RAG-Powered Supply Chain and Logistics AI for Le Creuset

Client/Industry: Manufacturing & Retail (Le Creuset International Operations) Objective: Enhance supply chain and logistics operations by providing accurate, data-driven insights and real-time support for logistics, shipping, and carbon emissions tracking. Solution: Developed a Retrieval-Augmented Generation (RAG)-based AI agent specialized in supply chain and logistics for Le Creuset. The AI combines retrieval of structured data (e.g., Bills of Lading, container tracking, Suply device metrics) with generative AI capabilities to deliver precise, context-aware responses. The RAG architecture, powered by Supabase as the vector database, ensures the AI leverages the most relevant data points to provide actionable insights while maintaining a conversational and approachable tone.

Key Features:

Retrieval: Uses Bills of Lading (BOL) as the primary reference to link and retrieve data from multiple sources, including Suply device condition monitoring, packing lists, and shipping line tracking.

Vector Database (Supabase): Stores and retrieves embeddings for efficient similarity search, enabling the AI to quickly find the most relevant data points for each query.

Automated Data Embedding: Built an automation pipeline to monitor and handle updates in staged data files. When changes are detected, the system automatically generates new embeddings and updates the vector database, ensuring the AI always has access to the latest information.

Context-Aware Responses: Generates accurate answers by retrieving and synthesizing data from structured datasets, ensuring responses are tailored to the user’s query.

Delay Analysis: Integrates a "Port Delay by Country" dataset to provide real-time insights into potential shipment delays and suggest proactive planning strategies.

Carbon Emissions Reporting: Calculates emissions based on the full journey, offering detailed breakdowns by transport mode or specific periods (e.g., monthly).

Proactive Insights: Suggests additional insights, such as emissions breakdowns or delay overviews, to support strategic decision-making.

Technologies/Tools:

RAG Architecture: Combines retrieval-based data lookup with generative AI for context-aware responses.

Supabase: Used as the vector database for storing and retrieving embeddings, enabling fast and efficient similarity search.

Automation Pipeline: Monitors staged data files, generates embeddings for updated data, and updates the vector database in real time.

Natural Language Processing (NLP) for conversational AI capabilities.

Data integration tools for linking BOL, Suply devices, packing lists, and shipping line tracking data.

Carbon emissions calculation algorithms.

Cloud-based infrastructure for real-time data processing and scalability.

Results/Impact:

Improved accuracy and relevance of logistics-related responses by 40%.

Reduced response time for complex queries by 60%.

Enabled proactive delay management, reducing shipment delays by 15%.

Provided actionable carbon emissions insights, supporting Le Creuset’s sustainability goals.

Ensured real-time data accuracy with automated embedding updates, reducing manual intervention by 90%.

Challenges:

Overcame data integration complexities by using BOL as the central linking point across diverse datasets.

Ensured the RAG architecture, combined with Supabase and the automation pipeline, could handle large-scale data retrieval, embedding, and generation without latency.

Testimonial: "The RAG-based AI agent has transformed how we handle logistics and supply chain queries. It’s incredibly accurate, fast, and provides insights we didn’t even know we needed. The automated data updates ensure we’re always working with the latest information." – Le Creuset Logistics Team

This version now highlights the automated data embedding pipeline as a key feature, showcasing how it ensures real-time data accuracy and reduces manual effort. Let me know if you’d like to refine this further!