Sprout

Sprout is a simple Business Intelligence (BI) system designed for retail stores. It consists of six separate but linked Dash applications running on different ports. The system provides key insights into inventory, sales/performance, customer behaviour, business goals, advanced analytics, and reports with customizable queries.

Features

Sprout consists of six dashboards:

1. Inventory Dashboard - Displays statistics on current inventory levels assiting in inventory management.
2. Sales Dashboard - Shows sales performance and key metrics to track business growth.
3. Customer Dashboard - Analyzes customer behavior and statistics.
4. Goals Dashboard - Tracks existing business goals and allows users to set new ones.
5. Analysis Dashboard - Provides advanced analytical insights, including:
   • Time Series Sales Prediction using Facebook's Prophet model.
   • Customer Lifetime Value (CLV) Analysis.
   • Product Affinity Analysis.
6. Queries Section - A collection of reports with advanced filtering mechanisms (not a dashboard but an interactive reporting tool).

Installation and Running the Project

Prerequisites

• Python installed on your system
• Required dependencies (see requirements.txt)

Steps to Run the Application

1. Clone the repository:

   git clone <repository_url>

2. Navigate into the project directory:

   cd Sprout

3. Navigate to the dashboards folder:

   cd dashboards

4. Run all the applications using the provided PowerShell script:

   ./run_all_apps.ps1

5. Open a web browser and visit one of the running dashboards, e.g., http://localhost:8050.

Notes

• This project originally included a separate home page for access control, but it is not included in this version.
• Each dashboard runs on a separate port and contains navigation links to the other dashboards.
• Use the side menu to explore different dashboards.

Technologies Used

• Dash (for building web-based dashboards)
• Plotly (for interactive visualizations)
• Prophet (for time series forecasting)
• Pandas & NumPy (for data manipulation)
• Statsmodels (for statistical analysis)
• Dash Mantine Components, Dash Bootstrap Components (for UI enhancements)

Future Improvements

• User management and Access Control.
• Enhancing visualizations and with more interactivity.
• Expanding analytics with additional/better predictive models.
• Use of machine learning models for clv and products affinity analysis instead of mathematical/statistical methods.

License

This project is open-source. Feel free to modify and extend it as needed.

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For questions or contributions, feel free to reach out!