Assortment Definition Engine for Retail Store Grouping
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Solution Overview
Problem
Current assortment planning methods in retail environments often rely on repetitive and sales-volume-based approaches, failing to effectively match products with customer desires and trends, leading to inefficient product placement and timing.
Innovation Solution
A system and method for defining assortments that utilize an assortment definition engine to input store information, perform matching processes, and identify optimal store groupings based on dimension information, enabling automated proposal of store-assortment assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional sales-volume-based assortment planning is used, then implementation simplicity is maintained, but assortment effectiveness and customer alignment deteriorate
Solution Approach 1:
The patent replaces manual, experience-based assortment planning with an automated computer-implemented system that uses classification engines and matching algorithms. This substitution transforms the mechanical process of manual analysis into an automated information processing system, resolving the contradiction by making the system complex but the operation simple and effective.
Solution Approach 2:
The system performs self-service by automatically classifying stores, generating assortment recommendations, and identifying optimal store-assortment assignments without requiring manual intervention. The classification engine and matching process operate autonomously, improving assortment effectiveness while keeping the user interface simple.
2Productivity
If manual assortment planning is used, then system complexity is low, but time consumption and efficiency deteriorate
Solution Approach 1:
The patent replaces manual assortment planning activities with automated computer-based processing. The classification engine automatically analyzes store characteristics, and the matching algorithm generates recommendations, dramatically improving productivity while accepting the necessary system complexity.
Solution Approach 2:
The system performs preliminary classification of stores and pre-generation of assortment recommendations before final assignment decisions are made. This preliminary processing automates the preparatory work, improving overall planning efficiency while structuring the complexity in manageable stages.
3Adaptability or versatility
If repetitive assortment breadth from previous seasons is used, then planning simplicity is maintained, but adaptability to customer trends and lifestyles deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms by analyzing store performance data, customer trends, and lifestyle factors to continuously refine assortment recommendations. The classification engine uses multiple dimensions including sales data and store characteristics to adapt to changing customer preferences, resolving the contradiction between adaptability and time consumption.
Solution Approach 2:
The system transforms static, repetitive assortment planning into a dynamic process that automatically adapts to changing customer trends and store characteristics. The matching algorithm continuously evaluates multiple dimensions and generates updated recommendations, making the system responsive to change while automating the time-consuming analysis.
4Measurement precision
If store assortments are created based on store volume alone, then simplicity is maintained, but measurement precision and strategic alignment deteriorate
Solution Approach 1:
The patent segments store classification into multiple dimensions including store characteristics, sales data, customer demographics, and lifestyle factors. The classification engine divides the complex assessment into separate analytical components, improving measurement precision while managing system complexity through structured segmentation.
Solution Approach 2:
The system adds multiple dimensions to store classification beyond simple volume metrics, incorporating geographic, demographic, and behavioral dimensions. This multi-dimensional approach improves classification accuracy by considering diverse factors simultaneously, resolving the contradiction between precision and complexity.
Data Source
AI summary
A system and method for performing assortment definition is provided. The method comprises inputting information regarding a plurality of stores into an assortment definition engine, and performing a matching process to identify at least one group of stores for assignment to the assortment. An automated system for defining an assortment and a program product for defining an assortment are also provided.