Demand Forecasting Models for Contraceptive Supply Chain
Forecasting
Healthcare
R
Python
Machine Learning
An introduction to time series forecasting
Demand Forecasting Models for Contraceptive Supply Chain
An introduction to time series forecasting
Context
Accurate demand forecasting is essential for ensuring reliable access to contraceptive products, supporting key processes like procurement, inventory management, and distribution. This course aims to equip pharmaceutical officers at healthcare site levels with the knowledge and tools to adopt modern forecasting methods using R and Python.
Target Audience
- Pharmaceutical officers at healthcare site levels responsible for demand planning
- Anyone interested in forecasting in the context of contraceptive supply chains
Learning Outcomes
Day 1: Forecasting with R
- Familiarize with RStudio and R Notebooks.
- Learn data wrangling and feature engineering.
- Understand time series graphics.
- Explore models: sNAIVE, Moving Average, ARIMA, ETS.
- Evaluate model performance.
Day 2: Advanced Forecasting with Python
- Familiarize with Google Colab and Python Notebooks.
- Implement advanced models: LightGBM, XGBoost, and TimeGPT.
- Evaluate advanced model performance.
- Explore additional learning resources.
Preparation & Prerequisites
No prior experience in time series is required. However, familiarity with the following is recommended:
- Writing R code and using tidyverse packages (dplyr, ggplot2). Learn R here.
- Writing basic Python code. Learn Python here.
- Basic statistical concepts such as mean, variance, and regression.
- Please have a laptop capable of running both R and Python.