AI for Supply Chain

This course explores how artificial intelligence is revolutionizing the supply chain industry, enhancing efficiency, accuracy, and decision-making. It delves into AI-driven solutions such as demand forecasting, warehouse automation, and route optimization.

This course covers fundamental mathematical concepts for machine learning, including derivatives, integrals, and optimization techniques like gradient descent.

This course, taught by a leading expert, covers the fundamentals of convex optimization and its applications.

This course covers advanced methods for data cleaning, preparation, and optimization using AI-assisted tools. You'll learn to generate synthetic data, address privacy concerns, and resolve data quality issues.

This course, part of a Machine Learning for Supply Chain Fundamentals specialization, explores all aspects of time series for demand prediction. It covers basic concepts like stationarity, trend, and seasonality, and then moves to autoregressive models and a final project on predicting demand using ARIMA in Python.

While not strictly focused on convex/stochastic optimization for ML, this course provides a strong foundation in optimization principles through discrete problems.

A beginner-friendly course exploring how Generative AI is transforming supply chain management, covering applications in demand forecasting, inventory optimization, and logistics through practical insights and case studies.

A lecture focusing on the role of optimization in machine learning, covering various algorithms and their properties.

Learn DeepLearning.AI Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Welcome to Supply Chain Analysis with Machine Learning & Neural Network course. This is a comprehensive project based course where you will learn step by step on how to perform complex analysis and visualization on supply chain dataset. This course will be mainly focusing on performing cost optimization, demand forecasting, lead time efficiency, risk management, and order quantity optimization. We will be utilizing two different models, those are LightGBM which is a machine learning model and RNNs which stands for Recurrent Neural Networks. Regarding programming language, we are going to use Python alongside several libraries like Pandas for performing data modelling, Numpy for performing complex calculations, Matplotlib for visualizing the data, and Scikit-Learn for implementing the machine learning models.Meanwhile, for the data, we are going to download the supply chain dataset from Kaggle. In the introduction session, you will learn basic fundamentals of supply chain analytics, such as getting to know its key objectives, getting to know models that will be used, and challenges that we commonly faced when it comes to analyzing supply chain data for example demand volatility and data integration. Then, you will continue by learning the basic mathematics and logics behind price and order quantity optimization where you will be guided step by step on how to solve a basic case study using economic order quantity equation. This session was designed to prepare your knowledge and understanding about order quantity optimization before implementing this concept to your code in the project. Afterward, you will learn about several different factors that can potentially cause supply chain disruption, such as natural disaster, economic volatility, and supplier issues. Once you’ve learnt all necessary knowledge about supply chain analytics, we will start the project. Firstly, you will be guided step by step on how to set up Google Colab IDE, then, you will also learn how to