Global Supply Chain Anomaly Forecasting via Entity Model Merging
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Solution Overview
Problem
Limited information availability regarding global supply chain commodity flows leads to delayed awareness of disruptions, as current data systems lack granularity and geographic scope, resulting in manufacturers being unaware of issues until local discrepancies are detected.
Innovation Solution
A method that identifies and categorizes data records into generic field types (numeric, categorical, and date fields) to construct entity-specific models for forecasting imports and exports, which are then combined into a global supply chain model to predict data anomalies affecting specific entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If high-level port authority data is used for supply chain monitoring, then geographic scope is improved, but data granularity deteriorates
Solution Approach 1:
The patent segments the supply chain data into multiple levels: global port-level data from authorities, entity-specific import/export records, and container-level detailed information. This segmentation allows the system to maintain both broad geographic coverage through port data and fine granularity through entity-specific records, resolving the contradiction between scope and detail.
2Device complexity
If limited port authority information is used, then data collection complexity is reduced, but disruption detection timeliness deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing entity-specific import and export records in advance, organizing them with standardized field types before anomalies occur. This pre-structured data is ready for immediate analysis when disruptions happen, enabling timely detection without the complexity of real-time data collection during crises.
Solution Approach 2:
The patent implements feedback mechanisms where the anomaly detection model continuously learns from historical data patterns and refines its predictions. The system compares expected versus actual supply chain flows, providing feedback that improves detection accuracy over time while maintaining manageable data collection complexity through automated processes.
3Measurement precision
If entity-specific models are constructed and combined into global model, then anomaly detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges entity-specific anomaly detection models into a coordinated global supply chain model. Each entity model processes local import/export data independently, then their results are combined to provide comprehensive supply chain-wide anomaly detection. This merging approach improves overall accuracy while managing complexity through modular architecture where each entity model remains independent but contributes to the global picture.
Data Source
AI summary
A method, apparatus, system, and computer program code for forecasting a data anomaly to a supply chain. A plurality of data records is identified for a plurality of entities. The data records include import records and export records. The data fields in the data records are categorized into generic field types. The generic field types include numeric fields, categorical fields, and date fields. For each of the plurality of entities, an entity-specific model is constructed for forecasting imports and exports based on the generic field types. The entity-specific model for each of the plurality of entities is combined into a global supply chain model. Based on the global supply chain model, a data anomaly is forecast to a supply chain that is associated with a particular entity.