E-procurement System Recommending Sourcing Events via Implicit Observation
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Solution Overview
Problem
Current e-procurement systems face inefficiencies due to excessive use of computer processing resources and network bandwidth from repeated searches for sourcing events and the creation of redundant events, leading to labor-intensive processes and wasteful resource utilization.
Innovation Solution
The system automatically identifies relevant community sourcing events using implicit observation data, such as context and historical data, to recommend applicable events to buyer accounts without explicit requests, reducing the need for repeated searches and redundant event creation through machine learning algorithms and cross-referencing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If participant computers perform repeated searches to find sourcing events, then they can locate relevant events, but excessive computer processing resources and network bandwidth are consumed
Solution Approach 1:
The system performs preliminary actions by proactively identifying and recommending sourcing events to participant computers before they need to search. The server computer analyzes spending data and automatically generates recommendations, eliminating the need for participant computers to perform repeated searches and reducing their processing resource consumption.
Solution Approach 2:
The server computer acts as an intermediary between sourcing events and participant computers. It receives spending data from multiple buyer accounts, processes this information centrally, and generates recommendations that are then provided to participant computers. This intermediary role consolidates the search and matching functionality, reducing the processing burden on individual participant computers.
2Productivity
If buyer computers create their own sourcing events when none are found, then they can source required goods or services, but redundant sourcing events are created consuming excessive resources
Solution Approach 1:
The server computer performs preliminary analysis of spending data across multiple buyer accounts before sourcing events are created. By proactively identifying sourcing opportunities and recommending existing events, the system prevents buyer computers from creating redundant sourcing events, thus avoiding waste of computing resources.
Solution Approach 2:
The system merges spending data from multiple buyer accounts to identify common sourcing opportunities. By consolidating this information centrally, the server can recommend a single shared sourcing event to multiple participants, preventing the creation of multiple separate sourcing events for the same goods or services and reducing overall resource consumption.
3Ease of operation
If third-party teams run sourcing events and negotiate on behalf of participants, then participants lack visibility and cannot negotiate for themselves, but fees are incurred resulting in inefficient sourcing solutions
Solution Approach 1:
The system provides feedback to participant computers by recommending sourcing events based on their spending data and characteristics. This feedback mechanism enables participants to make informed decisions about which events to join, improving their ability to source efficiently without requiring third-party negotiation teams.
Solution Approach 2:
The system enables participant computers to self-serve by providing them with automated recommendations for relevant sourcing events. Participants can independently identify and join appropriate sourcing events based on the recommendations, reducing their reliance on third-party teams and eliminating associated fees while maintaining sourcing effectiveness.
Data Source
AI summary
A computer-implemented method for improving efficiency in an electronic procurement system for sourcing resources, comprising, during digital electronic interactions of a buyer computer with one or more software platforms and without receiving explicit request for recommendations from the buyer computer: automatically generating, at a coding computer, implicit observation data of the buyer computer; automatically determining, at the coding computer, one or more active sourcing events from a plurality of sourcing events, based on at least the implicit observation data of the buyer computer; using the coding computer, causing to display at least one of the one or more active sourcing events in a graphical user interface.