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Machine Learning Applications for Supply Chain Planning

Special Note

This course is the third of 4 courses in the Supply Chain Analytics Professional (SCA) certificate. While participants are not required to complete the program’s previous courses in the series, we suggest being familiar with the learning outcomes of the previous courses.

The course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Please note that this course qualifies for our Georgia AI Manufacturing (GA-AIM) program discount for Georgia residents. Please see below Course Fees section for details.

Visit the course listing within the Georgia Tech Professional Education website.

Course Description

This course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces 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 use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You’ll use Python and PowerBI to create and analyze regression, clustering, and classification models.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

  • Understand the role of machine learning (ML) in Supply Chain Management (SCM)
  • Apply advanced analytics techniques to build planning tools that can leverage large and real-time data sets
  • Apply ML in demand forecasting and predictive maintenance
  • Understand how to assess ML model performance, improve models, and pick the best model for a decision
  • Use Python and PowerBI to build, analyze, and deploy ML models

What Is Covered

  • How ML relates to SCM
  • ML algorithms such as regression trees, clustering techniques, decision trees, random forests, logistic regression
  • Aspects of ML projects including parameter tuning, cross validation, and assess model performance
  • Application of ML in demand forecasting for sales and operations planning (S&OP) and inventory management
  • Application of ML in predictive maintenance
  • Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

Below is format for the online/virtual-Instructor led version of the course.

Webinar 1 – Machine Learning for SCM (regression and clustering)
Intro to ML as it relates to SCM
Regression and clustering models
  • Regression trees
  • Advanced time series forecasting
  • Various clustering techniques
Assessing model performance
  • Interpretability vs accuracy
  • Parameter tuning
  • Cross validation
Activity: Use techniques to forecast the Cardboard Company (CBC)’s demand
Homework: Finish CBC’s demand forecast
Webinar 2 – Demand Forecasting: S&OP and Inventory Management
Perform customer segmentation
Use ML to determine inventory policies
Activity: Determine CBC’s inventory polices using ML
Homework: Finish creating and assessing CBC’s ML models
Webinar 3 – Machine Learning for SCM (classification)
Classification models
  • Decision trees
  • Random forests
  • Logistic regression
Building planning tools with large and real-time data sets
Activity: Apply these models as they relate to CBC’s SCM
Homework: Finish creating CBC classification models
Webinar 4 – Production Planning and Predictive Maintenance
Production planning
  • Building anomaly detection models
  • Activity: build an anomaly detection model to sensor CBC’s paper production data
Predictive maintenance
  • Using models to support proactive production maintenance planning
  • Activity: build a predictive maintenance model for CBC
Final assignment (due 1 week after last webinar)
  • Complete the CBC predictive maintenance model
  • Create an explanation of your model choices for the instructors
  • Complete final assessment

Course Materials


  • Canvas Learning Management System - Visit to ensure the computer you will be using meets the minimum technical requirements to access online material and lessons associated with this course.
  • Zoom using both audio and video - Please visit​​​​​​ to ensure the computer you will be using is compatible.
  • Python installed on local computer with additional packages identified by the instructors
  • Power BI installed on local computer


  • A URL, username and password to access the online course material

Course Prerequisite and Related Certificate Information


  • General SCM knowledge
  • A general understanding of probability
  • Basic Python / Programming experience
  • Basic PowerBI experience


  • Knowledge of content and ability to apply skills covered in Course 1 and Course 2 of the program

For those interested in earning the Supply Chain Analytics (SCA) Professional Certificate, take the below 4 courses within four years.

  1. Transforming Supply Chain Management and Performance Analysis
  2. Creating Business Value with Statistical Analysis
  3. Machine Learning Applications for Supply Chain Planning
  4. Supply Chain Optimization and Prescriptive Analytics

Course CEUs

This course provides for 1.40 continuing education units (CEUs).

Course Instructors

Course Times

Online/Virtual-Instructor led 

On the first day, please log in at least 15 minutes before the class start time.

  • First Day - 1pm to 5pm ET
  • ​Second Day - 1pm to 5pm ET
  • Third Day - 1pm to 5pm ET
  • Fourth Day - 1pm to 5pm ET

Course Fees

Standard: $1,100.00, Certificate: $913.00 (cost of each course when signing up for and paying for a multi-course certificate program).

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.

All residents of the State of Georgia are eligible for a 50% discount while funds last thanks to a grant from the U.S. Department of Commerce's Economic Development Administration. Use of this discount is subject to verification of GA residency. Enter coupon code SCL-GAAIM at checkout.

If you have 3 or more participants from your organization, please contact us for volume discounts. Review coupon instructions for more information.

Discounts cannot be combined. For questions, call 404-894-2343 or send us an email prior to registration.


September 16, 2024 to September 19, 2024
Virtual (Instructor-led)

Supply Chain Analytics Series Information Session Webinar

An interview with Daphne de Poot, one of the SCA course series instructors.

ISyE location map

Georgia Tech Supply Chain and
Logistics Institute
H. Milton Stewart School of
Industrial & Systems Engineering
765 Ferst Drive, NW, Suite 228
Atlanta, GA 30332
Phone: 404.894.2343