LLM Demand Prediction Error Interpretation Using Multi-Source Product Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing demand prediction technologies lack the ability to accurately analyze and support the errors in demand prediction for specific products, particularly due to the lack of integration of intra-company and extra-company information.
Innovation Solution
An information processing system and method that utilizes a large language model to generate interpretation examples based on product-specific indices, intra-company information, and extra-company information to support the analysis of demand prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If demand prediction is performed using only basic data, then the prediction process is simple and fast, but the accuracy of demand prediction is insufficient
Solution Approach 1:
The patent combines multiple information sources including intra-company information (sales data, inventory data, production data) and extra-company information (market trends, competitor information, economic indicators) into a unified demand prediction system. This merging of diverse data sources enhances prediction accuracy while the information processing unit integrates them systematically to manage complexity.
Solution Approach 2:
The demand prediction system is designed to handle multiple types of data (structured and unstructured) from various sources simultaneously. The information processing unit performs multiple functions including data collection, cleaning, integration, and analysis, making the system multi-functional and adaptable to different prediction scenarios.
2Measurement precision
If multiple information sources are integrated to improve prediction accuracy, then the accuracy of demand prediction increases, but the complexity of information processing increases
Solution Approach 1:
The patent segments the information processing into distinct units: an information processing unit that handles data collection and integration, and a demand prediction unit that performs analysis. This segmentation allows complex information processing to be divided into manageable tasks, reducing overall system complexity while maintaining high prediction accuracy through specialized processing at each stage.
Solution Approach 2:
The information processing unit acts as an intermediary between multiple data sources and the demand prediction unit. It collects, cleans, and integrates diverse information from intra-company and extra-company sources, transforming raw data into structured formats that the prediction unit can effectively analyze, thereby simplifying the interface between data sources and prediction algorithms.
3Measurement precision
If detailed product-specific indices are analyzed, then the accuracy of error analysis improves, but the time and resources required for analysis increase
Solution Approach 1:
The system performs preliminary data processing and integration before demand prediction analysis. The information processing unit pre-processes intra-company and extra-company information, organizing it into structured formats and identifying key patterns in advance. This preliminary action reduces the time required for detailed error analysis of product-specific indices by having data ready in an optimized state.
Solution Approach 2:
The patent replaces manual analysis processes with automated information processing and demand prediction units that use algorithms to analyze product-specific indices. This substitution of mechanical/manual analysis with automated computational systems enables detailed error analysis of multiple indices without proportionally increasing time and resource requirements.
Data Source
AI summary
An information processing system includes an index acquisition unit that acquires product-specific indices for managing accuracy of demand prediction related to a product handled by an object company, an intra-company information acquisition unit that acquires intra-company information related to the product inside the object company, an extra-company information acquisition unit that acquires extra-company information related to the product outside the object company, and an interpretation example generation unit that generates, as an interpretation example of the product-specific indices, a sentence including an interpretation example based on the intra-company information and the extra-company information, by using a large language model.