Segmented Supply Chain Model Parameter Updating
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Solution Overview
Problem
Complex analytical models used in managing and analyzing segmented manufacturing or supply chain processes are difficult to update and maintain due to their intricate nature, leading to inefficiencies in predicting and reporting key metrics across dynamic supply chain sections.
Innovation Solution
A method and system for automatically updating complex analytical models by acquiring actual observed values, comparing predicted and actual values, determining performance scores, statistically adjusting parameters, and updating the models when performance is unacceptable, allowing for continuous adaptation to changes in supply chain dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to update complex analytical models, then model structure can be maintained, but updating efficiency and speed are severely limited
Solution Approach 1:
The patent creates virtual copies of the complex analytical model parameters and structures within a computing environment, allowing updates to be performed on these digital representations rather than physical models. This enables rapid iteration and testing of model updates without affecting actual manufacturing processes, significantly improving updating efficiency while managing complexity through virtualization.
Solution Approach 2:
The patent replaces manual, mechanical model updating processes with automated computational algorithms. Statistical software and computing systems automatically adjust model parameters based on observed data, eliminating the need for manual model restructuring and updating. This substitution of automated computational mechanisms for manual processes dramatically increases updating speed and efficiency.
2Measurement precision
If detailed mathematical algorithms are linked to each segment, then predictive accuracy improves, but model management and updates become increasingly difficult
Solution Approach 1:
The patent divides the complex analytical model into separate segment models, each handling specific portions of the supply chain or manufacturing process. This segmentation allows individual segments to be updated and managed independently, reducing the overall complexity of model management while maintaining detailed mathematical algorithms within each segment for high predictive accuracy.
Solution Approach 2:
The patent creates a universal framework and common interface that can accommodate multiple different mathematical algorithms across various segments. This universal structure allows the system to manage diverse complex algorithms through a single standardized management approach, improving ease of operation while preserving the predictive accuracy benefits of detailed mathematical modeling.
3Reliability
If models are updated frequently to reflect supply chain dynamics, then predictive accuracy improves, but computational resources and time requirements increase
Solution Approach 1:
The patent implements periodic updating of model parameters at predetermined intervals or when specific triggers are met, rather than continuous updating. This periodic action maintains predictive reliability by keeping models current with supply chain dynamics while significantly reducing the time and computational resources required compared to continuous updating approaches.
Solution Approach 2:
The patent updates only specific parameters that have changed or show significant deviations from expected values, rather than recalculating entire models. This selective parameter updating approach maintains predictive reliability by focusing computational efforts on the most relevant changes while minimizing overall updating time and resource consumption.
Data Source
AI summary
A method of systematically applying parameter evaluation techniques to large numbers of complex segmented models used for managing and analyzing a segmented manufacturing or supply chain process. These models include a plurality of segment models, each segment model representing a process segment within the segmented manufacturing or supply chain process and comprising a mathematical expression including at least one parameter. The method compares predicted values generated by a segment model with actual observed values to determining a score representative of the performance of the segment model. New parameters for the mathematical equation associated with the segment model are determined through statistical techniques when the model score indicates an unacceptable performance of the segment model. The segment models may be evaluated and updated in parallel and without manual intervention.