Enhancing Data Analysis Efficiency at the Largest Steel Manufacturing Company

Business Problem

The 2nd Largest Steel Manufacturer, a global leader in the steel and mining industry, faced significant challenges in extracting valuable insights from live vessel data and oxide reports.
The existing data analysis processes were time-consuming and required substantial manual effort, limiting the ability to make quick, informed decisions.
There was a pressing need to streamline data analysis, enhance operational efficiency, and drive sustainable growth through actionable insights and recommendations.

Solution

GoML, a leading expert in Generative AI and Natural Language Processing (NLP), implemented a comprehensive solution to address the company’s challenges. This solution included:

1.

Using NLP to analyze oxide reports and extract meaningful insights reduces manual efforts and improves analysis accuracy. This involved implementing a robust Natural Language Processing (NLP) Engine.

3.

Generating insights and recommendations to optimize operations, helping AMNS make quicker and more informed decisions. A comprehensive Data Analytics Platform facilitated this.

2.

Implementing an NLP engine capable of responding to English-based queries on the data will allow users to interact more intuitively with it.

4.

Developing a conversational API endpoint to integrate with RIWA, AMNS’s existing chat engine, ensuring seamless communication and data access through a Conversational API.

Architecture

Data Sources: Live Vessel Data, Oxide Reports
Data Ingestion Layer: Data Pipeline facilitates the extraction, transformation, and loading (ETL) of live vessel data and oxide reports into a centralized data repository.
Centralized Data Warehouse: Stores all the processed data from the data sources for efficient querying and analysis
Natural Language Processing (NLP) Engine: NLP models trained to understand and process complex technical language specific to the steel and mining industry are utilized to analyze oxide reports and enable English-based data queries.
Data Analytics Platform: The analytics engine processes data from the warehouse using machine learning, generating actionable insights and optimization recommendations.
Conversational API: An API endpoint integrates with RIWA, enabling users to query the data repository in natural language and receive real-time responses.
User Interface: The RIWA Chat Engine is the front-end interface through which users interact with the NLP engine and data analytics platform.

Outcomes