Supply Chain Risk Interface With Drill-Down Node Visibility
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Solution Overview
Problem
Conventional supply chain management systems are complex, disorganized, and fail to provide efficient tools for designing supply chains to mitigate risks, often focusing on predicting disruptive events rather than analyzing underlying root causes, and they lack visibility into the entire supply chain, failing to utilize large-scale data from sources like social media and third parties to accurately assess risks.
Innovation Solution
A supply chain management operating platform that collects, processes, and visualizes data from multiple nodes, incorporating third-party data sources to determine risk values, provides drill-down capabilities, and offers real-time risk management and optimization, using advanced analytics and visualization to identify and mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supply chain management systems are used, then basic supply chain operations can be managed, but the systems become complex and disorganized, failing to provide efficient risk mitigation tools
Solution Approach 1:
The patent segments the supply chain risk management system into distinct modules: data collection module, data processing module, risk assessment module, and visualization module. Each module handles specific tasks independently, reducing overall system complexity while improving reliability through specialized processing at each stage.
Solution Approach 2:
The patent introduces an intermediary data processing layer that sits between raw data collection and risk assessment. This intermediary layer standardizes and structures data from multiple sources before analysis, simplifying the complexity of integrating diverse data streams while enhancing risk mitigation capability through systematic processing.
2Measurement precision
If manual risk management processes are used, then detailed risk assessment can be performed, but the processes are time-consuming and fail to provide real-time visibility
Solution Approach 1:
The patent replaces manual mechanical risk assessment processes with automated computational systems. Algorithms and data processing routines automatically analyze supply chain data, calculate risk metrics, and generate assessments without manual intervention, maintaining precision while dramatically reducing processing time.
Solution Approach 2:
The patent implements continuous automated data processing and risk assessment that operates without interruption. The system continuously collects, processes, and analyzes supply chain data in real-time, providing ongoing risk visibility rather than periodic manual assessments, thus eliminating time loss while maintaining measurement precision.
3Loss of information
If traditional data sources are used, then supply chain data can be collected, but visibility into large-scale data including social media and third-party sources is lacking
Solution Approach 1:
The patent creates a universal data collection framework that can handle multiple data types from diverse sources including traditional supply chain data, social media, and third-party sources. The system uses a unified data structure and processing approach that works across all source types, improving data visibility without proportionally increasing integration complexity.
Solution Approach 2:
The patent introduces an intermediary data standardization layer that receives data from multiple diverse sources and converts them into a unified format. This intermediary layer handles the complexity of integrating different data structures, protocols, and formats, thereby improving overall data visibility while containing integration complexity within a single modular component.
4Reliability
If comprehensive risk analysis is performed, then accurate risk identification can be achieved, but the system lacks tools for design-stage risk mitigation
Solution Approach 1:
The patent enables risk assessment and analysis tools to be integrated into the supply chain design stage rather than only implementation stage. By providing preliminary risk analysis capabilities during the design phase, the system allows organizations to identify and mitigate risks before supply chain operations begin, improving risk identification accuracy while making implementation easier through upfront planning.
Data Source
AI summary
Apparatus, system and method for supply chain management (SCM) system processing. A SCM operating platform is operatively coupled to SCM modules for collecting, storing, distributing and processing SCM data to determine statistical opportunities and risk in a SCM hierarchy. SCM risk processing may be utilized to determine risk values that are dependent upon SCM attributes. Multiple SCM risk processing results may be produced for further drill-down by a user. SCM network nodes, their relation and status may further be produced for fast and efficient status determination.