Georgia Institute of Technology Physical Internet Center | Georgia Institute of Technology | Atlanta, GA

Course Description

This course is the second in the four-course Supply Chain Analytics Professional certificate program. It emphasizes operational performance metrics to align supply chain management with strategic business goals. You’ll learn several statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) along with inventory management models. You’ll use diagnostic analytics with PowerBI and Python to conduct demand and service profiling, undertake root cause analysis, and use time series forecasting in inventory management.

The online version of 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.

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 why and how to align Supply Chain Management (SCM) strategy with business strategy
• Learn statistics techniques as they relate to SCM
• Understand inventory management models and how to apply statistics techniques to them
• Create time series forecasts based on SCM data
• Utilize Python and PowerBI to perform statistical analyses, create time series forecasts and visualize results

What Is Covered

• The importance of aligning SCM and business strategy
• How to ask the right business questions as they relate to SCM
• How to use statistics to identify issues, compare data, and forecast decision outcomes
• Statistical concepts including variance analysis and hypothesis testing
• Inventory management models
• Applying statistics to inventory management models
• Forecasting techniques including time series forecasting
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)