Analysis
Demand Forecasting for a New York D2C Cosmetics Brand
Overview
What this challenge is about.
You are a forecasting analyst at Glow NYC. Using provided monthly sales data (units sold) for 10 SKUs, develop a time series forecast for the next 3 months. Compare at least two methods (e.g., Holt-Winters and ARIMA), evaluate accuracy using RMSE and MAE, and recommend a model. Present a dashboard with forecasts and a risk analysis for the upcoming holiday season. Success is a validated forecast with clear business recommendations.
The Brief
What you'll do, and what you'll demonstrate.
How can Glow Berlin accurately forecast short-term demand to reduce stockouts and excess inventory?
Earning criteria — what you'll demonstrate
- Apply time series decomposition to identify trend and seasonality
- Implement and compare exponential smoothing and ARIMA models
- Evaluate forecast accuracy using appropriate metrics
- Translate forecasting results into inventory management decisions
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career mappings coming soon.