Dynamic Peer Group Generation in E-Procurement Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electronic procurement systems face challenges in efficiently determining relevant data for forming peer groups among entities, as data from one entity may be irrelevant to another due to differences in fields, size, or geography.
Innovation Solution
The system dynamically generates and uses peer groups by creating an entity database with attributes and an ordered table of matching rules, allowing for the retrieval of relevant attributes and the formation of peer groups based on matching rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple entities is collected to form peer groups, then the quantity of available data increases, but the relevance and accuracy of peer group recommendations deteriorates due to irrelevant data from unrelated fields, sizes, or geographies
Solution Approach 1:
The patent segments the multi-tenant database into distinct entity groups using matching rules that divide entities based on attributes like industry, company size, and geography. This segmentation allows the system to collect data from multiple entities while maintaining relevance by only comparing entities within the same segment or peer group.
Solution Approach 2:
The patent applies local quality by making different parts of the database have different properties - each entity group has specific matching rules tailored to its characteristics. The system retrieves attributes selectively based on the particular entity's context, ensuring that data collection is both comprehensive and relevant to the specific entity's needs.
2Measurement precision
If matching rules are created to filter entities for peer groups, then the relevance of data improves, but the complexity of the system increases due to the ordered table of matching rules and attribute retrieval processes
Solution Approach 1:
The patent implements preliminary action by pre-defining matching rules and attribute priorities in an ordered table before peer group formation. This allows the system to quickly retrieve relevant attributes without complex real-time decision-making, reducing operational complexity while maintaining high relevance accuracy.
Solution Approach 2:
The patent uses parameter changes by allowing the matching rules to be dynamically adjusted based on entity attributes. The ordered table of matching rules can be configured with different priority levels and attribute weights, enabling flexible peer group formation without requiring complex algorithms for each comparison.
3Adaptability or versatility
If dynamic peer group generation is implemented, then the adaptability of the system improves, but the processing time increases due to the need to retrieve and compare multiple attributes
Solution Approach 1:
The patent reduces processing time by performing preliminary retrieval of entity attributes and establishing the ordered table of matching rules in advance. This preparation work is done once and reused for multiple peer group formations, making the dynamic generation process faster and more efficient.
Solution Approach 2:
The patent applies partial action by retrieving only the necessary attributes defined in the matching rules rather than all available entity data. This selective attribute retrieval reduces processing time while maintaining the adaptability needed for accurate peer group formation based on relevant characteristics.
Data Source
AI summary
In an embodiment, a method for dynamically generating and using peer groups in an e-procurement system includes creating an entity database with a plurality of attributes associated with entities and an ordered table of matching rules. Each matching rule having a priority value and two or more matching attributes. The method includes receiving a user input specifying a particular entity for generating a current peer group of other entities. The method includes accessing the entity database to retrieve particular attributes of the particular entity and querying the entity database to receive a result set of matching entities as the current peer group of other entities for the particular entity based on the particular attributes of the particular entity and the matching rules. The method further includes appending data for the matching entities in the result set to a peer group data structure associated with the particular entity.