210704 Big Data, AI and Cloud Computing in Supply Chain Management

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Subject handbook information prior to 2025 is available in the Archives.

UTS: Business
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Description

In modern business's big data era, within organisations information is created and transferred within and across organisations. Appropriately skilled staff are critical to optimising the value of the data via the insights and decision-making capability it brings. In this subject, students learn about specific emerging technologies (big data, data analytics, AI and cloud computing) and how the underlying concepts of these are intrinsically intertwined to influence the design, measurement, control and monitoring of supply chains. This subject gives students an overview of the Internet of Things 4.0 methods and how they can be applied in business applications to build digital supply chains that are more efficient, transparent and effective. Additionally, it provides students the fundamental knowledge of ethical implications of data capture, usage, storage and privacy across different contexts.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1.	Analyse Big Data, AI and cloud computing tools, techniques and concepts for supply chain management
2.	Apply Big Data, AI and cloud computing advanced knowledge, tools and techniques in business operations to build digital supply chains
3.	Demonstrate knowledge of ethical implications of possession and guarding of Big Data, including data security and privacy
4.	Deploy AI, Big Data and cloud knowledge and skills in digital supply chains via a real case-study

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the following program learning objectives:

Employ interpersonal and stakeholder management skills and communication strategies to effectively engage and influence diverse stakeholders at multiple levels (2.1)

Contribution to the development of graduate attributes

An iconic trend in the digital transformation era is the increasingly extensive use of data analytics and machine learning tools in decision-making as strategic and operational levers. Our society is experiencing a rapid digital transformation, changing the way how different players in supply chains and technological systems interact with each other and exert their influences. For example, the way businesses and customers interact has changed in the digital economy. Businesses now routinely collect large volumes of fine-grained data to analyse consumers’ behaviour and track changes in firms’ strategies to make informed purchasing decisions. In this subject, students will understand and deploy emerging technologies of Big Data, AI and computing technologies and fully integrate legacy supply chain management technologies such as ERP, demand planning, etc.

Further, the instrumentation of the supply chain is done with sensors and monitors – referred as the IoT 4.0 – a technology typically used to monitor manufacturing, supply chain and/or logistics processes. This end-to-end integration of processes through the deployment of technologies allows truly transforming and reinventing existing supply chains into fully integrated digital supply chains as they mine data from existing supply chains. The use of rich and large data sets mined critical societal concerns for data security and privacy as well as ethical implications of possessing and guarding Big data, including data security and privacy. Through this subject, students can gain
advanced knowledge on selecting and deploying appropriate operations methods, tools, techniques and algorithms in different contexts – including Indigenous contexts – to build supply chain resilience for sustainable and inclusive growth.

This subject contributes to the development of the following graduate attributes:

Communication and collaboration
Social responsibility and cultural awareness
Professional and technical competence

Teaching and learning strategies

This subject is delivered online using a range of resources, self-directed study and live interactive sessions with the academic. Learners engage in the essential content through a variety of formats (lecture slides, notes, webinars, videos, articles, real case studies) online and learner-led dialogue through online discussions and posts, and interactions via Canvas. The teaching and learning strategies have been designed to enable learners to make progress in their achievement and maximise their accomplishment of the learning outcomes. Various teaching and learning strategies adopted are as follows:

Learners will be expected to read all the assigned readings and media articles, research and identify a trajectory of the evolution of legacy supply chains underpinned by traditional technologies leading to fully integrated digital supply chains.
By conducting a self-paced study, learners will contribute to the discussions on the influence of adopting new technologies on firm performance. This will enhance learners’ ability to progress successfully throughout the subject and complete all assessment items effectively.
The online collaborative sessions will provide opportunities for group activities and discussion, self-assessment, peer review and formative feedback from the subject facilitator. Online collaborative sessions with the facilitator will be conducted at a set time.
Formative and summative feedback will be provided to all learners and will take several forms, including quizzes during the independent learning activities and online collaborative sessions to support and enhance learner performance outcomes via assessments.

Content (topics)

Awareness of existing legacy supply chain technologies and introduction to emerging big data, AI and cloud computing techniques
Supply chain decision-making using big data, AI and cloud computing techniques in different cultural contexts
Barriers and strategies for big data, AI and cloud computing adoption in supply chain across different cultural contexts
Adoption of big data, AI and cloud computing and supply chain performance, including within Indigenous contexts
Supply chain integration using big data, AI and cloud computing techniques
Algorithm based business operations, big data and advanced computational analytics for supply chains

Assessment

Assessment task 1: Online discussions/portfolio (Individual)

Intent:	Online discussions/portfolio (5% each week)
Objective(s):	This addresses subject learning objective(s): 1 and 2
Weight:	30%
Length:	150 words max
Criteria:	Credibility of content with evidence and impact Quality of analysis used in short answer discussion briefs (online posts)

Assessment task 2: Real Case Study Analysis (Individual)

Intent:	Report (20%) Recorded presentation (20%)
Objective(s):	This addresses subject learning objective(s): 2 and 3 This addresses program learning objectives(s): 2.1
Weight:	40%
Length:	Report: 500 words, excluding references; Recorded presentation: 2 mins (max)
Criteria:	Critically reflect and demonstrate the extent to which the supply chain has been digitised and identify future scopes for supply chain digitisation. Demonstrate knowledge of the ethical implications of possession and guarding of Big Data, including data security and privacy aspects in multiple contexts, including Indigenous contexts. Demonstration of inter-personal skills and communication strategies to effectively influence diverse stakeholders at multiple levels.

Assessment task 3: Solve a Supply Chain Problem using emerging technologies (Individual)

Intent:	Report (20%) e-poster or Infographic (10%)
Objective(s):	This addresses subject learning objective(s): 1, 2, 3 and 4
Weight:	30%
Length:	Report: 500 words, excluding references; e-poster or Infographic: 1-page
Criteria:	Identification of a problem and demonstration of applied knowledge of Big data, AI and cloud computing technologies in the newly designed digital supply chain using supply chain practices, including sustainable practices. Critically analyse and reflect on the value creation, challenges faced and policy implications due to the adoption of new technologies to solve the identified problem and how these findings influence stakeholders, including Indigenous communities. Presentation of the design of the new supply chain model as discussed in assessment criteria 1 and 2 via the e-poster or infographic.

Minimum requirements

Learners must achieve at least 50% of the subject’s total marks.

Required texts

There is no prescribed textbook required.

References

Resources from various sources will be used throughout the course.