AI-Powered Inventory Demand Forecasting System

1. Challenge Overview

This challenge focuses on developing an AI-driven inventory demand forecasting system that helps retailers optimize stock levels. The solution should predict product demand, reducing the risk of overstocking and stockouts while ensuring efficient inventory management.

2. Problem Statement

Build a machine learning-powered demand forecasting system that:

• Uses historical sales data, seasonal trends, and external factors (e.g., promotions, holidays, weather) to predict product demand.
• Supports two interfaces:
  • Retailer Dashboard – Allows retailers to input data, visualize predictions, and adjust inventory levels accordingly.
  • Analytics Engine – Processes data, applies ML models, and provides demand forecasts.
• Stores forecasting data in a database (SQL or NoSQL).
• Bounty Points for real-time integration with a POS (Point-of-Sale) system.

3. Technical Approach

Participants must implement:

• Machine Learning Model – Train a time-series forecasting model (e.g., ARIMA, LSTM, Prophet) to predict demand trends.
• Database Integration – Store historical sales data and generated predictions in SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase).
• Retailer Dashboard – Build an interactive dashboard (React, Flask, or Streamlit) to:
  • Display demand forecasts.
  • Adjust inventory levels based on predictions.
  • Visualize demand trends and alerts.
• External Factors Integration – Improve model accuracy by incorporating external variables (e.g., seasonality, weather, promotions).
• Notification System – Alert retailers about potential overstock or stockouts based on predictions.
• Performance Optimization – Measure model accuracy (MAE, RMSE) and response time.

4. Retailer Dashboard Features

The dashboard should enable retailers to:

• Upload historical sales data for training the ML model.
• View demand forecasts for different time horizons (daily, weekly, monthly).
• Set up automatic restocking alerts based on demand predictions.
• Monitor trends and analyze the impact of external factors.
• Track product performance and identify fast/slow-moving items.

5. References

• Prophet for Time-Series Forecasting: Prophet GitHub
• ARIMA for Demand Prediction: Statsmodels ARIMA
• OpenWeather API for Weather Data: OpenWeather API
• Twilio API for Notifications: Twilio Docs

6. Evaluation Criteria

Submissions will be assessed based on:

• Forecasting Accuracy (40%) – Model precision in predicting demand fluctuations.
• Model Efficiency (20%) – Performance metrics such as MAE, RMSE, and response time.
• User Experience (20%) – Ease of interaction with the dashboard.
• Scalability & Integration (10%) – Ability to scale for large datasets and integrate external factors.
• Code Quality & Documentation (10%) – Maintainable, well-documented code with clear setup instructions.

7. Deliverables

• Source Code & README – Well-documented code with setup instructions.
• Working Forecasting System – Fully functional model with a dashboard.
• Evaluation Report – Comparison of model performance (MAE, RMSE).
• Demo Video (Optional) – Short video showcasing system functionalities.

8. Implementation Guidelines

• Implement time-series forecasting using Prophet, ARIMA, or LSTM.
• Store and process data in SQL or NoSQL databases.
• Develop a user-friendly dashboard for retailers to interact with forecasts.
• Integrate real-time updates from POS systems (if possible).
• Ensure multi-user support for handling multiple retail branches.
• Implement automated alerts for inventory restocking.