Demand Forecasting KPIs for Supply Chain Planning
What you'll learn
- How to compute RMSE, MAE, MAPE, WMAPE, and Bias
- Pros and Cons of each forecasting metric
- Which forecasting metric(s) to use when evaluating the quality of a forecast
- Evaluate the quality of a forecast using relevant KPIs
- Why MAPE is the worst forecasting KPI and what to do instead
Requirements
- Excel basics
Description
This course will teach you how to use various forecasting metrics (Bias, MAE, MAPE, WMPAE, and RMSE) to select the best demand forecast. The end goal is that you can (as a demand planner or S&OP leader) automatically assess the quality of forecasts at scale - even if you have a wide product portfolio (including intermittency and products with different prices).
Specifically, you will learn:
The pros and cons of Bias, MAE, WMAPE, MAPE, and RMSE,
How Forecasting KPIs are influenced by intermittent demand and outliers,
Why MAPE is the worst forecasting KPI,
Which metric(s) to use to balance accuracy and bias?
How to use value-weighted KPIs to assess the quality of your forecasts, even if you deal with various products with different prices.
This course alternates theory with Do-It-Yourself exercises in Excel. You will learn by doing and gain hands-on experience with practical examples and case studies. You will be able to apply these concepts (and use the Excel templates) directly to your work environment for immediate impact.
The course includes one hour of videos, and its content is based on my books (Data Science for Supply Chain Forecasting and Demand Forecasting Best Practices) and the content of the course I teach to professionals and university students.
It should take you 2 to 4 hours to complete it.
The course only requires limited experience with Excel (such as using usual formulas such as average and sum).
Who this course is for:
- Demand and supply planners
- S&OP managers and leaders
- Supply chain practitioners in general
Instructor
- 4.1 Instructor Rating
- 239 Reviews
- 1,048 Students
- 2 Courses
Nicolas Vandeput helps supply chain leaders achieve demand and supply planning excellence.
He founded his consultancy company, SupChains, in 2016; and SKU Science, an online platform for supply chain forecasting, in 2018. Passionate about education, Nicolas is both an avid learner and a teacher.
Since 2020, he has been teaching demand forecasting and inventory optimization to master students in CentraleSupelec, Paris, France. He also teaches demand forecasting at Albert School, Paris, France, and guest teaching in various universities worldwide.
He published three books: Data Science for Supply Chain Forecasting in 2018 (second edition in 2021), Inventory Optimization: Models and Simulations in 2020, and Demand Forecasting Best Practices in 2023.