https://github.com/ritu456286/smartstockai
SmartStockAI uses AI to predict inventory trends, minimize deadstock risks, and provide actionable insights through advanced models and interactive visualizations.
https://github.com/ritu456286/smartstockai
bigquery bigquery-ml cloud-storage cloudrun cloudsql gemini google-maps-api
Last synced: about 1 month ago
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SmartStockAI uses AI to predict inventory trends, minimize deadstock risks, and provide actionable insights through advanced models and interactive visualizations.
- Host: GitHub
- URL: https://github.com/ritu456286/smartstockai
- Owner: ritu456286
- Created: 2024-12-18T10:09:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-08T19:08:56.000Z (over 1 year ago)
- Last Synced: 2025-03-25T06:45:18.370Z (about 1 year ago)
- Topics: bigquery, bigquery-ml, cloud-storage, cloudrun, cloudsql, gemini, google-maps-api
- Language: Python
- Homepage: https://youtu.be/6f0wTAcmsTo
- Size: 214 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SmartStockAI: Predictive Inventory & Deadstock Management
## 🚀 Overview
SmartStockAI is an AI-driven solution that optimizes inventory management by predicting inventory trends, identifying potential deadstock risks, and generating actionable insights to minimize losses. Leveraging **advanced machine learning models** and **large language models (LLMs)**, it transforms traditional inventory management into a dynamic, data-driven process adaptable to changing market conditions and consumer behaviors.
🏆 **Winner of Google Build and Blog Marathon '24**
## 🌐 Live Demo
🎥 **[Watch Demo Video](https://www.youtube.com/watch?v=6f0wTAcmsTo)**
🚀 **Deployment:** Initially deployed on `Cloud Run`, but due to cloud charges, it has been removed. All functionalities are showcased in the demo video.
📝 **Read the Full Story:** **[Medium Blog](https://medium.com/google-cloud/smartstockai-predictive-inventory-deadstock-management-ea8cb0556081)**
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## 🔥 Features
✅ **Demand Forecasting** - Uses the **ARIMA_PLUS** model in **BigQuery ML** to predict future sales and identify potential deadstock.
✅ **Unstructured Data Analysis** - Utilizes **Gemini 2.0 LLM** to extract insights from customer feedback and vendor notes.
✅ **Actionable Recommendations** - Generates strategies to **reduce waste** and **improve efficiency** in inventory management.
✅ **Interactive Visualization** - Provides dashboards via **Streamlit** and **Looker Studio** to visualize forecasts and insights.
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## 🏗️ Architecture
SmartStockAI integrates multiple **Google Cloud** services for a seamless, scalable solution:
- **BigQuery ML** - Implements the **ARIMA_PLUS** model for demand forecasting.
- **Gemini 2.0 LLM** - Processes unstructured data to generate insights.
- **Cloud SQL** - Stores structured relational data.
- **BigQuery** - Serves as the core analytics engine for large-scale data processing.
- **Streamlit** - Provides an interactive frontend for data visualization.
- **Looker Studio** - Offers collaborative dashboards for deeper analysis.
---
## 📌 Prerequisites
Before implementing SmartStockAI, ensure you have the following:
### 🔹 **Google Cloud Platform (GCP) Services**
- Cloud Storage
- Cloud SQL
- BigQuery
- BigQuery ML
- Looker Studio
### 🔹 **Machine Learning Models**
- ARIMA_PLUS Model (for demand forecasting)
### 🔹 **APIs & Tools**
- **Gemini 2.0 API** (for unstructured data analysis)
- **Streamlit** (for visualization)
### 🔹 **Required Knowledge**
- SQL Queries
- Machine Learning Concepts
- Python Programming
- Google Cloud Platform (GCP)
---
## ⚡ Getting Started
Follow these steps to set up and run SmartStockAI:
1️⃣ **Data Acquisition** - Obtain inventory data (e.g., the Nike Sales dataset from Kaggle).
2️⃣ **Data Upload** - Upload the dataset to **Google Cloud Storage**.
3️⃣ **BigQuery Integration** - Enable **BigQuery** and connect it to **Cloud SQL** for real-time data retrieval.
4️⃣ **Model Implementation** - Apply the **ARIMA_PLUS** model in **BigQuery ML** for demand forecasting.
5️⃣ **Unstructured Data Processing** - Integrate **Gemini 2.0 LLM** for analyzing customer feedback and vendor notes.
6️⃣ **Visualization** - Develop interactive dashboards using **Streamlit** and **Looker Studio**.
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## 📚 Resources
🔗 **[BigQuery ML ARIMA_PLUS Model](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview)**
🔗 **[Google Cloud Storage Documentation](https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv)**
🔗 **[Looker Studio Documentation](https://lookerstudio.google.com/)**
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## 🙌 Acknowledgments
A huge thanks to **Code Vipassana** for organizing the in-person event! 🎉
---
## 📜 License
This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.
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### 📩 Have Questions?
Feel free to **open an issue** or **reach out** via [LinkedIn/Twitter/GitHub Discussions]!
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⭐ **If you find this project useful, don't forget to give it a star!** ⭐