Supply Chain Risk Identification Visual Model
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Solution Overview
Problem
Existing supply chain modeling solutions lack visibility into the complete chain of sites and transportation, and fail to identify or assess risks associated with the flow of materials, due to limited data access and tabular representation, which hinders effective risk management in global manufacturing enterprises.
Innovation Solution
A visual model of the supply chain network is created, using logical stations as nodes and logical transits as links, which accesses extensive supply chain data to identify and assess risks, including environmental, geopolitical, and economic factors, and presents risk values graphically, enabling visibility and risk assessment across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a tabular format is used to represent the supply chain network, then the data structure is simple and easy to implement, but visibility of the complete chain of sites and transportations is difficult
Solution Approach 1:
The patent transforms the supply chain network from a tabular (2D) representation to a graphical visual model that adds spatial dimensionality. Sites are represented as nodes and transportations as edges in a graph structure, enabling users to visually perceive the complete supply chain network layout, relationships between sites, and material flow paths that are impossible to comprehend in tabular format.
2Device complexity
If prior supply chain modeling solutions are used, then the system complexity is low, but risk identification and assessment capabilities are lacking
Solution Approach 1:
The patent merges the supply chain network visualization model with risk identification and assessment functionality into an integrated system. The visual model not only displays the supply chain structure but also overlays risk data, risk scores, and risk assessments directly on the graphical representation, enabling simultaneous visualization and risk analysis without requiring separate complex systems.
Solution Approach 2:
The patent introduces a risk assessment module as an intermediary that bridges the supply chain data and the visual model. This module processes supply chain data, calculates risk scores, and presents risk information through the visual interface, enabling risk identification capabilities while maintaining the relative simplicity of the underlying system architecture.
3Measurement precision
If extensive supply chain data is accessed for risk assessment, then risk identification accuracy is improved, but data access complexity increases
Solution Approach 1:
The patent creates a universal data access framework that handles multiple data sources (internal supply chain data, external risk data, site-specific data) through a single integrated interface. The system accesses diverse data types including supply chain network data, site data, transportation data, and external risk indicators through unified data collection mechanisms, reducing the complexity of accessing extensive data from multiple sources.
Data Source
AI summary
Computer systems and methods that identify and assess risk in a supply chain network. The systems and methods create a visual model of a supply chain network, which includes: (i) logical stations graphically representing the physical sites in the supply chain network, and (ii) logical transits graphically representing the transportation of materials between the represented physical sites. For each given logical station, the systems and methods identify risk values for risk categories associated with the physical site. The systems and methods identify the risk values based on physical conditions related to: (a) the physical site represented by the given logical station, (b) each physical site represented by a logical station positioned in a downstream supply chain path to the given logical station, and (c) each transportation represented by a logical transit positioned in the downstream supply chain path. The systems and methods generate dynamic graphical indications comparing the identified risk values for the risk categories and total risk values for the represented physical sites and transportations.