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Machine Learning for Supply Chains

This specialization is intended for students who wish to use machine language to analyze and predict product usage and other similar tasks. There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.

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What's inside

Four courses

Fundamentals of Machine Learning for Supply Chain

(0 hours)

This course teaches how to leverage Python to understand supply chain datasets. It uses rich datasets to orient students with Pythonic tools and best practices for exploratory data analysis (EDA).

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Demand Forecasting Using Time Series

(0 hours)

This course explores time series, especially for demand prediction. We'll cover basic concepts, including stationarity, trend, cyclicality, and seasonality. We'll analyze correlation methods in relation to time series (autocorrelation). In the 2nd half, we'll focus on demand prediction methods using time series, such as autoregressive models. Finally, we'll conclude with a project, predicting demand using ARIMA models in Python.

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Advanced AI Techniques for the Supply Chain

(0 hours)

In this course, we will explore advanced machine learning methods used in the supply chain. We will cover different ML paradigms, including regression and classification, and examine specific techniques like neural networks and random forests. We will also discuss the assumptions and preprocessing steps required for these models. Finally, we will apply these techniques to an image classification problem to identify faulty products.

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Capstone Project: Predicting Safety Stock

(0 hours)

In this course, we'll predict product usage and calculate safety stock. We'll use a time series of shoe sales and a SARIMA model. We'll analyze statistics to judge the model's viability and tune hyper-parameters for better results. Finally, we'll calculate safety stock using monthly usage predictions and lead times.

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Time

0 hours

Level

Intermediate

Via

Coursera

Institution

LearnQuest

Instructors

Rajvir Dua

Neelesh Tiruviluamala

Level

Intermediate

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