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Machine Learning Applications for Supply Chain Planning (Online or Onsite)

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 first and second course to take this third one in the series, they will be expected to be able to fully perform the skills developed in previous courses.

The online/virtual-Instructor led version of the course is comprised of (4) half-day online instructor-led LIVE group webinars (January 25, 26, 27, 28 | 1-5pm EST or May 17, 18, 19, 20 | 1-5pm EST) 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.

The onsite/in-person version of the course takes place on the Georgia Tech campus over 2 days.

Course discounts are available! For more information please email info@scl.gatech.edu and mention which course you are interested in and which version (Online or Onsite/In-Person).

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

Required

  • Canvas Learning Management System - Visit https://pe.gatech.edu/technology-requirements to ensure the computer you will be using meets the minimum technical requirements to access online material and lessons associated with this course.
  • BlueJeans using both audio and video - Please visit https://bluejeans.com/111​​​​​​ 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

Provided

  • A URL, username and password to access the online course material
  • Optional 2-Hour Online Preparation Seminar offered one week prior to course start which will provide a high-level overview of the software that will be used during the course.
    • Course software: Canvas and video conferencing services
    • Python: Installation and Basics
    • PowerBI: Installation and Basics

Course Prerequisite and Related Certificate Information

Recommended

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

Required

  • 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 Program Capstone

Course CEUs

This course provides for 2.00 continuing education units (CEUs).

Course Fees

Standard: $1,200.00, Alumni/Org Discount: $1,200.00, Certificate: $1,200.00 (cost of each course when signing up for and paying for a multi-course certificate program).

First time attendees pay the listed Course Fee. If you are a returning student of the Supply Chain & Logistics Institute (SCL) courses (or are active/retired military), you will receive a 10% discount when you enter the coupon code SCL-Alum on the "Check Out" page. If you register and pay for all required courses in a Supply Chain & Logistics certificate in one single transaction, you can receive a discount of 17% off each course by entering the coupon code SCL-Cert on the "Check Out" page. There are also discounts available for multiple-team member registrations or to members of certain organizations.

Discounts cannot be combined. For questions, call 404-385-8663 or send us an email prior to registration

The program fee for LIVE courses (non-online) includes continental breakfasts, lunches, breaks, parking, internet access, and all classroom materials.

If the Supply Chain & Logistics Institute must cancel a program, registrants will receive a full refund. Georgia Tech, however, cannot assume the responsibility for other costs incurred. Due to program enrollment limits, early registration is encouraged. Registrations will be acknowledged by a letter of confirmation from Professional Education.

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
  • ​Second Day - 1pm to 5pm
  • Third Day - 1pm to 5pm
  • Fourth Day - 1pm to 5pm
Onsite/In-Person

On the first day, please arrive 30 minutes before the class start time.

  • First Day - 8am to 5pm
  • ​Second Day - 8am to 5pm

UPCOMING OFFERINGS*

January 25, 2021 to January 28, 2021
Online (Instructor-led)
May 17, 2021 to May 20, 2021
Online (Instructor-led)

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