Auto-discovery Reasoning Knowledge Graphs Supply Chain Climate Risks
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Solution Overview
Problem
Existing supply chain optimization techniques fail to account for mid-term and long-term disruptions due to climate changes and disruptive events, such as floods and heatwaves, and do not provide meaningful insights for proactive risk optimization.
Innovation Solution
The auto-discovery of reasoning knowledge graphs (KGs) in supply chains using spatiotemporal queries, which analyze climate and disruptive event parameters to generate a knowledge graph that predicts demand and provides explanations for mid-term and long-term risks, enabling proactive decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional supply chain optimization techniques are used, then short-term demand forecasting is achieved, but mid-term and long-term disruptions due to climate changes and disruptive events cannot be accounted for
Solution Approach 1:
The system dynamically adapts to different time horizons and disruption types by adjusting the knowledge graph structure and analysis parameters. It transitions from static short-term forecasting to dynamic mid-term and long-term risk assessment, enabling the supply chain model to respond flexibly to changing conditions including climate changes and disruptive events
Solution Approach 2:
The patent introduces a new dimension of temporal analysis by extending from short-term to mid-term and long-term forecasting. It adds spatial and environmental dimensions through climate parameters and disruptive event factors, creating a multi-dimensional knowledge graph that captures complex supply chain risks across multiple scales
2Productivity
If existing demand forecasting methods are applied, then operational efficiency is maintained, but meaningful insights for proactive risk optimization are not provided
Solution Approach 1:
The system implements feedback loops where the knowledge graph continuously learns from new data, user interactions, and prediction outcomes. This feedback mechanism enables the system to refine its risk assessments and provide increasingly accurate proactive insights while maintaining operational efficiency through automated learning and adaptation
Solution Approach 2:
The knowledge graph serves as an intermediary layer between raw supply chain data and decision-making processes. It transforms complex climate and disruptive event data into meaningful risk insights, bridging the gap between operational efficiency requirements and strategic risk optimization needs
3Reliability
If comprehensive climate and disruptive event parameters are analyzed, then mid-term and long-term risk insights are generated, but system complexity increases
Solution Approach 1:
The system segments the complex analysis into distinct modules: climate parameter analysis, disruptive event analysis, knowledge graph construction, and prediction generation. Each module handles specific aspects of the analysis independently, reducing overall system complexity while maintaining comprehensive risk assessment capabilities across mid-term and long-term horizons
Data Source
AI summary
Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.