This module explores the intersection of Artificial Intelligence (AI) and demand forecasting, equipping learners with advanced tools and methodologies to improve decision-making in dynamic, data-rich environments. Students will gain a comprehensive understanding of traditional and AI-based forecasting techniques, with a strong emphasis on real-world applications in supply chain management. This module covers key concepts including time series analysis, machine learning models, neural networks, and deep learning approaches for demand forecasting.

1. Critically evaluate traditional and contemporary demand forecasting methods, identifying their strengths, limitations, and suitability for various real-world scenarios.
2. Examine the strategic role of AI-driven demand forecasting within the broader supply chain, examining its influence on organisational planning, agility, and competitiveness.
3. Propose forecasting solutions using programming tools and AI frameworks, integrating external data to improve forecast accuracy.
4. Apply and optimise AI techniques, including machine learning and deep learning algorithms, to develop and refine demand forecasting models tailored to specific industry contexts.

5. Interpret and communicate forecasting results, leveraging statistical metrics and visualization tools to support strategic business decisions.
6. Critically assess ethical, data governance, and bias considerations in AI-driven forecasting, ensuring responsible use of AI technologies.

This module equips students with the strategic knowledge and practical tools to understand how Artificial Intelligence can be used for managing supply chain risks. Students will explore how AI can be used to identify, assess, and mitigate a wide range of supply chain vulnerabilities from supplier disruptions and geopolitical shocks to operational failures. The module uses case studies, real-world examples, and hands-on low-code activities to provide insight into predictive analytics, real-time monitoring, and building resilience strategies. The module also addresses important topics such as AI ethics, governance, and practical challenges, helping students make thoughtful, data-informed decisions in managing supply chain risks.

1. Appraise and synthesise key sources and types of supply chain risks in a global context.
2. Critically analyse concepts and strategic roles of AI in supply chain risk management within the contemporary organisation.
3. Demonstrate advanced proficiency in AI tools for risk detection and prediction.
4. Critically Interpret AI-generated insights for strategic decision-making and resilience planning.
5. Design risk mitigation strategies incorporating AI tools and real-time data sources.
6. Assess and debate ethical implications of AI-driven supply chain risk management.

This module is designed to help students understand how to use artificial intelligence to make better decisions in circular supply chains. It focuses on how AI can support sustainability by reducing waste, using resources more efficiently, and extending the life of products. Throughout the course, students will explore real-world examples and practical tools that show how AI can improve key areas like reverse logistics, remanufacturing, recycling, and inventory management. The module also covers important topics such as environmental regulations, ESG reporting, and the transition toward autonomous, data-intelligent circular supply chain networks.

1. Critically evaluate the role of circular supply chain models in advancing sustainable business performance and long-term value creation.
2. Appraise the roles of various AI technologies in optimizing circular supply chain operations.
3. Research the future role of AI in shaping autonomous, intelligent, and regenerative circular supply networks.
4. Critically interpret regulatory frameworks and ESG standards, and determine how AI can support traceability, compliance, and responsible governance in supply chain operations.
5. Evalute the potential of AI in enabling stakeholder collaboration and ecosystem innovation in circular supply chains, including suppliers, customers, and regulators.
6. Develop AI-enabled circular supply chain strategies that address sustainability targets, operational risks, and compliance requirements.