Cause Analysis Scoring for Independent Warehouse Abnormalities
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Solution Overview
Problem
In distribution warehouses, identifying the cause of changes to abnormal states is challenging due to variations in steady states and independent occurrences of abnormalities, unlike manufacturing factories where multiple abnormalities often occur under the same conditions.
Innovation Solution
A cause analyzing apparatus that acquires reference and target data, calculates outlier scores, relationship weights, and property weights, and uses these to determine cause scores for explanatory variables associated with changes in objective variables, facilitating the identification of causes even in independent abnormality scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a statistical test framework calculating p-value from bias in abnormalities is used, then cause identification is effective when multiple abnormalities occur under the same condition, but cause identification becomes difficult when abnormalities occur independently for each condition
Solution Approach 1:
The patent changes the statistical parameter from p-value (based on bias in multiple abnormalities) to outlier scores (based on deviation from normal distribution). This allows the system to effectively identify causes whether abnormalities occur repeatedly under the same condition or independently, by measuring how much each data point deviates from the expected normal state rather than requiring multiple occurrences to detect a pattern
Solution Approach 2:
Instead of identifying causes by finding bias across multiple abnormal occurrences (conventional approach), the patent inverts the approach by identifying causes through outlier detection in individual data points compared to the normal state. This inversion allows effective cause identification even when abnormalities occur independently without repetition
2Productivity
If data acquisition and examination systems are developed for manufacturing factories, then cause analysis efficiency is improved, but the systems are not effective for distribution warehouses due to fundamental differences in operational characteristics
Solution Approach 1:
The patent adapts the cause analysis approach by changing from manufacturing-oriented statistical methods (p-value calculation requiring multiple abnormalities) to distribution warehouse suitable methods (outlier score calculation based on deviation from normal state). This parameter change makes the system effective for distribution warehouses where abnormalities occur independently due to uncontrollable factors like customer orders and reception timing
Solution Approach 2:
The patent introduces dynamic adaptability by selecting different analysis methods based on the operational context. The system can handle both manufacturing-type scenarios (with repeated abnormalities) and distribution warehouse scenarios (with independent abnormalities) by dynamically applying appropriate statistical approaches, making the cause analysis system versatile across different environments
Data Source
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AI summary
According to one example, a cause analyzing apparatus (1) acquires a reference data group and target data. The cause analyzing apparatus calculates an outlier score of an explanatory variable of the target data. The cause analyzing apparatus calculates a relationship weight representing strength of a relationship between an objective variable and the explanatory variable of the reference data group. The cause analyzing apparatus calculates a property weight representing a degree of match between values of the objective variable and the explanatory variable of the target data. The cause analyzing apparatus calculates a cause score of the explanatory variable based on the outlier score, the relationship weight, and the property weight.