ML Basics for Supply Chain Management Applications

This course is designed to equip Supply Chain Management (SCM) professionals with the foundational knowledge of Artificial Intelligence (AI) and its learning capabilities. You'll explore how AI can revolutionize SCM processes, optimize your operations, and gain a competitive edge.

• Define AI and its core concepts.

• Understand different AI learning approaches (supervised, unsupervised, reinforcement learning).

• Identify real-world applications of AI in Supply Chain Management.

• Analyze the impact of AI on forecasting, demand planning, inventory management, and logistics.

• Explain the ethical considerations surrounding AI implementation.

1. Introduction to AI

• What is AI? History and evolution of AI.

• Types of AI: Machine Learning, Deep Learning, Natural Language Processing (NLP).

• Benefits and limitations of AI in SCM.

2. AI Learning Techniques

• Supervised Learning: Regression, Classification.

• Unsupervised Learning: Clustering, Dimensionality Reduction.

• Reinforcement Learning: Applications in SCM optimization.

3. AI Applications in SCM

• Demand forecasting with AI models.

• Intelligent inventory management with AI.

• AI-powered optimization of transportation and logistics.

• Case studies of successful AI implementations in SCM.

4. The Future of AI in SCM

• Ethical considerations of AI in supply chains (bias, transparency).

• Emerging trends: AI for risk management, sustainability, and automation.

• Career opportunities in AI-powered supply chain management.

• Basic understanding of mathematics, including calculus and linear algebra

• Familiarity with programming concepts and a language such as Python

• Knowledge of basic machine learning concepts and algorithms