Bayesian Store Remodel Recommendation System
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Solution Overview
Problem
Current methods for determining which stores to remodel are based on guesses and lack a quantifiable, repeatable approach, failing to accurately consider the numerous variations in remodeling options and their impact on profitability.
Innovation Solution
A computer system utilizing a Bayesian Structural Time Series model to analyze profitability impacts, generate lift classifications, and calculate remodel scores, which are then used to determine whether a store should be remodeled, considering approximately 2^500 possible variations in remodeling scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional guess-based methods are used to determine store remodels, then the decision-making process is simple, but the accuracy and consistency of remodeling recommendations deteriorates
Solution Approach 1:
The patent segments the remodeling evaluation into multiple independent components: data collection module, feature extraction module, scoring module, and recommendation module. Each module handles a specific aspect of the evaluation, making the complex system manageable and improving accuracy through specialized processing at each stage.
Solution Approach 2:
The patent transforms qualitative remodel decisions into quantitative parameters by assigning numerical scores to various factors (store performance, customer traffic, operational efficiency). This parameter transformation enables consistent, repeatable evaluations and allows for precise comparison across different store candidates.
2Reliability
If 2^500 remodeling variations are evaluated manually, then comprehensive analysis is achieved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent replaces manual mechanical evaluation with an automated computer-based system that uses algorithms and data processing to assess remodeling variations. This substitution enables rapid evaluation of 2^500 scenarios by transforming the mechanical process into computational operations that can be executed efficiently by processors.
Solution Approach 2:
The patent performs preliminary data collection and feature extraction before the actual evaluation process. By pre-processing data and identifying key characteristics in advance, the system reduces the computational burden during the main evaluation phase, enabling faster processing of numerous remodeling variations.
3Measurement precision
If detailed data analysis is performed on each store, then the accuracy of profitability impact assessment improves, but the time and computational resources required increase
Solution Approach 1:
The patent extracts only the most relevant features and data points from extensive store information, focusing on key profitability indicators, traffic patterns, and operational metrics. This extraction approach maintains assessment accuracy by concentrating on critical factors while discarding redundant information that would consume unnecessary analysis time.
Solution Approach 2:
The patent applies partial analysis by evaluating a selected subset of stores in detail while using summary assessments for others. This approach balances accuracy requirements with time constraints by applying comprehensive analysis only where most needed, rather than uniformly analyzing all stores at maximum detail.
Data Source
AI summary
An iterative, tiered system for identifying which assets need to be remodeled. This tiered system uses a Bayesian Structural Time Series, followed by an ensemble classification and cost estimation. The results are then input into an optimization model, where the best possible set of stores is selected according to the constraints. Remodeling of the store then commences.