Supply Chain Anomaly Detection Ensemble Using Bayesian and Frequentist Models
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Solution Overview
Problem
Existing supply chain machine learning systems fail to leverage Bayesian data analysis algorithms for anomaly detection and are unable to combine frequentist and Bayesian approaches effectively, leading to suboptimal performance in identifying data anomalies.
Innovation Solution
A probabilistic graphical model (PGM) system that integrates both frequentist and Bayesian anomaly detection algorithms, using weighted combinations to identify and correct data anomalies in supply chain networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only frequentist algorithms are used for anomaly detection, then good performance is achieved over large data sets, but the system cannot leverage prior information effectively
Solution Approach 1:
The patent combines frequentist and Bayesian anomaly detection algorithms into a unified system that leverages the strengths of both approaches. The frequentist component handles large-scale data effectively while the Bayesian component incorporates prior information and domain knowledge, creating a more versatile and accurate anomaly detection system than either approach could achieve alone.
2Adaptability or versatility
If only Bayesian algorithms are used, then good performance is achieved when prior information is available, but the system performs suboptimally over large data sets without prior information
Solution Approach 1:
The patent integrates Bayesian and frequentist methods in an ensemble framework where the Bayesian component utilizes prior information when available, while the frequentist component provides robust performance on large datasets. This combination ensures the system maintains high accuracy across different data conditions and scales.
3Device complexity
If existing systems use only one approach (frequentist or Bayesian), then implementation is simpler, but the system cannot gain benefits of both approaches
Solution Approach 1:
The patent implements an ensemble system that integrates frequentist and Bayesian anomaly detection algorithms. Despite the increased complexity of combining multiple approaches, the system achieves superior anomaly detection accuracy by leveraging the complementary strengths of both methods, with frequentist handling large-scale patterns and Bayesian incorporating domain knowledge and prior information.
Data Source
AI summary
A system and method are disclosed for applying machine learning to identify anomalous supply chain data that generates a probabilistic graphical model based on training data from historical attributes of a supply chain comprising supply chain entities to represent the performance of the supply chain entities in the supply chain, standardizes input features data related to the probabilistic graphical model, performs data anomaly detection within the probabilistic graphical model using one or more frequentist data anomaly detection algorithms, performs data anomaly detection within the probabilistic graphical model using one or more Bayesian data anomaly detection algorithms, combines according to one or more weighting methods, the data anomaly detection performed using one or more frequentist data anomaly detection algorithms with the data anomaly detection performed using one or more Bayesian data anomaly detection algorithms, and detects in response to the combining, an anomaly within the standardized input features data.