AI Data Lake Control Tower for ERP and SCM 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 different modules, leading to issues such as data silos, accuracy and storage problems, and the inability to handle real-time changes, which results in inefficiencies and increased costs due to manual data repair and structural limitations.
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 improvements across ERP and SCM applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data repair and restructuring is performed on corrupt data in ERP systems, then data accuracy is temporarily improved, but the process becomes extremely labor, time and money consuming as data volume increases
Solution Approach 1:
The system implements self-service through automated data quality agents that continuously monitor, detect, and repair corrupt data without human intervention. The agents use machine learning models to identify data anomalies and automatically execute repair procedures, enabling the system to maintain itself autonomously as data volume grows.
Solution Approach 2:
The system performs preliminary action by proactively detecting and repairing corrupt data before it propagates through the ERP system. Data quality agents continuously scan incoming data streams and correct issues in real-time, preventing corrupt data from affecting downstream processes and eliminating the need for reactive manual repairs.
2Measurement precision
If data is extracted from ERP system for repair and structuring, then data quality issues can be addressed, but data gets dirty again within a few days and the problem recurs
Solution Approach 1:
The system implements continuous feedback loops where data quality agents monitor data quality metrics in real-time and automatically trigger repair procedures when degradation is detected. This closed-loop approach ensures data quality is maintained continuously rather than temporarily, as the system responds dynamically to quality changes.
Solution Approach 2:
The system ensures continuity of useful action through 24/7 automated data quality monitoring and repair operations. Data quality agents run continuously without interruption, constantly detecting and repairing corrupt data, which eliminates the recurring nature of the problem and maintains sustained data quality improvement.
3Device complexity
If traditional ERP systems with siloed data storage are used, then system structure is simple, but real-time collaboration among supply chain players is prevented and data accuracy deteriorates quickly
Solution Approach 1:
The system merges previously siloed data storage into a unified data lake architecture that consolidates data from multiple sources including ERP systems, supply chain partners, and external sources. This unified structure enables real-time data sharing and collaboration while maintaining data accuracy through centralized quality control mechanisms.
Solution Approach 2:
The system introduces data quality agents as intermediary components that mediate between diverse data sources and the ERP system. These agents cleanse, validate, and standardize data before it enters the ERP system, ensuring data accuracy is maintained regardless of the complexity of data sources.
4Device complexity
If existing ERP systems are used without automated repair mechanisms, then system complexity is low, but identifying and repairing corrupt data becomes extremely difficult as data volume increases
Solution Approach 1:
The system replaces manual mechanical processes of data inspection and repair with automated electronic systems using machine learning algorithms. Data quality agents use AI models to automatically detect patterns of corrupt data, identify anomalies, and execute repairs, making the process scalable to large data volumes without increasing complexity.
Solution Approach 2:
The system implements self-service through automated data quality agents that autonomously monitor, detect, and repair corrupt data without human intervention. The agents use embedded machine learning models to identify data issues and execute repair procedures automatically, eliminating the difficulty of manual detection and repair.
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.