Supply Chain Graph Model Generation for Adaptive Relationship Queries
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Solution Overview
Problem
Existing supply chain and operations data are typically stored in tabular structures that require complex merge queries for meaningful analysis, and there is a lack of a technical solution for dynamically adapting to varying database structures and adding new information.
Innovation Solution
A system that transforms supply chain and operations data into a graph data model, allowing for the creation of relationships between entities at the individual record level, with nodes and edges forming human-readable sentences, and enabling efficient queries and machine learning applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If supply chain data are stored in tabular structures, then data storage is straightforward, but complex merge queries are required for meaningful analysis
Solution Approach 1:
The patent segments supply chain data into distinct graph entities (suppliers, manufacturers, distributors, retailers, products) with defined relationships. This segmentation transforms the monolithic tabular structure into modular graph components, enabling simpler queries that can target specific entity relationships without complex joins across multiple tables.
Solution Approach 2:
The patent introduces graph database technology as an intermediary between data storage and analysis requirements. The graph database serves as a mediator that natively supports relationship queries through its data model, eliminating the need for complex SQL merge queries while maintaining straightforward data ingestion capabilities.
2Reliability
If traditional database structures are used, then data storage is established, but there is a lack of dynamic adaptation to varying database structures
Solution Approach 1:
The patent implements a dynamic graph schema that can adapt to varying supply chain data structures. The graph model allows entities and relationships to be added, removed, or modified without restructuring the entire database, enabling dynamic adaptation to changing data requirements while maintaining stable core relationships.
Solution Approach 2:
The patent creates a universal graph data model that can accommodate multiple types of supply chain entities and relationships within a single framework. This universal structure can represent diverse data formats and relationships (suppliers, products, exchanges, contracts) using the same graph primitives, providing both stability and versatility.
3Ease of manufacture
If tabular data structures are used, then data storage is simple, but efficient queries and pattern identification are difficult
Solution Approach 1:
The patent transitions from two-dimensional tabular data structures to a multi-dimensional graph structure where entities and relationships exist in additional dimensions. This dimensional change enables efficient traversal of relationships (e.g., finding all suppliers of a product through all distributors) by following graph edges rather than executing complex multi-table joins.
Solution Approach 2:
The patent replaces the mechanical query execution process of traditional databases (which requires assembling results from multiple tables through joins) with a native graph traversal mechanism. The graph database engine directly follows relationship paths in the data model, substituting the mechanical join operation with efficient graph algorithms for pattern identification and query resolution.
Data Source
AI summary
Systems, methods, and computer-readable storage media for graph model generation, and more specifically to generating graph models using supply chain data and operations. A system can receive, from a plurality of sources, sensor data, each piece of the sensor data including information associated with an exchange. The system can then parse, via at least one processor, the sensor data to identify components of each piece of the sensor data, resulting in parsed sensor data. The system resolves, via the processor, missing data within the parsed sensor data, resulting in parsed, resolved sensor data. The system can then map, via the at least one processor, the parsed, resolved sensor data to a graph data structure, the graph data structure having nodes and edges, and store the graph data structure in a graph database.