Machine Learning Applications for Supply Chain Planning

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Machine Learning Applications for Supply Chain Planning

Course Description

As the third course in the Supply Chain Analytics Professional program, you’ll be introduced to the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You’ll learn to forecast future demand and use this information to evaluate inventory policies, while also learning the importance of and how to perform customer segmentation. The course will cover regression (trees), advanced time series forecasting, various clustering techniques (such as k-means), decision trees, random forests, neural nets, logistic regression, and Bayes classifiers. Using Power BI and Python, you’ll apply the techniques to sensor data of the fictional Cardboard Company’s paper production to build an anomaly detection model that supports proactive production maintenance planning.

While you are not required to complete Transforming Supply Chain Management and Performance Analysis and Creating Business Value with Statistical Analysis, and Machine Learning Applications for Supply Chain Planning courses prior to this capstone, we suggest you being familiar with their learning outcomes.

Course Content

INTRODUCTION TO MACHINE LEARNING

CLUSTERING AND REGRESSIONS ALGORITHMS

  • Regression (trees), time series forecasting, various clustering techniques (such as k-means)
  • Parameter tuning and cross validation

FORECASTING

  •  CBC’s future demand and use this information to evaluate inventory policies

CLASSIFICATION ALGORITHMS

  • Decision trees, random forests, neural nets, logistic regression and Bayes classifiers
  • Parameter tuning and cross validation

DETECTION MODEL

  • Use sensor data to build an anomaly detection model  
  • Support proactive production maintenance planning
Requirements & Materials
Important Information

Access to the content for this course requires you to set up a password for your Georgia Tech (GT) account different from your account and password on this website.  

After you complete your registration follow the "How to Access Your Course Content" instructions which display on the session details (summary) page for this course.

Prerequisites

Recommended

Required

  • Python/programming experience
  • Power BI experience*

*Don’t have experience with Python or PowerBI? We designed the courses to apply to both people with experience and without experience in these programs. We will provide resources for installing and getting started with the programs in the weeks leading up to the course. One week before the course starts, we will have a webinar to provide more guidance and provide direct assistance. During the course, we will provide the solutions to the exercises so that participants can choose to write the code on their own, use the solutions as hints, or use the solutions entirely and focus on the content rather than coding.

Materials

Required

  • Laptop computer

Provided

  • Lecture materials via PDF in Canvas

Session Details

  • Register and pay for all required courses in a Supply Chain & Logistics certificate and receive a discount of 17% off per course. Enter coupon code SCL-Cert at checkout. Returning students of the Supply Chain & Logistics Institute (SCL) courses or alumni of GT EMIL and MSSCE programs are eligible to receive a 10% discount. Enter coupon code SCL-Alum at checkout. Members of certain organizations are eligible to receive a 10% discount. Enter coupon code SCL-Org at checkout. Review coupon instructions for more information.

Who Should Attend

This course is designed for experienced business professionals who perform (or want to perform) data analyses of any form in the area of supply chain, and who seek to get more from their supply chain data. This course will benefit learners who want additional tools and who want to become a change agent that tackles strategic supply chain goals.  

Supply chain professionals collaborating during course

What You Will Learn

  • Machine learning (ML) techniques
  • ML algorithms
  • How to apply ML in demand forecasting, sales and operation planning (S&OP), and inventory management
  • ML in production planning and predictive maintenance
  • Forecasting
  • Advanced analytics techniques using engine downtime predictive modeling example
Supply chain professionals collaborating in warehouse

How You Will Benefit

  • Understand the use of regression and clustering techniques in supply chain planning.
  • Apply ML in demand forecasting, S&OP, and inventory management.
  • Use Python and Power BI to build forecasting models.
  • Apply advanced analytics techniques to build planning tools that can leverage large and real-time data sets.
  • Understand and apply ML techniques specific to production planning and predictive maintenance.
  • Build an anomaly detection model that supports production maintenance planning.
  • Taught by Experts in the Field icon
    Taught by Experts in the Field
  • Grow Your Professional Network icon
    Grow Your Professional Network

The purpose of taking these courses was to advance my career and gain and greater understanding of the role that I am in with my employer. Our group was diverse in all of their roles and came from various industries, so I was able to get information they shared, and I can apply that to what I am doing in my own role.

- Roz Nero
Procurement Negotiator and Proposal Specialist
Roz Nero, procurement specialist

TRAIN AT YOUR LOCATION

We enable employers to provide specialized, on-location training on their own timetables. Our world-renowned experts can create unique content that meets your employees' specific needs. We also have the ability to deliver courses via web conferencing or on-demand online videos. For 15 or more students, it is more cost-effective for us to come to you.

  • Save Money
  • Flexible Schedule
  • Group Training
  • Customize Content
  • On-Site Training
  • Earn a Certificate
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