Machine Learning in Supply Chain

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:

1. Introduction to Machine Learning in Supply Chain

• Overview of machine learning
• Types of machine learning
• Importance of machine learning in supply chain management

1. Data Pre-processing and Feature Engineering

• Data cleaning and normalization
• Feature extraction and selection
• Techniques for data pre-processing and feature engineering

1. Supervised Learning

• Linear regression
• Decision trees
• Random forests
• Support vector machines
• Neural networks
• Hands-on training with supervised learning algorithms

1. Unsupervised Learning

• K-means clustering
• Hierarchical clustering
• Principal component analysis
• Hands-on training with unsupervised learning algorithms

1. Deep Learning

• Convolutional neural networks
• Recurrent neural networks
• Autoencoders
• Hands-on training with deep learning algorithms

1. 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.