Smart Product Attributes for New Product Assortment Planning
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Solution Overview
Problem
Retailers face challenges in launching new products due to the lack of historical sales data, inconsistent product attribute data, and limitations of retail planning systems, leading to erroneous predictions and analysis, particularly in fashion retail where product attributes are not consistently defined, causing errors in assortment planning.
Innovation Solution
A system and method using smart product attributes that provide a user interface for editing and inputting product attributes and values, offering visual feedback and machine learning enhancements to improve prediction accuracy and analysis, enabling better assortment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional retail planning systems use simplistic grid view product selectors, then the system operation is simple, but the measurement precision of product attributes is poor and erroneous descriptions are generated
Solution Approach 1:
The patent segments product attributes into hierarchical categories (e.g., product type, material, color, size) with standardized values at each level. This segmentation allows the system to capture detailed product information through structured attribute selection rather than free-text input, improving measurement precision while maintaining user-friendly interfaces.
Solution Approach 2:
The patent introduces an intermediary layer between the user interface and the planning system that automatically processes and validates product attribute data. This intermediary layer includes attribute validation rules, consistency checks, and automatic completion features that ensure accurate data entry without requiring complex user input procedures.
2Adaptability or versatility
If retailers use product attributes to describe new products without historical sales data, then the adaptability to new products is improved, but the reliability of sales predictions deteriorates
Solution Approach 1:
The patent establishes predetermined attribute schemas and value hierarchies before new products are introduced. By pre-defining the structure and validation rules for product attributes, the system ensures that even new products without historical data are described using consistent, standardized attributes that can be reliably compared across different products and seasons.
Solution Approach 2:
The patent transforms product descriptions from unstructured or semi-structured text into structured parameter-based representations with standardized values. This parameterization allows the system to perform quantitative analysis and comparisons on new products using consistent attribute metrics, thereby improving the reliability of predictions for products without historical sales data.
3Quantity of substance
If product attribute data from multiple supply chain entities is collected, then the quantity of product information is increased, but the homogeneity of data consistency deteriorates
Solution Approach 1:
The patent implements a universal attribute schema that can accommodate product information from multiple supply chain entities (suppliers, manufacturers, retailers) while maintaining consistent data structure. The standardized attribute framework allows different entities to contribute their data using the same vocabulary and classification system, enabling the system to aggregate diverse information sources without sacrificing data homogeneity.
Data Source
AI summary
A system and method are including a computer and a processor and memory. The computer receives a product class representing a product in a supply chain network including one or more supply chain entities and generates one or more new products for the product class using one or more automatically generated templates including a graphical representation of an exemplary product using a first smart product attribute value, the first smart product attribute value defined by a quantifiable measurement of a product attribute. The computer further causes items to be transported among the one or more supply chain entities to restock the inventory of the one or more items of the product class according to the current state of items in the supply chain network and the one or more new products.