Course Duration: 6 weeks (12 sessions, 1.5 hours per session)
Prerequisites:
- Basic knowledge of supply chain management
- Fundamentals of statistics and linear algebra
- Basic programming skills in Python
- Familiarity with Machine Learning concepts
Final Project (Week 6)
- Design and implement a machine learning model for one of the following:
- Demand forecasting for a given product line
- Inventory optimization for a retail store
- Route optimization for a logistics provider
Tools & Technologies:
- Programming Language: Python
- Libraries: Scikit-learn, Pandas, NumPy, TensorFlow, Keras, Pyomo, Gurobi (for optimization)
- Data Visualization: Matplotlib, Seaborn
- Cloud Platforms: AWS Sagemaker, Google Cloud ML
Learning Outcomes:
- Understand the role of ML in solving supply chain challenges.
- Develop predictive models for demand forecasting and inventory optimization.
- Implement ML solutions for network design and route optimization.
- Apply anomaly detection techniques to improve supply chain resilience.
- Gain hands-on experience with real-world supply chain datasets.