Smart Product Attributes for New Product Retail 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 and reduced predictive insight.
Innovation Solution
A system and method using smart product attributes to enhance retail planning by providing an interactive user interface that identifies and transforms product attributes, offers visual feedback, and visualizes relationships between product data and machine learning predictions, enabling accurate product 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 value sets for each attribute. This segmentation allows the system to capture detailed product information systematically without overwhelming complexity, enabling precise attribute measurement while maintaining structured data organization.
Solution Approach 2:
The system transforms product descriptions from unstructured text into standardized parameter-based attributes with defined value sets. By changing the representation from free-text to parameterized data structures, the system achieves higher measurement precision for product attributes while enabling consistent computational processing.
2Adaptability or versatility
If retailers use product attributes to describe new products without historical data, then the adaptability to new products is improved, but the reliability of sales predictions deteriorates due to lack of historical sales data
Solution Approach 1:
The system implements feedback loops where product attribute data from new products is continuously collected, stored, and used to train and refine machine learning models. This feedback mechanism allows the system to progressively improve prediction reliability for new products by learning from accumulated attribute-performance relationships across multiple product cycles.
Solution Approach 2:
The system performs preliminary analysis of product attributes using machine learning models to generate initial sales predictions and performance estimates before products are launched. This preliminary action enables retailers to make informed decisions about new product assortment and inventory allocation even without historical sales data for specific new products.
3Ease of manufacture
If product data from supply chain entities is disorganized and inconsistent, then the ease of data collection is improved, but the manufacturing precision of product attribute data deteriorates
Solution Approach 1:
The patent introduces an intermediary data normalization layer that sits between diverse supply chain data sources and the retail planning system. This intermediary automatically transforms inconsistent data from various sources into a standardized attribute format, maintaining ease of data collection while ensuring manufacturing precision through automated consistency enforcement.
Solution Approach 2:
The system creates a universal product attribute framework that can accommodate data from multiple supply chain entities with different data formats and conventions. This universal framework defines standardized attribute schemas that can universally represent products across different sources, enabling consistent data processing while maintaining flexibility for diverse input formats.
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.