Supplier Risk Quantification via Segmented Analytics
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Solution Overview
Problem
Quantifying risks associated with suppliers and geographic locations has been challenging due to the difficulty in processing and analyzing data in a timely manner, which hinders organizations' ability to prepare for and respond to disruptions in the supply chain.
Innovation Solution
A computer-based system and method that converts data from disparate sources into quantified risk metrics using analytics and algorithms to assess the impact of specific events or parameters on risks, allowing for the calculation of composite risk scores and providing guidance for mitigating risks based on these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from disparate sources is collected and analyzed to quantify supplier and location risks, then measurement precision of risk assessment is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the risk assessment system into multiple independent modules: data collection module, data processing module, risk calculation module, and reporting module. Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive risk assessment capabilities across multiple dimensions (financial, operational, geopolitical, etc.).
Solution Approach 2:
The patent introduces intermediary components including standardized data processing algorithms, risk scoring models, and aggregation mechanisms that mediate between raw disparate data sources and final risk metrics. These intermediaries transform heterogeneous data into standardized risk scores, simplifying the integration of multiple data sources without requiring complex custom processing for each source.
2Reliability
If comprehensive data from multiple sources is processed to assess risks, then reliability of risk assessment is improved, but loss of time in processing and analyzing data increases
Solution Approach 1:
The patent implements preliminary action through pre-established risk calculation models, predefined data processing rules, and pre-configured assessment frameworks. These are set up in advance to handle specific risk scenarios, enabling rapid processing of incoming data without requiring complex real-time analysis decisions, thus reducing processing time while maintaining comprehensive assessment reliability.
Solution Approach 2:
The patent applies parameter changes by transforming various data parameters into standardized risk score parameters through mathematical models and algorithms. Different data sources with varying formats and units are converted into uniform risk parameters that can be aggregated and compared, enabling fast processing while preserving the reliability of comprehensive risk assessment across diverse data types.
3Measurement precision
If detailed risk metrics are calculated for multiple suppliers and locations, then measurement precision is improved, but difficulty of detecting and measuring risks increases
Solution Approach 1:
The patent implements feedback mechanisms where risk metrics are continuously calculated, monitored, and compared against thresholds. The system provides feedback loops that automatically update risk assessments when new data is received or when risk parameters change, making it easier to detect and measure risks through automated monitoring rather than manual analysis of multiple precise metrics.
Solution Approach 2:
The patent uses copying by creating standardized risk profile templates and metric frameworks that can be replicated across multiple suppliers and locations. Instead of developing unique measurement approaches for each entity, the system copies and adapts standardized templates, reducing the difficulty of detecting and measuring risks while maintaining precise measurement through consistent application of the same rigorous metrics across all assessed entities.
Data Source
AI summary
Computer-implemented systems and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located. The systems and methods transform risk parameter data into risk metrics that allow comparison of relative risk between suppliers, supplier sites, or geographic locations, and allow comparison of risk metrics to minimum risk scores calculated for a given metric. The systems and methods further provide guidance/proposed action to take based on the generated risk metrics.