Adaptive Predictive Analytics for Supply Chain Design Modification
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Solution Overview
Problem
Current supply chain management systems face inefficiencies and inaccuracies in processing product change requests due to the need for human interaction, private data protection, and complex communication between nodes, which complicates decision-making and implementation of changes within the supply chain.
Innovation Solution
An adaptive predictive analysis network employing AI modules that generate and train predictive models using both private and public data to automate decision-making processes for product change requests, including approval, denial, or recommendation, within the supply chain, utilizing neural networks, machine learning, and deep learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human interaction is used for decision-making in product change requests, then accuracy and contextual understanding are improved, but productivity and efficiency deteriorate due to time-consuming manual processes
Solution Approach 1:
The patent introduces an AI-based intermediary system that mediates between product change requests and final decisions. The AI model analyzes private node data, public supply chain data, and historical decisions to automate approval/denial processes, reducing human intervention while maintaining decision quality through machine learning algorithms
Solution Approach 2:
The system performs preliminary actions by pre-training AI models with historical decision data and private node information before actual product change requests occur. This preliminary training enables the system to quickly process requests with high accuracy without requiring real-time human analysis
2Measurement precision
If private data from multiple nodes is collected for comprehensive analysis, then measurement precision and decision quality are improved, but device complexity and data security requirements worsen
Solution Approach 1:
The patent segments the supply chain data into private node data and public supply chain data, with each segment processed appropriately. Private data remains secured at individual nodes while public data is aggregated for model training, reducing overall system complexity through structured data segmentation
Solution Approach 2:
The AI model acts as an intermediary that receives and processes segmented data from multiple nodes without requiring direct complex interactions between nodes. The model integrates private and public data through standardized interfaces, simplifying the overall system architecture
3Measurement precision
If comprehensive data from all supply chain nodes is aggregated for training, then predictive model accuracy is improved, but loss of information security and data privacy worsens
Solution Approach 1:
The patent segments training data into private node data that remains localized and public supply chain data that is aggregated for model training. This segmentation allows the predictive model to learn from comprehensive data patterns while preserving the privacy and security of sensitive private information at each node
Solution Approach 2:
The system uses copies of public data and aggregated patterns for training the predictive model without requiring access to or transmission of original private data. The model learns from replicated information and statistical patterns that preserve data privacy while maintaining training effectiveness
4Adaptability or versatility
If manual communication and coordination between supply chain nodes is used, then adaptability to complex situations is improved, but productivity and response time deteriorate
Solution Approach 1:
The patent enables the supply chain system to serve itself through automated AI-based decision-making. The predictive model independently analyzes requests, coordinates with relevant nodes, and implements decisions without requiring manual communication, achieving both high productivity and adaptability through intelligent automation
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a predictive analytics system that provides a mechanism to change the design or implementations of a product manufactured in a supply chain are disclosed. In one aspect, a method includes the actions of receiving training data that includes private information for a node in a supply chain network and information regarding previous decisions related to product change requests for a product manufactured through the supply chain network; training, using the training data, a predictive model configured to render decisions for requests to change a part used in manufacturing the product; receiving a request to change a given part; applying the predictive model to the request to change the given part; determining a decision approving or denying the request; and transmitting the decision to the requesting node.