Commodity Price Forecasting via NLP and ML
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Solution Overview
Problem
Current systems lack a comprehensive method to predict the supply and cost of materials for manufacturing products, failing to incorporate diverse data such as public sentiment, competitors, and industry trends, making it difficult for businesses to make proactive decisions on raw material procurement.
Innovation Solution
A predictive system that uses both qualitative and quantitative models, including natural language processing, to forecast material prices and availability, aggregating data to provide recommendations for material ordering and manufacturing scheduling, based on historical data and market insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual monitoring of news sources is used to track geopolitical events and supply chain disruptions, then valuable insights into potential raw material shortages can be obtained, but the data is difficult to synthesize into useful predictions
Solution Approach 1:
The patent replaces manual monitoring and synthesis of news sources with automated natural language processing systems and machine learning algorithms. These computational systems automatically ingest, analyze, and synthesize data from multiple news sources, geopolitical event feeds, and supply chain databases to generate predictive insights about raw material availability and pricing without requiring manual data processing.
Solution Approach 2:
The system enables self-service through automated predictive analytics that continuously monitor and analyze market conditions, supply chain events, and geopolitical developments. The algorithms automatically generate forecasts and alerts without human intervention, allowing businesses to receive actionable predictions about material shortages and price fluctuations without dedicating staff time to manual research and synthesis.
2Measurement precision
If a predictive system incorporates diverse data sources including public sentiment and industry trends, then more accurate material price and supply predictions can be made, but the system complexity increases
Solution Approach 1:
The patent segments the complex predictive system into distinct functional modules: data collection modules that gather information from various sources (news APIs, geopolitical event feeds, supply chain databases), natural language processing modules that analyze text data, quantitative analysis modules that process numerical data, and prediction generation modules that synthesize results. This modular architecture manages system complexity by organizing diverse data processing functions into separate, manageable components that can be independently maintained and scaled.
Solution Approach 2:
The system introduces intermediary processing layers between raw data sources and final predictions. Natural language processing intermediaries translate unstructured text from news articles and reports into structured insights. Data normalization intermediaries harmonize diverse data formats from different sources. These intermediary layers simplify the integration of diverse data sources by providing standardized interfaces and preprocessing functions that reduce the complexity of directly combining heterogeneous data streams.
Data Source
AI summary
A method and system to predict the cost of a product. Materials necessary to manufacture a product are determined. Data related to the price of each of the materials required for the product over a predetermined period of time is collected. The price of each of the materials at a future time is predicted based on the collected data via a set of models. The prices are aggregated to determine the aggregate predicted cost of the product at a period of time in the future. A recommendation of materials to meet a future demand based on the predicted cost is determined.