Automated Expert Selection for Proposal Assembly
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Solution Overview
Problem
The conventional method of assembling proposals in response to requests for proposal (RFPs) is inefficient due to subjective selection of experts, reliance on manual processes, and limited access to recent expert experiences, leading to suboptimal team composition and increased time consumption.
Innovation Solution
A computer program or processor-based system that utilizes databases to identify and select experts based on key topics, time availability, and supervisor approval, integrating with existing systems like email, calendar, and document management to automate the expert selection and proposal assembly process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert selection by proposal manager is used, then flexibility and human judgment are maintained, but selection subjectivity and time consumption increase
Solution Approach 1:
An automated expert selection system acts as an intermediary between the proposal manager and the expert database. The system objectively matches experts to proposal topics using database queries and algorithms, eliminating subjective bias while maintaining human oversight for final approval. This intermediary process simultaneously improves selection accuracy and reduces time consumption by handling the initial filtering and matching automatically.
2Measurement precision
If comprehensive expert database is implemented, then expert matching quality improves, but system complexity increases
Solution Approach 1:
The expert selection system is segmented into distinct functional modules: a database management module for storing expert profiles and topics, a query processing module for receiving proposal topics, a matching algorithm module for comparing topics with expert expertise, and an output module for generating expert recommendations. This segmentation allows each component to be developed, maintained, and updated independently, reducing overall system complexity while maintaining high matching accuracy.
3Productivity
If automated expert selection system is deployed, then selection objectivity and speed improve, but reliance on historical data increases
Solution Approach 1:
The system performs preliminary actions by continuously updating the expert database with current expert information, recent projects, and new expertise areas before proposal assembly is needed. Experts are pre-tagged with their skill sets, and the database is pre-structured with topic classifications. When a proposal arrives, the system can immediately query this pre-prepared data structure, ensuring both speed and access to recent experience without relying solely on historical data.
Data Source
AI summary
Technology may be used to modify and improve the process of assembling a response. An automated proposal may be generated by storing databases with information regarding experts and their availability. The database may be generated by monitoring other server systems within a network, such as email and instant messaging systems. A method may include identifying topics of interest in a request for proposal; identifying a credible expert based, at least in part, on the identified key topics; accessing time availability of the credible expert; requesting approval from a supervisor for participation by the credible expert, wherein the approval request includes the time availability of the credible expert; and assembling a response to the request for proposal that identifies the credible expert when approval from the supervisor is received.