Product Attribute Combination Analysis for Faster Assortment Planning
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Solution Overview
Problem
Existing supply chain systems struggle with computationally intensive methods for deriving insights on product attribute combinations, leading to inaccurate assortment planning due to reliance on human intuition and inefficiencies in locating insights within feasible timeframes.
Innovation Solution
A system and method utilizing frequentist and Bayesian analysis techniques to conduct hindsight analysis, identifying high-performing product attribute combinations efficiently, and generating recommendations based on attribute strengths in both supervised and unsupervised contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems are used to model demand and derive insights on product attribute combinations, then insight accuracy is improved, but computational intensity increases and insight location time exceeds feasible timeframes
Solution Approach 1:
The patent segments the large-scale machine learning problem into smaller, more manageable components by using frequentist and Bayesian analysis techniques that can be applied to specific product attribute combinations independently. This allows the system to focus computational resources on targeted analyses rather than processing entire datasets uniformly, thereby reducing overall computational time while maintaining insight accuracy.
Solution Approach 2:
The patent changes the analytical parameters by transitioning from traditional supervised/unsupervised machine learning approaches to frequentist and Bayesian statistical methods. This parameter change enables the system to derive insights with comparable accuracy but with significantly reduced computational intensity and faster execution times, as these statistical methods are more efficient for the specific task of analyzing product attribute combination performance.
2Use of energy by moving object
If traditional supply chain planning methods using human intuition are used, then computational resources are saved, but assortment planning accuracy deteriorates
Solution Approach 1:
The patent introduces frequentist and Bayesian analysis techniques as intermediary methods between human intuition and complex machine learning systems. These techniques provide a structured, data-driven approach that captures domain expertise more systematically than human intuition alone, improving assortment planning accuracy while requiring far fewer computational resources than full machine learning models.
3Loss of information
If comprehensive machine learning modeling is applied to supply chain data, then insight depth is improved, but system complexity increases
Solution Approach 1:
The patent extracts and applies only the essential statistical principles (frequentist and Bayesian methods) needed to derive insights on product attribute combinations, rather than implementing comprehensive machine learning systems. This extraction approach maintains sufficient insight depth for assortment planning decisions while dramatically reducing system complexity and making the solution more accessible and interpretable.
Data Source
AI summary
A system and method are disclosed for extracting hindsights for assortment planning. The system provides for classifying each of one or more products in a product display area of a retail entity as high-performers or low-performers according to selected metrics. The system further provides for identifying a strength of each individual product attribute associated with each of the products, deriving a multi-combination strength of combinations of product attributes using the identified strength of each individual product attribute, and generating recommendations for product combinations based on the derived multi-combination strength of combinations of product attributes and the selected metrics.