TRC5801 - Data driven supply chain optimisation and AI applications

Overview

This unit provides a comprehensive introduction to the use of data analytics and artificial intelligence (AI) in optimising modern supply chains. You will gain skills to evaluate key supply chain processes and apply data-driven methodologies to enhance efficiency, reduce costs and improve decision-making. Through hands-on projects and case studies, you will design predictive models using AI and matching learning techniques, synthesising data from multiple sources to address complex challenges. You will explore the integration of AI within decision-making frameworks, ensuring supply chain resilience and adaptability in dynamic global markets. Emphasis is placed on the importance of sustainable and ethical practices, highlighting their role in creating socially responsible supply chain strategies. By the end of the unit, you will have a strong foundation in leveraging data insights to justify strategies and solutions in real-world supply chain environments, preparing you for roles in industries increasingly driven by AI and data analytics.

Offerings

S1-01-MALAYSIA-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Dr Alpha Agape

Unit Coordinator(s)

Dr Alpha Agape

Learning outcomes

On successful completion of this unit, you should be able to:

Evaluate supply chain processes and use data analytics for optimisation.

Design AI-driven predictive models for decision-making processes within supply chains.

Synthesise data from various sources to solve complex supply chain challenges.

Justify supply chain strategies through data-driven insights.

Appreciate sustainable and ethical supply chain practices and their impact on the largest society.

Teaching approach

Peer assisted learning

Active learning

Online learning

Simulation or virtual practice

Assessment summary

Continuous assessment: 50%
Final assessment: 50%

This unit contains threshold hurdle requirements that you must achieve to be able to pass the unit. You are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final assessment component. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Assignments

2 - Tests and quizzes

3 - Project

4 - Final assessment

Scheduled and non-scheduled teaching activities

Practical activities

Workshops

Workload requirements

Workload

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.

Availability in areas of study

E6013 Master of Applied Engineering - Specialisation: Industrial and robotics engineering