E-procurement Catalog Optimization via Transaction Data Weighting
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Solution Overview
Problem
E-procurement platforms face inefficiencies due to item saturation, where suppliers must balance offering a wide range of products to facilitate quick searches while avoiding unnecessary resource usage and ensuring highly specific items are readily available, leading to challenges in maintaining optimal digital catalogs.
Innovation Solution
A computer-implemented method that analyzes historical and expected e-marketplace data to determine the relative importance of items, generating recommendations for suppliers to optimize their digital catalogs by prioritizing items with higher weights based on transaction frequency and relevance, thereby reducing unnecessary resource usage and improving searchability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If suppliers saturate the e-marketplace with every item they offer, then item availability and search completeness improve, but server memory usage and buyer parsing time increase
Solution Approach 1:
The patent extracts only the most important items from the complete supplier inventory and places them in the digital catalog. This selective extraction resolves the contradiction by making frequently searched items immediately available (improving reliability) while excluding less important items from the catalog (reducing quantity of items that buyers must parse through and server memory requirements).
Solution Approach 2:
The patent applies different availability qualities to different items based on their importance. High-importance items are placed in the digital catalog with immediate searchability, while lower-importance items remain in the full inventory but are not prominently featured. This local differentiation resolves the contradiction by optimizing availability for critical items without universally increasing catalog size.
2Productivity
If suppliers restrict the number of items in digital catalogs, then search and query results are fulfilled more quickly, but highly specific items may not be available or noticeable
Solution Approach 1:
The patent uses feedback from transactional data and search logs to dynamically determine item importance weights. Items that are frequently searched or purchased receive higher weights and are more likely to be included in the catalog. This feedback mechanism ensures that the catalog contains exactly the items needed for quick search fulfillment while maintaining availability of specific items through continuous learning from buyer behavior.
Solution Approach 2:
The patent changes the parameter of item inclusion from a static binary decision to a dynamic weighted probability based on importance metrics. By adjusting the importance weight parameter for each item based on transactional data, the system optimizes catalog composition to achieve fast search fulfillment while preserving access to specific items through the weighted selection process.
3Reliability
If suppliers maintain comprehensive item listings, then item availability improves, but resource consumption for maintaining and processing items increases
Solution Approach 1:
The patent extracts only the essential subset of items needed for most procurement activities and places them in the optimized digital catalog. This extraction approach maintains reliability for common items while dramatically reducing the computational resources needed to maintain and process the full inventory, as the system only needs to actively manage the curated catalog items rather than all possible items.
Solution Approach 2:
The patent applies partial action by maintaining full item availability in the background system while only actively cataloging and optimizing the most important items. This partial approach ensures comprehensive availability when needed while reducing everyday resource consumption by focusing computational effort only on the critical subset of items that constitute the optimized catalog.
4Ease of operation
If suppliers use detailed catalog groupings, then item discoverability and related purchases improve, but catalog complexity and maintenance difficulty increase
Solution Approach 1:
The patent performs preliminary analysis of transactional data and search patterns to pre-determine item importance weights and optimal catalog groupings before catalog generation. This preliminary action resolves the contradiction by establishing the catalog structure in advance based on data-driven insights, making items easily discoverable through pre-organized groupings while avoiding the complexity of manual, iterative catalog maintenance.
Data Source
AI summary
A method and apparatus for generating recommendation data for cataloging items in an e-procurement system is provided. In various embodiments, a database of records is created and maintained corresponding to a plurality of transactions in an e-procurement system. In various embodiments, database records are weighted and sorted according a transaction method associated with the records. In various embodiments, recommendation data is generated for items associated with the records to suggest more efficient methods for offering items for procurement in an e-marketplace based on the weights and sort order of the records.