AI Price Discrepancy Detection via Predictive Regression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Retailers face significant challenges in detecting price errors and discrepancies across multiple stores, leading to revenue, margin, or profit leakage due to variability in procurement prices influenced by store characteristics and undetected discrepancies.
Innovation Solution
A method and system utilizing a predictive model, including natural language processing (NLP) and artificial intelligence (AI), to calculate baseline prices and identify pricing issues by comparing actual prices across stores, flagging outliers and predicting prices to detect errors and discrepancies in procurement and retail pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual price checking methods are used across multiple stores, then operational simplicity is maintained, but detection precision and coverage are insufficient leading to undetected pricing errors
Solution Approach 1:
The patent replaces manual mechanical price checking with an automated AI-based system that uses machine learning models to predict prices and detect discrepancies. The system automatically collects pricing data from multiple stores, processes it through predictive models, and identifies errors without human intervention, thereby improving detection precision while managing complexity through automation.
Solution Approach 2:
The patent introduces an intermediary AI prediction layer between actual prices and discrepancy detection. Instead of directly comparing prices, the system uses machine learning models to generate predicted baseline prices, which serve as intermediaries to identify anomalies. This intermediary approach enables more sophisticated detection while maintaining system manageability.
2Reliability
If price monitoring is extended to cover more stores and products, then detection coverage is improved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent segments the price monitoring system into store-specific predictive models and product-specific analysis components. Each store can have its own customized model trained on local data patterns, while the overall system coordinates these segmented models to provide comprehensive coverage. This segmentation allows scalable expansion to more stores without proportionally increasing overall system complexity.
Solution Approach 2:
The patent implements preliminary action by pre-training machine learning models with historical pricing data before actual discrepancy detection begins. The system performs offline model training and baseline establishment, so that when actual price monitoring occurs, the computational burden is reduced to comparing actual prices against pre-established predictions rather than performing full analysis in real-time.
3Adaptability or versatility
If store-specific procurement price variations are accommodated, then adaptability to different store characteristics is improved, but difficulty in identifying actual errors increases
Solution Approach 1:
The patent applies local quality by customizing predictive models for each store based on its specific characteristics such as location, size, and procurement patterns. Each store's model learns local pricing behaviors and variations, enabling the system to adapt to store-specific nuances while maintaining the ability to detect actual errors through standardized anomaly detection mechanisms.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting model parameters and thresholds based on store-specific data patterns. The system learns optimal detection thresholds and price variation parameters for each store, allowing it to accommodate local variations while maintaining consistent error detection capabilities across different store contexts.
Data Source
AI summary
A method for detecting price errors and/or discrepancies includes receiving an item file containing at least retail store identifiers, product identifiers corresponding to products sold in stores associated with the store identifiers, and product prices corresponding to the product identifiers. The method further includes creating a dataset having a first grouping with the store identifiers, a second grouping with the product identifiers, and a third grouping with the product prices; selecting a group of the stores based on one or more criteria; and applying regression analysis on the dataset to predict a price for each product across the group of the stores. Additionally, the method includes ranking residuals from the regression analysis to identify, within the group of the stores, products whose prices do not match their predicted prices.