assessed: LO3 LO4
In-semester test (Record+) Type B in-semester exam Computer exam
Computer exam covering spreadsheet modelling and linear programming
30% Week 07
Due date: 20 Apr 2021 at 20:00
2 hours
Outcomes assessed: LO1 LO2 LO3
Assignment Individual report
The individual report topic will be available on Canvas
30% Week 12
Due date: 25 May 2021 at 16:00

Closing date: 04 Jun 2021
4 pages
Outcomes assessed: LO2 LO3 LO4
Type B in-semester exam = Type B in-semester exam ?
Type E final exam = Type E final exam ?

Assessment summary

Indvidual report:

Computer exam:

Final exam:

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 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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 supply chain modelling Workshop (3 hr) LO1 LO2 LO4
Week 02 Data description and presentation Workshop (3 hr) LO2 LO4
Week 03 Spreadsheet modelling Workshop (3 hr) LO1 LO2 LO3
Week 04 Linear programming Workshop (3 hr) LO1
Week 05 Linear programming applications Workshop (3 hr) LO1 LO2 LO3 LO4
Week 06 Aggregate planning and mid semester exam review Workshop (3 hr) LO1 LO2 LO3 LO4
Week 08 Relationships between data sets Workshop (3 hr) LO1 LO2
Week 09 Forecasting with regression Workshop (3 hr) LO1 LO2 LO3
Week 10 Demand modelling Workshop (3 hr) LO1
Week 11 Demand modelling applications Workshop (3 hr) LO1 LO2 LO3 LO4
Week 12 Simulation modelling Workshop (3 hr) LO1 LO2 LO3
Week 13 Simulation modelling applications and course review Workshop (3 hr) LO1 LO2 LO3 LO4

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. (2021). Business Analytics, (4th ed), Cengage Learning US, Boston. ISBN: 9780357131787

https://cengage.com.au/product/title/business-analytics/isbn/9780357131787 

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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Improvements to feedback and changes to the topic mix are being implemented this year.

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.