Demand Forecasting for a New York D2C Cosmetics Brand
Overview
What this challenge is about.
Forecast 3 months of sales for a D2C cosmetics brand using Holt-Winters and ARIMA, compare accuracy, and present a risk analysis. Earn a verifiable certificate.
The scenario
Glow NYC is a fast-growing D2C brand with 10 SKUs, seasonal demand spikes (e.g., Christmas, summer), and a 30% stockout rate during promotions. They have no dedicated data science team.
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.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Time Series Analysis
Apply time series analysis to solve real industry problems and demonstrate production-level capability.
- Forecasting
Apply forecasting to solve real industry problems and demonstrate production-level capability.
- Data Visualization
Transform complex data into clear, insightful visual representations.
- Excel Or Python
Apply excel or python to solve real industry problems and demonstrate production-level capability.
- Inventory Management
Apply inventory management to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles: