Attribute-Value Combination Analysis for Retail Assortment Planning
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Solution Overview
Problem
Traditional product assortment planning in retail is inadequate as it relies on human intuition and rudimentary business intelligence, making it difficult to accurately determine the combination of product qualities or characteristics that satisfy consumer demand, especially with the vast number of products and attributes involved.
Innovation Solution
The system and method employ an attribute-value combination analysis to determine the most valuable attribute combinations for product assortment decisions, using a transactional approach to analyze product attributes and value metrics, and generating assortment plans based on sales, profitability, and other relevant data within the supply chain network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional human-based planning methods are used to determine product assortments, then the planning process is simple and intuitive, but the accuracy of identifying consumer-demanding product combinations deteriorates due to the enormous number of products and attributes
Solution Approach 1:
The patent replaces the mechanical human decision-making process with an automated computer-based system that uses algorithms to analyze product attributes and historical data. The system automatically generates attribute-value combinations and ranks them based on consumer demand metrics, eliminating the limitations of human intuition while handling the enormous complexity of product attribute analysis.
Solution Approach 2:
The system enables self-service by allowing the computer to autonomously perform the entire assortment planning process without human intervention. The automated system independently analyzes historical sales data, identifies profitable attribute combinations, generates product assortment recommendations, and updates plans based on changing consumer demands, making the complex analysis self-executing.
2Productivity
If a planner manually analyzes historical data to identify successful product combinations, then the process is straightforward, but the ability to accurately parse and analyze the combination of products and attributes deteriorates
Solution Approach 1:
The patent segments the complex product assortment planning problem into distinct analytical components: extracting product attributes from historical data, generating all possible attribute-value combinations, evaluating each combination's performance metrics, and ranking them based on consumer demand. This segmentation allows the system to systematically process and retain complete information about each attribute combination without manual oversight.
Solution Approach 2:
The system introduces an intermediary computational layer that acts as a bridge between raw historical data and final assortment decisions. This intermediary automatically parses, structures, and evaluates all attribute combinations, ensuring no information is lost in the transition from historical data to planning recommendations, while significantly improving processing efficiency.
3Reliability
If traditional planning methods are used, then the decision-making process is simple and quick, but the ability to identify qualities or characteristics that satisfy consumer demand deteriorates
Solution Approach 1:
The patent replaces unreliable human judgment with a reliable automated system that consistently evaluates all product attributes and combinations using standardized algorithms. The system reliably identifies consumer-demanding characteristics by analyzing historical sales data and performance metrics, ensuring consistent and objective decision-making that can be replicated and updated as new data becomes available.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring sales performance and consumer demand patterns, using this feedback to refine and update attribute combination rankings. This feedback loop ensures the system adapts to changing consumer preferences and maintains high reliability in identifying profitable product assortments over time.
Data Source
AI summary
A system and method are disclosed for generating an assortment plan by an assortment planner. The assortment planner stores value metrics for two or more products, each of the products including two or more of attributes. The assortment planner also analyzes the two or more attributes, by generating attribute values for each of the two or more attributes and communicates the attribute values for each of the two or more attributes to a planning engine. The assortment planner also further constructs a product attributes table and a value metrics table, duplicates the attribute values to generate a merged product attributes table including a number of duplicates of the attribute values and generates a binary value table for each of the attribute values, one or more itemsets, and an assortment of products based, at least in part, on the one or more itemsets. Other embodiments are also disclosed.