AI Control Tower for Real-Time ERP and SCM 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chain management (SCM) systems face challenges in dynamically managing data across silos, leading to inaccurate and outdated data, duplicate entries, and inefficiencies in real-time collaboration and data cleansing, which hampers their ability to adapt to changing conditions.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 recalibrating functions in real-time, enabling automatic identification and resolution of data changes and issues across multiple applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is extracted out of ERP system for repair and structuring, then data accuracy is improved, but processing time and cost increase significantly
Solution Approach 1:
The patent implements preliminary data validation and cleansing rules within the ERP system itself, rather than extracting data for repair later. Data quality checks are performed at the point of entry and during transactions, preventing corrupt data from propagating through the system. This eliminates the need for time-consuming external data extraction and repair processes.
Solution Approach 2:
The system employs automated data validation mechanisms that self-correct common data quality issues without human intervention. Built-in validation rules automatically detect and correct inconsistent data formats, duplicate entries, and formatting errors as they occur, eliminating the need for manual data extraction and restructuring operations.
2Reliability
If data validation rules are applied to all data, then data accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent applies different levels of validation intensity based on data characteristics and risk profiles. High-risk data fields such as financial transactions and inventory quantities undergo rigorous validation, while low-risk fields use lighter validation checks. This selective approach maintains data accuracy for critical fields while preserving overall processing speed.
Solution Approach 2:
The system performs essential validation checks on all data entries to maintain baseline quality, while applying more comprehensive validation only when anomalies are detected or for high-stakes transactions. This partial validation approach ensures data accuracy where needed without imposing excessive processing overhead on routine operations.
3Adaptability or versatility
If ERP system structure is modified to handle dynamic data, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent implements a flexible data validation framework where validation rules can be dynamically configured and modified without changing the core ERP system architecture. Business users can add, remove, or adjust validation rules through configuration interfaces, allowing the system to adapt to changing data requirements while maintaining structural simplicity.
Solution Approach 2:
The system introduces a layer of configurable validation rules that acts as an intermediary between the fixed ERP system structure and dynamic data requirements. This validation layer handles adaptability needs without requiring modifications to the underlying ERP architecture, thereby maintaining system simplicity while achieving flexibility.
Data Source
AI summary
The present invention provides self-driven Artificial Intelligence 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.