Revolutionizing Product Data Management with GenAI
Managing SKUs, the critical identifiers that track every product detail, is the backbone of effective supply chain operations. However, legacy systems often struggle to accurately capture and process the vast volume and variety of SKU data, leading to errors, delays, and costly inefficiencies. Automating SKU extraction not only accelerates data handling but also ensures consistency and accuracy across the entire product lifecycle.
The Challenge: Unstructured Data Slows Down Supply Chain Velocity
In today’s fast-paced supply chain landscape, a core necessity is a unified and instantly accessible view of all Stock Keeping Units (SKUs). Our Client faced severe hurdles:
- Unstructured Data Chaos: Product data was trapped in diverse, unstructured supplier brochures and documents (PDFs, images, etc.).
- Slow Manual Extraction: The extraction of crucial SKU details was a slow, labor-intensive manual process, consuming hours of team time.
- Fragmented View: The lack of a unified, automated extraction process prevented a consolidated view of SKUs, hindering quick decision-making and inventory management.
This challenge was a significant drag on operational efficiency, increasing the risk of errors and dramatically slowing down the time-to-market for new products
The GenAI Solution: Intelligent, On-Demand SKU Data Retrieval
To resolve these legacy barriers and achieve rapid data access, the firm developed and implemented the SKU Extractor, a cutting-edge GenAI-powered solution
The solution architecture hinged on leveraging Azure Open AI’s Large Language Models (LLMs):
GenAI Solution Architecture
- Unified Data Repository
All unstructured SKU data documents, regardless of format, were ingested into the system, creating a centralized, searchable data corpus. - GenAI-Powered Chatbot Interface
A web application hosting an intelligent chatbot was built. The chatbot serves as the primary interface for users. - Intelligent Query Mapping and Extraction
The core innovation lies in the LLM’s capability:- Interpretation: The chatbot uses the LLM to interpret complex user queries and natural language requests.
- Mapping: It intelligently maps these queries to the unstructured content within the documents.
- Extraction: The LLM then extracts precise, contextualized SKU information from any document format, delivering accurate, on-demand answers.
This approach effectively transformed unstructured data into an immediately query table knowledge base, bypassing the need for manual review.
Real Business Impact: Achieving Speed, Accuracy, and Scalability
This GenAI-driven solution created a unified, high-integrity, and instantly accessible data foundation for the supply chain firm.
The transformative results demonstrated clear business value:
| Metric | Before GenAI Solution | After GenAI Solution | Impact |
| Data Retrieval Time | Hours (Manual Extraction) | Seconds (Chatbot Query) | Dramatic acceleration |
| Manual Errors | High | Minimized | Superior data accuracy |
| Decision-Making | Slow and Delayed | Accelerated | Enabled faster response to market changes |
| Scalability | Limited by Manpower | High (AI-Driven) | Sustainable, AI-driven competitive edge |
The firm achieved a scalable, AI-driven advantage in managing crucial product data across the supply chain, moving from slow, error-prone manual work to instant, accurate data retrieval.
Looking Ahead: From Automation to Intelligent Transformation
This case demonstrates how GenAI and LLMs can redefine the way organizations approach legacy data challenges. By combining contextual understanding, intelligent query mapping, and automated extraction, the firm not only solved an immediate pain point but also established a future-ready digital core. This foundation is poised to evolve, leading to fully intelligent supply chain ecosystems capable of self-correcting and providing deep, contextual insights with minimal human intervention.
