Transforming Supply Chain Management and Performance Analysis

Georgia Tech Professional Education

Georgia Tech Professional Education


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Who should attend

This course is designed for 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.

About the course

This course is the first of four courses in the Supply Chain Analytics Professional certificate program. It prepares you to apply leading-edge analytical methods and technology enablers across the supply chain. You’ll learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you’ll learn about data cleansing, exploratory data analysis, and visualization. You’ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.

What You Will Learn

  • Types of analytics (descriptive, diagnostic, predictive, and prescriptive) and the relationships between them
  • The role of analytics in supply chain management (SCM)
  • Preprocessing (cleaning and integrating) data as it relates to SCM
  • How to conduct exploratory data analysis on supply chain data
  • Best practices for visualizing data and building dashboards
  • How to identify and analyze Key Performance Indicators (KPIs) of SCM
  • Supply chain professionals collaborating in warehouse

How You Will Benefit

  • Understand the most relevant planning challenges across the strategic, tactical, and operational levels of supply chains.
  • Learn the difference between analytics types, the links between them, and how to best use them to improve SCM processes.
  • Use KPIs to find causes of underperformance in supply chains and to plan for analytics projects that will address strategic SCM goals.
  • Utilize Python and PowerBI to understand, visualize, and analyze data in order to prepare for deeper analytics.

Course Content


  • Overview of analytics transforming supply chain management
    • Introduction to SCM and analytics
    • Most relevant planning challenges across strategic, tactical, and operational levels
  • Different types of analytics
    • Descriptive, diagnostic, predictive, prescriptive
    • How the types relate to each other and to SCM
  • Data preprocessing (cleaning and integrating)
  • Activity: Introduce the Cardboard Company (CBC) case and begin preprocessing CBC’s data


  • Data preprocessing for EDA
  • Why EDA is important
  • Conducting EDA
    • Types of data
    • Pareto concept
    • Basic visualization
  • Activity: understand the data available for the CBC case


  • Various visualization techniques
    • Applying those techniques in PowerBI
    • Best data visualization practices
  • Designing user-friendly dashboards
  • Activity: create a dashboard for CBC sales and deliveries data
  • Cardboard Company case introduction


  • Identifying KPIs in SCM
    • Common KPIs
    • Identifying additional KPIs
  • Analyzing KPIs
    • Understanding KPI choices
    • KPIs in descriptive analytics
    • Using KPIs to plan analytics projects
  • Activity: identify and add KPIs to your CBC dashboard


Tim Brown

Mr. Brown has over 30 years of experience in supply chain management operations and services as an operations manager, consultant, educator, and executive recruiter. Tim was appointed as Managing Director in the Georgia Tech Supply Chain & Logistics Institute (SCL). SCL is a unit of Georgia ...

Transforming Supply Chain Management and Performance Analysis at Georgia Tech Professional Education

From  $1,400

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Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

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