Supplier Risk Assessment System with Dynamic Thresholds
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Solution Overview
Problem
Existing risk assessment systems for suppliers are inefficient and impractical for organizations with numerous suppliers, as they require manual, tailored assessments that are not scalable or dynamic enough to account for changes in organizational goals and risk tolerances.
Innovation Solution
A computer-implemented method and system that generates an impact score, a likelihood score, and a combined risk score for suppliers, using predictive models and dynamic risk thresholds, to provide a scalable and dynamic risk assessment that can be automatically tailored to an organization's goals and risk tolerances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual risk assessments are performed for each supplier, then accuracy and tailoring to organizational goals are improved, but scalability and efficiency deteriorate
Solution Approach 1:
The patent replaces manual risk assessment processes with an automated machine learning system. The system uses predictive models that automatically evaluate supplier risk based on multiple data sources, eliminating the need for manual analysis while maintaining high accuracy through AI-driven risk scoring and classification.
Solution Approach 2:
The system dynamically adjusts risk assessment parameters based on organizational goals, risk tolerances, and supplier characteristics. The machine learning models continuously refine their risk scoring algorithms, adapting to changing organizational requirements without manual reconfiguration, thus maintaining both accuracy and scalability.
2Reliability
If manual, tailored risk assessments are conducted, then reliability and consistency are improved, but device complexity and resource requirements worsen
Solution Approach 1:
The patent implements a universal risk assessment platform that handles multiple risk types (cyber, operational, financial) through a single integrated machine learning system. The system provides consistent risk scoring across all supplier categories while adapting to specific organizational requirements, reducing overall system complexity compared to multiple separate assessment tools.
Solution Approach 2:
The machine learning system performs self-calibration and continuous improvement without human intervention. The models automatically learn from new data and organizational feedback, maintaining reliable and consistent risk assessments while reducing the need for complex manual configuration and monitoring processes.
3Adaptability or versatility
If dynamic risk thresholds are implemented, then adaptability to organizational changes is improved, but measurement precision and scoring accuracy worsen
Solution Approach 1:
The patent implements dynamic risk thresholds that automatically adjust based on organizational goals, risk tolerances, and external conditions. The machine learning models continuously update risk scoring algorithms in response to changing organizational requirements, maintaining both adaptability and measurement precision through data-driven parameter adjustment rather than static thresholds.
Solution Approach 2:
The system incorporates feedback mechanisms where organizational performance data and risk outcomes are continuously fed back into the machine learning models. This feedback loop enables the system to refine risk scoring accuracy while maintaining dynamic thresholds that adapt to changing organizational priorities, ensuring both adaptability and precision improve over time.
Data Source
AI summary
Systems and methods for assessing enterprise-level risk assessment of a supplier. The method includes receiving an indication of a supplier, generating an impact score for the supplier; generating a likelihood score for the supplier; generating a combined risk score based on the generated impact score and the generated likelihood score; evaluating the generated combined risk score relative to a dynamic risk threshold; and based on the evaluation, generating an output including the generated combined risk score.