Virtual Outfit Catalog for Custom Product Order Aggregation
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Solution Overview
Problem
Vendors face challenges in producing and selling custom products due to high costs associated with creating prototypes and marketing, as well as difficulty in gathering sufficient orders for custom products tailored to individual preferences, making it hard to achieve profitability and efficiently utilize production processes.
Innovation Solution
A system and method for automatically collecting, processing, and combining custom product orders into bulk orders using a virtual fitting system with adjustable prices and configurable terms, where virtual outfit catalogs are broadcasted to consumers, and orders are converted into bulk orders when reaching a minimum quantity through a digital sale contract.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vendors produce custom products with high quality and tailored to individual preferences, then customer satisfaction improves, but production costs increase significantly due to prototype creation and marketing expenses
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing consumer preferences and order patterns before production. Virtual outfit catalogs are created in advance with customizable parameters, allowing the system to pre-configure production parameters based on aggregated demand data, thereby reducing per-unit prototype costs while maintaining high quality standards
Solution Approach 2:
The invention changes production parameters dynamically based on order aggregation. As more orders are collected for similar custom products, the system adjusts production parameters to optimize for batch production efficiency, reducing the marginal cost of each additional unit while preserving customization quality through parameter optimization
2Productivity
If vendors aggregate custom orders into bulk orders to reduce production costs, then profitability improves, but difficulty increases in finding customers with matching preferences for the same product
Solution Approach 1:
The system implements continuous feedback loops where consumer preferences, order patterns, and purchase behaviors are constantly monitored and fed back into the recommendation engine. This feedback mechanism automatically identifies groups of consumers with matching preferences, enabling seamless order aggregation without manual intervention while maintaining high profitability
Solution Approach 2:
The invention enables self-service order aggregation through automated algorithms that independently match consumers with similar preferences and aggregate their orders. The system autonomously performs the complex task of finding compatible order groups, eliminating the need for manual market research and reducing aggregation complexity
3Adaptability or versatility
If vendors create unique custom products for each customer, then customer personalization improves, but production efficiency decreases due to inability to duplicate successful prototypes
Solution Approach 1:
The system segments customization into modular parameters that can be independently adjusted. Virtual outfit catalogs are structured with separable customization dimensions (style, color, material, fit), allowing the system to identify common segments across multiple orders and produce standardized components in bulk while maintaining final product personalization through parameter combination
Data Source
AI summary
A system and method for automatic collecting, processing and combining custom product orders into bulk orders according to a procedure for storing custom product offers with adjustable price and configurable terms in virtual outfit catalogs of a virtual fitting system. Broadcasting the virtual outfit catalog to consumers in timeframes using the virtual fitting system, collecting orders from consumers. Automatically adjusting the product offers in the virtual outfit catalog for promoting the products and prototypes that received more orders. Upon receiving more orders than the minimum order quantity of a product or prototype, automatically converting the orders into a bulk order using the digital sale contract.