AI Query Language Control Tower for ERP Data Recalibration
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Solution Overview
Problem
Existing enterprise resource planning (ERP) and supply chain management (SCM) systems face challenges in dynamically managing data across silos, leading to inaccurate and outdated data, duplicate entries, and inefficiencies in real-time collaboration and data cleansing, which hampers their ability to adapt to changing conditions.
Innovation Solution
A self-driven system that utilizes a data lake to receive and process data from diverse sources, auto-selects data models, and employs AI-based processing logic to generate scripts for real-time recalibration of functions, enabling automatic identification and resolution of data changes and issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is stored in silos in existing ERP systems, then data storage is simplified, but real-time collaboration and data accuracy deteriorate
Solution Approach 1:
The patent merges previously siloed data storage into a unified data structure where master data is centrally stored and accessible by multiple modules. This consolidation eliminates data silos while maintaining system organization, enabling real-time collaboration across supply chain partners and ensuring data accuracy through single source of truth.
Solution Approach 2:
The unified data structure serves multiple functions simultaneously: it stores master data, enables real-time collaboration, supports impact analysis, and facilitates automated script generation. This multi-functional approach replaces multiple separate data storage systems while improving overall system reliability.
2Reliability
If data is extracted and restructured outside ERP systems, then data accuracy improves temporarily, but processing time and costs increase
Solution Approach 1:
The system performs preliminary data validation and impact analysis before data changes are committed. By pre-identifying potential issues and automatically generating correction scripts, the system prevents data accuracy problems rather than requiring post-processing extraction and restructure operations.
Solution Approach 2:
The ERP system automatically detects data quality issues, analyzes their impact, and generates correction scripts without external intervention. This self-service capability eliminates the need for time-consuming manual data extraction and restructure operations while maintaining continuous data accuracy.
3Adaptability or versatility
If ERP systems are modified structurally to handle dynamic data, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent implements dynamic data models that automatically adapt to changing data requirements without structural ERP modifications. The system dynamically identifies data changes, analyzes their impact, and adjusts data relationships in real-time, providing adaptability while maintaining the stability of the core ERP architecture.
Solution Approach 2:
The unified data structure acts as an intermediary layer between the stable ERP core and dynamic data requirements. This mediator enables flexible data handling and real-time collaboration without requiring modifications to the underlying ERP system structure, thus avoiding increased system complexity.
4Reliability
If manual data repair and structuring is performed, then data quality improves, but labor and time consumption increase
Solution Approach 1:
The system automatically detects data quality issues, determines their impact on supply chain operations, and generates correction scripts without manual intervention. This automated self-service approach continuously maintains data quality while eliminating the labor and time consumption associated with manual data repair operations.
Solution Approach 2:
The system implements continuous feedback loops where data changes are automatically monitored, validated, and corrected. This real-time feedback mechanism ensures sustained data quality improvement without requiring repeated manual repair cycles, thereby maintaining high productivity.
Data Source
AI summary
The present invention provides self-driven AI based system and method for operating one or more applications including enterprise application and supply chain management applications. The system includes centralized data lake for storing data received from plurality of distinct sources, a control tower configured for sensing change in attribute of the received data and determining impact of the change on plurality of functions of EA and SCM applications.