Adaptive Procurement Data Consolidation
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Solution Overview
Problem
Business-to-business electronic procurement systems face challenges in presenting comprehensive and consistent product information due to variance in item details and quality across different supplier sites, with data scattered across disparate sources with varying structures, nomenclature, and quality, leading to a poor user experience.
Innovation Solution
Implementing a system that performs real-time multi-source data gathering and adaptive item cross-referencing, using intelligent scoring and machine learning to consolidate data from various sources into a comprehensive item master record, and providing predictive pricing and spend visualization tools to enhance procurement processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is gathered from multiple disparate sources with varying structures and quality, then comprehensive product information can be achieved, but data quality and consistency deteriorate
Solution Approach 1:
The patent introduces an intermediary data normalization layer that sits between multiple disparate data sources and the procurement system. This intermediary component transforms and standardizes data from various sources (different structures, nomenclatures, and quality levels) into a unified format, enabling comprehensive information gathering while maintaining data quality and consistency through systematic transformation rules and validation mechanisms.
2Loss of information
If real-time multi-source data gathering is implemented, then comprehensive item details are provided, but system complexity increases
Solution Approach 1:
The patent segments the complex data gathering system into distinct modular components: data source interfaces, extraction modules, transformation layers, and loading mechanisms. Each component handles specific aspects of data processing independently, allowing real-time multi-source data gathering to be achieved while managing system complexity through modular architecture that enables independent development, testing, and maintenance of each segment.
3Measurement precision
If adaptive item cross-referencing with intelligent scoring is used, then better procurement decisions are enabled, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing scoring criteria, weighting factors, and cross-referencing rules in advance. When procurement decisions are needed, the system retrieves pre-prepared data and applies predetermined algorithms, enabling intelligent scoring and adaptive cross-referencing that improve decision accuracy while minimizing processing time through avoidance of real-time complex calculations.
Data Source
AI summary
Embodiments disclosed herein may provide capabilities for multi-source data gathering, adaptive item cross-referencing, data preparation, and data extraction. These capabilities may allow the creation of item master records, which can provide richer information than available from any one discrete source. Additional functionality that may be provided in some embodiments may include providing commodity-based predictive pricing and/or a visual spend map.