Supply Chain Resiliency Feedback Loop for Climate-Driven Policy Updates
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Solution Overview
Problem
Conventional supply chain optimization models lack a feedback loop, leading to inadequate spatio-temporal resiliency and ineffective resiliency policy generation due to errors in climate forecasts and AI/ML models, which are not addressed by existing systems.
Innovation Solution
Implementing a spatio-temporal feedback loop for supply chain resiliency by dynamically generating resiliency policies, embedding them in a resiliency reasoning graph, and updating AI/ML models based on user feedback to enable continuous improvement and joint optimization across supply chain stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supply chain optimization models are used without feedback loops, then device complexity is reduced, but supply chain resiliency and measurement precision deteriorate due to unaddressed errors in climate forecasts and AI/ML models
Solution Approach 1:
The patent implements a feedback loop that captures user interactions with the resiliency reasoning graph and uses this feedback to update AI/ML models and regenerate resiliency policies. This closed-loop system continuously improves model accuracy and policy effectiveness by learning from actual user decisions and outcomes, directly addressing the reliability deterioration caused by conventional open-loop models.
Solution Approach 2:
The system performs preliminary actions by pre-generating multiple resiliency policies and embedding them in a reasoning graph before actual supply chain disruptions occur. This allows the system to have pre-computed contingency plans ready, improving response time and reliability when disruptions happen, while the feedback loop subsequently refines these preliminary policies based on actual performance.
2Measurement precision
If AI/ML models are updated frequently based on user feedback, then measurement precision and policy accuracy improve, but loss of time and computational resources increase due to repeated joint optimization solving
Solution Approach 1:
The system applies partial updates to AI/ML models by selectively retraining only on new feedback data rather than complete retraining. This approach improves model accuracy incrementally while significantly reducing computational time and resources compared to full retraining, balancing precision improvement with time loss constraints.
Solution Approach 2:
The feedback loop operates periodically rather than continuously, updating models at intervals based on accumulated user feedback. This periodic action allows the system to maintain high model accuracy while avoiding excessive computational overhead, as updates are performed only when sufficient feedback data has been collected to justify the computational investment.
3Adaptability or versatility
If a comprehensive resiliency reasoning graph is created with multiple policies, then adaptability and policy coverage improve, but device complexity and difficulty of operation increase
Solution Approach 1:
The resiliency reasoning graph is segmented into multiple independent policies, each addressing specific supply chain disruption scenarios. This segmentation allows the comprehensive graph to maintain high adaptability and policy coverage while improving ease of operation, as users can select and apply only the relevant policy segments needed for their specific situation rather than navigating a monolithic complex system.
Solution Approach 2:
Different regions of the resiliency reasoning graph are designed with different levels of detail and complexity based on local requirements. High-priority policies receive more detailed analysis and embedding, while lower-priority policies use simplified representations. This local quality approach maintains comprehensive policy coverage while reducing overall operational complexity by focusing detailed attention only where needed.
Data Source
AI summary
Spatio-temporal climate forecasts are analyzed and one or more resiliency policies for a supply chain are dynamically generated. The resiliency policy is embedded in a resiliency reasoning graph and a temporal feedback loop is performed based on user feedback regarding the generated resiliency policy and user interaction with the resiliency reasoning graph. One or more machine learning models are updated based on the user feedback and a joint optimization of the machine learning models is re-solved based on the user feedback. The resiliency policy is updated based on the updated machine learning models based on the user feedback and an operation of a supply chain is adjusted based on the updated resiliency policy.