Data driven supply chain optimisation and AI applications

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 machine 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.

Online learning - Independent study using video, written and online quiz-based resources.

Peer assisted learning - Working in a team of 2, you will work to apply what you have learnt in the workshops to solve case studies via assignments.

Simulation or virtual practice - Modern simulation tools will be introduced to help you simulate and visualise the production line and understand how various parameters affect the supply chain and operations management.

Active learning - Working through theory-based, problem-based and industrial-based examples during the workshop sessions.

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.