Machine Learning in Supply Chain

Categories: Machine Learning
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About Course

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


  • 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.


  • 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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What Will You Learn?

  • 1. Understand the principles and techniques of machine learning and their application in supply chain management.
  • 2. Pre-process and engineer supply chain data to make it suitable for machine learning.
  • 3. Apply supervised learning algorithms to solve supply chain problems, such as demand forecasting and inventory optimization.
  • 4. Apply unsupervised learning algorithms to perform clustering and dimensionality reduction on supply chain data.
  • 5. Apply deep learning algorithms to solve complex supply chain problems, such as quality control and transportation optimization.
  • 6. Evaluate the effectiveness of different machine learning models in solving supply chain problems

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