Machine Learning Supplier Assessment Models
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Solution Overview
Problem
Retailers face high costs and risks due to poor product quality, management inefficiencies, and gaps in product and supplier management processes, which current approaches fail to effectively address, particularly in identifying risky suppliers and products.
Innovation Solution
A computer-implemented method using machine learning to assess suppliers by training intermediate and aggregate models with TIC, transactional, and customer data, analyzing supplier performance, and providing predictions and recommendations for improving product and supplier quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional product and supplier management approaches are used, then operational simplicity is maintained, but risk assessment accuracy and quality improvement capabilities deteriorate
Solution Approach 1:
The patent segments the supplier assessment process into multiple specialized machine learning models, each trained on specific data types (TIC data, transactional data, customer data). This segmentation allows each model to specialize in analyzing particular aspects of supplier performance, thereby improving overall risk assessment accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary aggregate machine learning model that synthesizes outputs from multiple intermediate models. This intermediary layer consolidates diverse data sources and model predictions into a unified risk assessment, improving measurement precision by integrating multiple information streams while managing complexity through a structured aggregation approach.
2Measurement precision
If comprehensive data analysis is performed to improve quality assessment, then measurement precision improves, but data retrieval time and processing complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple specialized machine learning models on comprehensive datasets (TIC, transactional, and customer data) before actual supplier assessment. This preliminary training enables the models to quickly process and analyze new supplier data, improving quality assessment accuracy while reducing data retrieval and processing time during actual operations.
3Reliability
If multiple data sources are integrated to identify risky suppliers, then reliability of assessment improves, but device complexity increases
Solution Approach 1:
The patent segments the integration of multiple data sources by creating separate intermediate machine learning models for each data type (TIC data, transactional data, customer data). This segmentation improves assessment reliability by ensuring each data source is analyzed by a specialized model while managing integration complexity through a modular architecture where each model handles specific data independently.
Solution Approach 2:
The aggregate machine learning model serves as a universal intermediary that processes and integrates outputs from multiple specialized intermediate models. This multi-functional approach improves reliability by consolidating diverse data analyses while managing complexity through a single aggregation point that handles multiple data streams uniformly.
Data Source
AI summary
Systems and methods for using machine learning to dynamically assess performance of products and suppliers in the marketplace are disclosed. According to certain aspects, an electronic device may train a plurality of intermediate machine learning models and an aggregate machine learning model using various marketplace data associated with products and suppliers. The electronic device may analyze information associated with a given product or a given supplier using the trained machine learning models to predict a performance of the product or supplier, as well as determine various recommendations associated with the design, testing, inspection, and/or distribution of products.