This course is designed for supply chain professionals who want to develop a deep understanding of machine learning and its application in supply chain management. The course covers topics such as data pre-processing, feature engineering, supervised and unsupervised learning, deep learning, and their application in supply chain forecasting, demand planning, inventory optimization, transportation optimization, and quality control.
Course Outline:
- Introduction to Machine Learning in Supply Chain
- Overview of machine learning
- Types of machine learning
- Importance of machine learning in supply chain management
- Data Pre-processing and Feature Engineering
- Data cleaning and normalization
- Feature extraction and selection
- Techniques for data pre-processing and feature engineering
- Supervised Learning
- Linear regression
- Decision trees
- Random forests
- Support vector machines
- Neural networks
- Hands-on training with supervised learning algorithms
- Unsupervised Learning
- K-means clustering
- Hierarchical clustering
- Principal component analysis
- Hands-on training with unsupervised learning algorithms
- Deep Learning
- Convolutional neural networks
- Recurrent neural networks
- Autoencoders
- Hands-on training with deep learning algorithms
- Supply Chain Applications of Machine Learning
- Forecasting and demand planning
- Inventory optimization
- Transportation optimization
- Quality control
- Best practices for applying machine learning in supply chain management
Prerequisites:
- Basic knowledge of supply chain management concepts and principles.
- Familiarity with statistics and programming languages such as Python or R.
Recommended Textbook:
- “Machine Learning in Supply Chain Management: An Introduction” by Keivan Tahmasebian.
Assessment:
- Midterm Exam (30%)
- Final Exam (40%)
- Machine Learning in Supply Chain Project (30%)
Note: The course syllabus and assessment methods are subject to change based on instructor’s discretion.
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