Grupo 65

The wide diversity of products offered by the businesses allows attracting several different customer types and sell most of the crafted stock. However, these remaining items result as a problem because it takes up space and stalls the arrival of new collections. In our proposal we consider that the issue on hand can be resolved with an adequate forecasting prediction model(s) that considers the impact of the Inventory, Portfolio and Production variables. Being able to get the right forecasting model that avoids an excess of inventory and diminish storage costs to the company, while at the same time keeping enough product for customers to purchase.

To provide a consistent solution, we consider important to gather information that accurately represents the current and past stock movements and related sales distributions over time, in conjunction with the economical context and the unique industry appropriation of Furni Inv S.A.S. in relation to the whole industry. All to be able to correctly interrelate possible market movements and significant sale appropriation of the company to accomplish the desired goal.

* Python
* Install pandas
* Install dash
* Install dash_bootstrap_components
* Install scikit-learn
* Install statsmodels

Clone the project

Open a console and run

pip install -r requirements.txt

Open a console on the project's root folder

On the console run

python main.py

Open your browser on http://localhost:8050/

• Pandas - Open source data analysis and manipulation tool
• Dash - Low-code framework for rapidly building web data apps
• Dash Bootstrap Components - Library of components that makes it easier to build consistently styled apps with complex, responsive layouts
• Scikit-learn - Simple and efficient tools for predictive data analysis
• statsmodels - Python module that provides classes and functions for the estimation of many different statistical models

• Andrés Manrique Ardila
• Juan Manuel Velez
• Nicholas Gooding
• Johann Roa
• David Rubio