I’ll be starting the ISyE Time Series Analysis course at Georgia Tech in future semesters and plan to take the Machine Learning for Supply Chains Specialization course on Coursera. This project will integrate the skills I gain from these courses to enhance my expertise in applying machine learning techniques to supply chain challenges.

The Coursera course focuses on using Python to handle and analyze supply chain data, employing libraries such as Numpy and Pandas. It covers a range of topics from basic and advanced Python functionalities to solving supply chain cost optimization problems using Linear Programming with PuLP. The course delves into demand forecasting using time series analysis, including building ARIMA models and developing frameworks for advanced neural networks like LSTMs. Additionally, it explores advanced machine learning methods such as neural networks and random forests, addressing their applications in predicting product demand and classifying products. A capstone project on predicting safety stock using SARIMA predictions and manipulating lead times will solidify these concepts. By applying the skills from my deterministic optimization course, the aim to improve decision-making processes in supply chains through advanced machine learning techniques. Stay tuned for this exciting project!