Temporospatial Knowledge Graph for Value Chain Risk Profiling
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Solution Overview
Problem
Current supply chain management systems are not integrated across value chains, making data and information sharing difficult, and they struggle to reason about risk from transitive dependencies, such as Tier-N relationships, due to the plurality of data sources required for comprehensive analysis.
Innovation Solution
A system and method using a temporospatial knowledge graph to gather and analyze value chain relationships between legal entities, people, systems, and assets, layering private data with end-user and public records data, and employing deep learning and machine learning for risk profiling and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If supply chain management systems capture data between directly interfacing organizations, then data collection is straightforward, but data and information sharing across value chains remains difficult
Solution Approach 1:
The patent merges multiple data sources including private data from paid vendors with end-user owned and public records data into a unified knowledge graph. This integration enables comprehensive value chain analysis by combining previously siloed information sources, allowing organizations to share and access data across the entire value chain rather than being limited to direct interfaces.
Solution Approach 2:
The knowledge graph system serves multiple functions simultaneously: it collects data from diverse sources, stores structured relationships, performs risk analysis, enables information sharing, and supports decision-making. This multi-functional platform resolves the contradiction by creating a universal system that handles both data collection ease and information sharing comprehensively.
2Device complexity
If existing systems focus on direct organizational interfaces, then system complexity is reduced, but the ability to reason about risk from transitive dependencies is lost
Solution Approach 1:
The patent extends the analysis from direct organizational interfaces to multiple tiers of suppliers and customers by adding temporal and spatial dimensions to the knowledge graph. This enables the system to reason about Tier-N relationships and transitive dependencies while maintaining structured data organization, thus improving risk analysis capability without overwhelming complexity.
Solution Approach 2:
The knowledge graph acts as an intermediary layer that structures and connects data from multiple sources, enabling risk analysis across transitive dependencies. Rather than directly managing complex multi-tier relationships, the system uses the knowledge graph as a mediator to organize, store, and query relationships between organizations, suppliers, and customers across multiple tiers.
3Measurement precision
If comprehensive value chain analysis requires multiple data sources, then analysis completeness improves, but data integration complexity increases
Solution Approach 1:
The patent changes the structural parameters of data integration by implementing a knowledge graph with standardized schemas and ontologies. This transformation converts unstructured or semi-structured data from multiple sources into a consistent format, enabling comprehensive value chain analysis while managing integration complexity through standardized data representation and relationships.
Data Source
AI summary
A system and method for gathering and analyzing the value chain relationships between legal entities, people, systems, and real and intangible assets using a temporospatial knowledge graph of the integrated value chain. The system provides the ability to layer private data from paid vendors with end-user owned and public records data to enable more comprehensive, contextualized and complete representations of the underlying value chain. Data analysis techniques, such as deep learning and machine learning, are performed on the knowledge graph and its underlying data set, in conjunction with simulation and modeling, to analyze the value chain, including generation of a risk profile for an entity's value chain and potential optimization options to remediate the identified risks.