Attribute-Based Decomposition Engine for Product Portfolio Optimization
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Solution Overview
Problem
Conventional systems lack automated and scalable methods to estimate the importance of product characteristics and demand transferability among products, failing to provide data-driven decision approaches for optimizing product portfolios and pricing strategies.
Innovation Solution
An attribute-based decomposition engine using non-parametric machine learning and game theoretic frameworks to quantify the contribution of each product attribute to sales and estimate demand transferability, determining weights of product attributes and simulating different business scenarios for optimal Price Pack Architecture (PPA) strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional systems are used to analyze product attributes, then manual analysis methods are employed, but automation and scalability are lacking
Solution Approach 1:
The patent replaces manual mechanical analysis methods with machine learning models (Random Forest, Gradient Boosting, Neural Networks) that automatically process product attribute data. The system uses algorithms to quantify attribute importance and estimate demand transferability without human intervention, achieving both automation and scalability simultaneously.
2Measurement precision
If comprehensive product attribute analysis is performed, then accurate demand transferability estimation is achieved, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modules: data preprocessing, multiple machine learning model training (Random Forest, Gradient Boosting, Neural Networks), attribute importance calculation, and demand transferability estimation. Each module handles a specific aspect of the analysis, making the overall complex system manageable and interpretable while maintaining high precision.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw product attribute data and demand transferability estimates. These models act as mediators that transform complex attribute relationships into quantifiable importance scores and transferability metrics, reducing system complexity while preserving measurement accuracy.
3Measurement precision
If multiple machine learning models are used to quantify attribute importance, then measurement accuracy improves, but computational resources increase
Solution Approach 1:
The patent merges multiple machine learning models (Random Forest, Gradient Boosting, Neural Networks) into a unified analysis framework. By training and comparing multiple models on the same dataset, the system leverages their complementary strengths to produce more accurate and robust attribute importance weights, justifying the increased computational resources through improved measurement precision.
Data Source
AI summary
Systems and methods for attribute-based modelling are disclosed. A system includes an attribute-based decomposition engine, which when executed using a processor, causes the engine to retrieve one or more product attributes associated with each of a set of products, an importance of the product attributes being determined based on the product sales data, product data, product parameters, and financial data associated with product. The attribute-based decomposition engine using the processor establishes, for a set of products, a relationship between a retrieved one or more product attributes and product sales associated with product, based on implementation of a non-parametric machine learning (ML) modeling on a data model. The attribute-based decomposition engine quantifies contribution of each product attribute on product sales based on an established relationship and a game theoretic framework. The attribute-based decomposition engine using the processor estimates demand transferability among a set of products based on the determined weights of the respective product attributes.