Supply Chain Risk Weighting by Data Source Significance
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Solution Overview
Problem
Existing supply chain risk assessment systems fail to accurately account for the significance of different data sources and user preferences, leading to varying and potentially inaccurate risk judgments.
Innovation Solution
A supply chain management system that includes a data acquisition section, a target data selection section, and a risk assessment section, which assess risks based on the significance and user acceptance of data sources, expressed through weights, allowing for user feedback to adjust scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data sources are used for risk assessment, then the comprehensiveness of risk evaluation is improved, but the accuracy of risk judgment deteriorates due to varying significance and user acceptance of different data sources
Solution Approach 1:
The patent applies local quality by assigning different weights to different data sources based on their significance and user acceptance. Each data source receives a specific weight value that reflects its local importance, allowing the system to emphasize certain data sources over others. This resolves the contradiction by maintaining comprehensiveness (using multiple data sources) while improving accuracy (through differentiated weighting).
Solution Approach 2:
The patent changes the parameter of data source significance by introducing weight values that can be adjusted based on user feedback. The system dynamically modifies the weight parameters of different data sources, transforming the risk assessment from a uniform treatment of all data sources to a parameterized approach where each source's contribution can be precisely controlled.
2Measurement precision
If data sources are selected based on significance and user acceptance, then the accuracy of risk assessment is improved, but the system complexity increases due to weight definition and management
Solution Approach 1:
The patent applies self-service by enabling users to directly provide feedback on risk assessment results, which the system then uses to automatically adjust data source weights. Users serve their own need for customized risk assessment by providing feedback, and the system automatically manages the complexity of weight adjustment without requiring manual configuration. This resolves the contradiction by improving accuracy through user-specific weighting while managing system complexity through automated feedback processing.
Solution Approach 2:
The patent implements feedback mechanisms where user assessments of risk results are collected and used to refine the weighting of data sources. The system continuously cycles through assessment and feedback, automatically adjusting weights based on user responses. This resolves the contradiction by improving accuracy through iterative refinement while managing complexity through automated feedback loops rather than manual system configuration.
3Ease of operation
If uniform weight is assigned to all data sources, then the system simplicity is maintained, but the user-specific relevance of risk assessment deteriorates
Solution Approach 1:
The patent applies dynamics by making the weight assignment adaptive rather than static. The system starts with uniform weights for simplicity but dynamically adjusts weights based on user feedback and acceptance. This allows the system to maintain simplicity when user-specific customization is not needed, while automatically becoming more adaptive when user feedback is provided, thus resolving the contradiction between simplicity and user-specific relevance.
Data Source
AI summary
Provided are a supply chain management apparatus, a supply chain management method, and a supply chain management system that make it possible to more accurately assess risks to a supply chain by assessing the risks to the supply chain according to data sources that are more significant for each user. The supply chain management apparatus includes a data read section and a priority data extraction section. The data read section acquires data sources as data related to risks that affect a supply chain. The priority data extraction section selects target data for assessing the risks from the data sources according to significance of the data sources and user's acceptance of the data sources and assesses the risks to the supply chain according to the target data.