Supply Chain Risk Visualization via Geographic Aggregation
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Solution Overview
Problem
Current supply chain risk management systems lack comprehensive and visualized methods to assess and present data on supply chain conditions, including revenue impact and risk scores, across geographic regions, leading to inadequate resilience and disruption management.
Innovation Solution
A computer-automated method and system that aggregates supply chain data, including revenue impact and risk scores, to provide graphical representations on maps and scatter plots, and calculates a single-valued measure of supply chain resiliency by analyzing parts data, supplier performance, and location risks, enabling enterprises to identify vulnerabilities and optimize risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If supply chain risk management is performed using traditional methods, then risk assessment can be conducted, but comprehensive visualization and geographic aggregation of supply chain conditions are lacking
Solution Approach 1:
The patent transforms supply chain risk data from traditional tabular formats into geographic spatial representations by mapping suppliers, manufacturing sites, and distribution centers onto geographic coordinates. This dimensional transformation enables visualization of supply chain conditions across geographic regions, allowing executives to see risk concentrations and vulnerabilities that were previously invisible in non-spatial data formats.
Solution Approach 2:
The evaluation system integrates multiple functions into a single platform: data collection from diverse sources (suppliers, manufacturing sites, distribution centers), risk calculation across multiple dimensions (financial, operational, geographic), and visualization through multiple chart types (geographic maps, scatter plots, bar charts). This multi-functional system eliminates the need for separate tools for each analysis type.
2Measurement precision
If detailed supply chain data is collected and analyzed, then comprehensive risk assessment is achieved, but the complexity of data processing and presentation increases
Solution Approach 1:
The system calculates risk scores for each supplier and site based on collected data, then feeds these scores back into the visualization layer where they drive the positioning and sizing of graphical elements on maps and charts. This feedback loop automatically updates presentations as underlying data changes, maintaining precision without requiring manual intervention.
Solution Approach 2:
The patent introduces an intermediate processing layer that aggregates raw supply chain data into standardized risk metrics and geographic coordinates before presentation. This intermediary layer transforms complex multi-source data into a unified format suitable for visualization, simplifying the interface between data collection and presentation while preserving measurement precision.
3Reliability
If supply chain risks are assessed across multiple geographic regions, then comprehensive risk visibility is improved, but the complexity of data aggregation and visualization increases
Solution Approach 1:
The system divides the global supply chain into discrete geographic segments representing individual countries or regions. Each segment is evaluated independently with its own risk metrics, then aggregated into regional totals. This segmentation allows comprehensive multi-regional assessment while managing complexity through modular processing of individual geographic units.
Solution Approach 2:
The patent combines multiple data dimensions (supplier risk scores, manufacturing site risks, distribution center risks, geographic location data) into unified geographic aggregations. By merging these diverse data types into a single geographic framework, the system achieves comprehensive risk visibility across regions without requiring separate analysis systems for each data type.
4Adaptability or versatility
If risk scores and revenue impact are calculated for each supply chain element, then prioritization capability is enhanced, but computational complexity increases
Solution Approach 1:
The system transforms qualitative risk factors (supplier stability, location risk, operational vulnerabilities) into quantitative risk scores and revenue impact metrics. By changing the parameter representation from descriptive to numerical, the system enables mathematical aggregation and comparison across diverse supply chain elements, enhancing prioritization capability through standardized measurable parameters.
Data Source
AI summary
The present application is directed to, among other things, a computer-automated method of presenting data relating to a supply chain. The method may include using stored parts data of the enterprise, including content of a bill of materials for at least one of a product or a group of products, and, for each part in the bill of materials and a list of approved sources for such part, risk data associated therewith. The method may include using stored supplier data. The method may include computing supply chain data, including at least one of revenue impact and risk score, corresponding to the at least one of the product and the group of products, of the enterprise, wherein the data is aggregated according to geographic region. The method may include serving graphical information wherein the computed supply chain data is represented on a map on the basis of geographic region.