Supply Chain Analytics And Digitalisation
UndergraduateLTU-BUS3SSP2024
Course information for 2024 intake View information for 2025 course intake
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- Study method
- 100% online
- Assessments
- 100% online
- Entry requirements
- Part of a degree
- Duration
- 12 weeks
- Loan available
- HECS-HELP and FEE-HELP available
Supply Chain Analytics And Digitalisation
About this subject
On successful completion you will be able to:
- Critique an existing supply chain design with the intention of improving technology contributions.
- Model and explain a data driven smart supply chain based on best practice technology and data integration.
- Assess the potential effectiveness, efficiency, and transparency of a smart supply chain design.
- Basics of Logistics and Supply Chain Management (KPI)
- Introduction to Supply Chain Analytics
- Managing Logistics and Supply Chain Distribution network
- Balancing Demand and Supply
- Warehousing and Inventory Management
- Introduction to Supply Chain Digitalisation and Industry 4.0
- Internet of Things (IoTs) in Smart Supply Chain
- AI and Machine learning in logistics and Supply Chain
- Cloud Computing in Logistics and Supply Chain
- Blockchain for Supply Chain and Social Sustainability
- Supply Chain Digital Twins
- Achieving Environmental Sustainability through Supply Chain Digitalisation
Technology-enabled supply chains are becoming increasingly effective, efficient, and transparent. In the future scenario of smart supply chains, a supply chain in the real world will be simulated by its digital twin deployed in a cloud computing platform. Real-time data in factories, warehouses, and transport vehicles will be collected and processed by Internet of Things (IOT) devices, and then sent to the supply chain’s digital twin. The data analytics and artificial intelligence (AI) models behind the digital twin will analyse the data, simulate the supply chain processes in the real word, and help us to make informed decisions for supply chain management, such as inventory control and redeployment for supply chain disruptions, dynamic management of suppliers, and fast responses to customers’ complaints. In this subject you will learn how to model and design smart supply chains to enable data driven decision making, optimisation of operations, and adaptation to changing market conditions. Using authentic case studies and practical digital tools, you will develop the critical thinking and problem-solving skills required to identify problems in the smart supply chains and develop practical solutions to address these problems.
- Prepare an individual report on supply chain mapping and analysis. Equivalent to 900 words per student. (20%)
- Using AI and simulation tools in supply chain. Equivalent to 1800 words per student. (40%)
- A presentation on a business case study on smart supply chain (equivalent to 1800 words per student) (40%)
For textbook details check your university's handbook, website or learning management system (LMS).
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- QS Ranking 2024:
- 17
- Times Higher Education Ranking 2024:
- 18
Entry requirements
Part of a degree
To enrol in this subject you must be accepted into one of the following degrees:
Elective
- LAT-BUS-DEG-2024 - Bachelor of Business
- LAT-ART-DEG-2024 - Bachelor of Arts
- LAT-PYS-DEG-2024 - Bachelor of Psychological Science
Others
Prerequisites: Students must have completed 240 credit points (at least 120 credit points at Level one, and at least 90 credit points at Level two).
Additional requirements
No additional requirements
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.
Related degrees
Once you’ve completed this subject it can be credited towards one of the following courses
UndergraduateLAT-BUS-DEG
UndergraduateLAT-ART-DEG
Bachelor of Psychological Science
UndergraduateLAT-PYS-DEG