AI Traceability Mapping for Upstream Supplier Risk Gaps
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Solution Overview
Problem
Companies face challenges in identifying and assessing risks associated with suppliers further upstream in their supply chain, beyond their direct suppliers, particularly during disasters or malfunctions, due to limited traceability information.
Innovation Solution
A traceability information creation support system utilizing a processor and storage device to learn relationships between articles and their suppliers, estimate missing information, and update models based on user confirmation to create comprehensive supplier risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a company directly purchases parts from Tier 1 suppliers, then information on direct suppliers can be acquired, but information on upstream suppliers (Tier 2 and beyond) cannot be obtained
Solution Approach 1:
The patent introduces an AI model as an intermediary that mediates between the company and the complex multi-tier supply chain. The model learns relationships between articles and suppliers from available data, then estimates missing upstream supplier information without requiring direct access to Tier 2+ suppliers. This resolves the contradiction by using an intelligent intermediary to bridge the information gap across complex supply chain structures.
Solution Approach 2:
The patent replaces the mechanical/manual approach of directly querying each supplier in the supply chain with an AI-based estimation system. Instead of physically or directly communicating with multiple tiers of suppliers to gather traceability information, the system uses machine learning models to infer missing information from partial data, substituting the mechanical information gathering process with intelligent estimation.
2Loss of information
If AI estimation is used to fill missing traceability information, then comprehensive supplier information can be obtained, but verification and accuracy control become more difficult
Solution Approach 1:
The patent implements a feedback mechanism where the AI estimation results are presented to users for verification. Users can confirm or correct the estimated upstream supplier information, and this feedback is used to refine and update the AI model. This closed-loop feedback system maintains measurement precision by allowing human verification while still achieving comprehensive information coverage through AI estimation.
Solution Approach 2:
The patent applies partial action by having users verify only the AI-estimated portions of traceability information rather than requiring verification of all information. The system performs excessive action by providing more comprehensive supplier information than would be available through direct purchasing alone, then uses selective user verification to maintain accuracy without requiring complete manual validation of all data.
Data Source
AI summary
Provided is a traceability information creation support system, comprising a processor and a storage device, wherein the storage device holds an article and a model that has learned a relationship between an article and an article forming the article and a supplier thereof, and wherein the processor is configured to receive an input of traceability information indicating a relationship between an article and at least one article forming the article and a supplier thereof, estimate, by using the model, one or more articles and suppliers thereof that are lacking in the input traceability information, output the estimated one or more articles and suppliers thereof for confirmation by a user, store one or more articles and suppliers thereof confirmed by the user in the storage device as determined traceability information, and update the model by learning the determined traceability information.