Retail Metric Anomaly Detection via Linear Regression
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Solution Overview
Problem
The replenishment process in retail environments is complex and prone to unexpected issues due to various predictable and unpredictable factors, leading to delays and increased costs, which existing technologies struggle to address efficiently.
Innovation Solution
A system that aggregates and caches metric data using linear regression techniques to predict metric bounds and identify anomalies in real-time, allowing for automatic alerting and remediation before disruptions occur, by dynamically determining predicted values and tolerance ranges for retail environment metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data aggregation and linear regression prediction are implemented to detect metric anomalies, then the reliability of the replenishment process is improved, but the device complexity increases
Solution Approach 1:
The system performs preliminary actions by aggregating metric data and calculating linear regression predictions in advance, establishing expected value ranges before actual replenishment operations occur. This allows anomalies to be detected proactively, improving reliability by preventing issues before they disrupt the replenishment process.
Solution Approach 2:
The patent introduces an intermediary analytical layer that sits between raw metric data and replenishment decisions. This layer aggregates data, applies linear regression models, and generates predicted value ranges, serving as a mediator that translates complex data into actionable insights without requiring direct complex processing in the core replenishment system.
2Device complexity
If manual estimations and static bounded ranges are used for metric evaluation, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The system transitions from static bounded ranges to dynamic predicted ranges by applying linear regression analysis to historical metric data. This creates adaptive, time-varying boundaries that automatically adjust to changing conditions, significantly improving measurement precision while maintaining computational efficiency through the use of straightforward regression formulas.
Data Source
AI summary
The disclosed system provides real-time detection and remediation of metric anomalies in a replenishment network. A computer system can receive, from data sources, event data, retrieve historic metric data associated with a metric of a retail environment, aggregate the event data and historic metric data into aggregated data for the metric, store the aggregated data in a cache data store, determine, based on applying a statistical algorithms model to the cached aggregated data, (i) a predicted metric value and (ii) upper and lower tolerance bounds for the metric, iteratively receive the event data, aggregate the data, and determine (i) and (ii) until (a) determining a condition is triggered to evaluate the metric or (b) expiration of a time period to which the predicted value and the upper and lower tolerances apply, and return, based on determining the condition is triggered to evaluate the metric, information about metric anomalies.