ERP SKU Classification Using Context-Specific ML Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing SKU classification methods overlook diverse perspectives, leading to inconsistent categorization and are time-consuming and error-prone, hindering scalability and accuracy in data management, supply chain optimization, and regulatory compliance.
Innovation Solution
A machine learning-based classification system that integrates natural language processing and calibrated support vector machines, leveraging context-specific rules and regulatory guidelines for accurate SKU classification, seamlessly integrating with ERP systems for real-time data synchronization and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual classification processes are used, then flexibility and adaptability to diverse SKU perspectives are maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The system segments the classification task by creating multiple specialized classifier models, each trained on specific datasets representing different SKU perspectives (e.g., retail, manufacturing, supply chain). Each classifier handles a specific classification context, allowing the system to process diverse SKU types efficiently while maintaining adaptability to different classification requirements.
Solution Approach 2:
The patent replaces manual mechanical classification processes with an automated machine learning-based classification system. The system uses trained classifier models that automatically process SKU data, eliminating the need for manual classification while maintaining high adaptability through configurable classifiers that can be trained on diverse datasets representing different business perspectives.
2Adaptability or versatility
If manual classification processes are used, then flexibility in handling diverse SKU perspectives is maintained, but accuracy and consistency deteriorate
Solution Approach 1:
The system divides the classification problem into multiple specialized classifier models, each trained on specific datasets representing different SKU perspectives. This segmentation allows each classifier to achieve high accuracy for its specific domain while the collective system maintains flexibility across diverse SKU types.
Solution Approach 2:
The system changes parameters by training classifier models with different parameters and configurations tailored to specific SKU perspectives. Each classifier can be optimized with specific training data, features, and parameters relevant to its classification context, thereby achieving high accuracy for diverse classification scenarios.
3Device complexity
If existing classification techniques are used, then implementation simplicity is maintained, but scalability and usability are hindered
Solution Approach 1:
The patent creates a universal classification system where a single platform supports multiple classifier models that can handle diverse SKU classification requirements. The system provides a unified interface and common infrastructure that can scale to handle increasing numbers of SKUs and classification contexts without requiring separate manual processes for each scenario.
Solution Approach 2:
The system performs preliminary actions by pre-training multiple classifier models on diverse datasets representing different SKU perspectives before deployment. These pre-trained classifiers are ready to immediately process new SKU data, enabling the system to scale efficiently without requiring manual classification for each new SKU type.
4Quantity of substance
If existing classification techniques are used, then resource requirements are minimized, but classification accuracy and usability deteriorate
Solution Approach 1:
The patent merges multiple classifier models into a single integrated classification system. By combining the capabilities of multiple specialized classifiers trained on different datasets, the system achieves high classification accuracy across diverse SKU perspectives while consolidating resource requirements into a unified platform rather than requiring separate systems for each classification scenario.
Data Source
AI summary
The technology described herein is directed towards product classification across multiple contexts within an enterprise environment, e.g., based on stock keeping unit (SKU-) related data. Relevant SKU attributes and patterns for each context are used to train machine learning-based classification models, which are then used to classify descriptive data of a product, such as one or more modules of a SKU. In one implementation, natural language processing and vectorization of input data are described to provide appropriate input to the trained classifier. Context-specific rules derived from domain knowledge and regulatory guidelines can be integrated to refine the classifications made by the model. The system incorporates integration with enterprise resource planning (ERP) systems, enabling efficient data management and classification within an existing infrastructure.