Project detail
Product Demand with PyMC
Bayesian model for estimating future demand and supporting product, inventory, and launch decisions.
Highlights
- Demand prediction with Bayesian inference
- Support for product and inventory planning
- Useful model for uncertain scenarios
- Simple framework to extend with more variables
No public repository or demo link is published for this project yet.
Architecture
Probabilistic example with simulated historical data and posterior predictive estimation of expected demand
Challenges
- Representing demand uncertainty without overfitting
- Incorporating external signals without losing simplicity
- Turning predictions into actionable decisions
Outcome
A clear foundation for estimating future demand and reducing blind product decisions
Overview
Problem
Demand changes due to many factors, and deterministic models often fall short when uncertainty matters.
Approach
I used PyMC to model demand as a distribution and obtain a more realistic estimate of the future.
Outcome
The result helps plan inventory, product priorities, and launch scenarios more effectively.