AI BOM Recommendation Engine for Customer-Specific Product Matching
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Solution Overview
Problem
Current product recommendation systems are generalized and often suggest irrelevant products, lacking alignment with customer-specific requirements, especially in companies with large product portfolios.
Innovation Solution
A system comprising a BOM processing device that leverages a recommendation engine to retrieve and process customer, product, and transaction data, employing various filtering methods such as community-based, popularity-based, content-based, and collaborative filtering to generate a personalized bill of materials aligned with customer needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generalized product recommendation systems are used, then system complexity is reduced, but product recommendation accuracy deteriorates
Solution Approach 1:
The patent segments the recommendation system into multiple specialized components: collaborative filtering module, content-based filtering module, popularity-based filtering module, and community-based filtering module. Each module handles specific aspects of product recommendation independently, allowing the system to maintain low overall complexity while achieving high recommendation accuracy through coordinated operation of specialized segments.
2Measurement precision
If multiple product recommendation processes are employed, then product recommendation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple independent filtering processes (collaborative filtering, content-based filtering, popularity-based filtering, and community-based filtering) into a unified recommendation system. Each filtering process operates as a separate module that can be independently developed and maintained, yet they work together to produce comprehensive product recommendations, balancing accuracy improvement with manageable system complexity.
3Adaptability or versatility
If personalized bill of materials generation is implemented, then customer satisfaction is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing customer data, product data, and transaction data into structured formats before actual recommendation generation. The collaborative filtering module pre-computes user-item interaction matrices, and the content-based filtering module pre-extracts product features and attributes. These preliminary processing steps enable faster personalized bill of materials generation when actual customer requests are received, reducing processing time while maintaining high adaptability.
Data Source
AI summary
A system for generating a bill of materials comprising one or more product recommendations aligned with customer requirements, comprises a bill of materials processing device. The bill of materials processing device is configured to execute an AI-based recommendation engine. The recommendation engine is configured to receive customer data from one or more user inputs, retrieve product data from a product data source, and retrieve transaction data from a transaction data source. The recommendation engine includes a pre-processing module, training module, and inference module. The pre-processing module executes one or more pre-processing techniques to translate the data into a program-compatible format. The training module executes training and inference processes to generate the bill of materials. The training processes put the data in better condition for the inference module. The inference module uses two or more product recommendation processes to evaluate the data and to generate the BOM in a machine-readable format.