Automated Material Master Data Harmonization System
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Solution Overview
Problem
In IT environments, disparate systems and incompatible data formats lead to siloed master data, resulting in data redundancies, inaccuracies, and difficulties in integrating business processes, necessitating a system for harmonizing and enriching material master data to improve data quality and productivity.
Innovation Solution
An automated material master data harmonization system comprising attribute extraction, quality check, and non-source enrichment sub-systems, which standardizes, normalizes, and enriches data using embedded knowledge, leveraging enterprise assets and internet data sources to classify and extract manufacturer information and attribute values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data harmonization processes are used across disparate systems, then data accuracy can be maintained through human review, but productivity is severely reduced due to the time-consuming nature of manual cleansing and enrichment
Solution Approach 1:
The system enables automated self-service data harmonization where the computer automatically performs cleansing, enrichment, and standardization of master data without requiring manual human intervention. The system extracts attributes, validates data quality, enriches with external sources, and harmonizes data across systems autonomously, resolving the contradiction by replacing manual processes with automated intelligence while maintaining accuracy through embedded validation rules.
Solution Approach 2:
The patent replaces the mechanical manual process of data harmonization with an automated computer-based system that uses algorithms, machine learning models, and external data source integrations to perform cleansing, enrichment, and standardization tasks, thereby dramatically improving productivity while maintaining data accuracy through systematic automated validation.
2Loss of information
If comprehensive data enrichment from multiple external sources is performed, then data completeness is improved, but system complexity increases due to integrating multiple data sources and processing pathways
Solution Approach 1:
The system segments the complex data enrichment process into distinct modular sub-systems: attribute extraction module, data quality validation module, enrichment module, and harmonization module. Each module handles specific tasks independently, allowing comprehensive data enrichment from multiple external sources while managing complexity through modular architecture that can be configured and maintained separately.
Solution Approach 2:
The system implements a universal harmonization platform that can integrate multiple types of external data sources (manufacturer data, internet sources, internal systems) through a common architecture. The system performs multiple functions including extraction, validation, enrichment, and harmonization within a single unified platform, reducing overall system complexity compared to separate systems for each function.
3Productivity
If automated attribute extraction is performed across all items, then productivity is improved through automation, but measurement precision may deteriorate due to automated errors in extracting attribute values
Solution Approach 1:
The system implements feedback mechanisms where extracted attributes are validated against data quality rules, cross-checked with external data sources, and reviewed for consistency. The validation module provides feedback on extraction quality, allowing the system to identify and correct automated extraction errors while maintaining high productivity through continued automation of the extraction process itself.
Solution Approach 2:
The system performs preliminary data quality validation and cross-checking with external sources before finalizing extracted attributes. By preparing and validating data in advance through multiple verification steps, the system ensures high extraction accuracy while maintaining automated productivity, resolving the contradiction between automation speed and extraction precision.
Data Source
AI summary
A system for automated material master data harmonization that is extremely configurable and easy-to-use solution to standardize, normalize, attribute, rationalize and enrich the organization's material master data using embedded knowledge that leverages enterprise knowledge assets. The system provides various customer centric systems and processes by providing harmonization of data with dependencies of important embodiments such as data classification and MFR-MPN extraction that are not dependent on any other stage. Attribute extraction is dependent on data classification and data sheet definition. Post processing is dependent on data classification, data sheet definition and attributes extraction. Identify L2 dups is dependent on data classification, data sheet definition, attribute extraction and post processing. Non-Source enrichment and Identify L1 dups are dependent on MFR-MPN extraction.