Bosch India – AI & Blockchain Enabled Spare Part Traceability

An academic prototype for product-level spare-part authenticity verification inspired by Bosch India's digital transformation and aftermarket traceability challenges.

Project Overview

The application verifies an automotive spare part using:

• Product ID
• Batch ID
• Component type
• Supplier ID
• Inspection score
• Packaging integrity
• Serialization status

The system classifies a product into:

1. Counterfeit – packaging integrity or serialization fails.
2. Genuine - High Risk – authenticity checks pass, but inspection score is below 75.
3. Genuine - Low Risk – authenticity checks pass and inspection score is 75 or higher.

A SHA-256 hash is generated as a blockchain-inspired verification proof. This is a simulation/prototype rather than a full distributed blockchain network.

Tech Stack

• Python
• Streamlit
• Pandas
• SHA-256 / Python hashlib
• Jupyter Notebook

Repository Structure

Bosch-AI-Blockchain-Traceability/
├── app.py
├── product_verification.ipynb
├── requirements.txt
├── .gitignore
├── data/
│   └── product_quality_data.csv
└── docs/
    └── DT_Team-6_Project_Report.pdf

Run Locally

pip install -r requirements.txt
streamlit run app.py

The application will open in your browser.

Example Test Products

The project report demonstrates three scenarios:

• P310-10 → Counterfeit
• P302-02 → Genuine - High Risk
• P301-01 → Genuine - Low Risk

Academic Context

This project was developed as a digital transformation and supply-chain traceability case study focused on Bosch India, with a proposed AI and blockchain approach for aftermarket integrity and counterfeit-product detection.

Note: The dataset is synthetic and industry-aligned for academic/prototype purposes. The blockchain component generates a cryptographic verification hash; it does not implement a production distributed ledger.