This repository contains a functional AIMMS example model for Demand Forecasting. It demonstrates how to apply multiple forecasting algorithms from the AIMMS Forecasting library to historic demand data, enabling comparison and selection of the best-fit method for your business.
Imagine you run a small cookie factory. For a few months, you have been storing daily demand data and now you want to use it to forecast future demand. This analysis will:
- Create understanding of how demand will behave over time.
- Decrease waste through better planning.
- Help you choose the best-fit forecasting algorithm by comparing all available methods side by side on a single dashboard.
To get the most out of this model, we highly recommend reading our detailed step-by-step guide on the AIMMS How-To website:
👉 Read the Full Article: Demand Forecasting Guide
- AIMMS: You will need AIMMS installed to run the model. Download the Free Academic Edition here if you are a student.
- WebUI: This model is optimized for the AIMMS WebUI for a modern, browser-based experience.
- Forecasting Library: Install the
Forecastinglibrary from the AIMMS Library Repository. An AIMMS Community license is sufficient.
- Download the Release: Go to the Releases page and download the
.zipfile from the latest version. - Open the Project: Launch the
DemandForecasting.aimmsfile. - Run the Model: Use the WebUI workflow to navigate through each forecasting method and compare results on the Dashboard.
This example is maintained by the AIMMS User Support Team.
- Found an issue? Open an issue.
- Questions? Reach out via the AIMMS Community.
Maintained by the AIMMS User Support Team. We optimize the way you build optimization.