Supply Chain Risk Visualization Platform
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Solution Overview
Problem
Conventional supply chain management systems are overly complex and disorganized, focusing on predicting disruptive events rather than analyzing underlying root causes, and fail to account for modern information sources like social media and accurately assess risks from small suppliers or sole source items, leading to inefficient risk management.
Innovation Solution
A supply chain management operating platform that collects, processes, and visualizes data from multiple sources to determine statistical risks and opportunities, using advanced analytics and visualization tools to provide actionable insights and optimize supply chain operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supply chain risk management systems use multiple layers of classification and focus on predicting disruptive events, then they can identify potential risks, but they become overly complex and disorganized
Solution Approach 1:
The patent segments the supply chain risk management system into distinct functional modules: data collection module, data processing module, risk assessment module, and visualization module. Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive risk identification capabilities through structured segmentation of the supply chain into nodes and relationships.
Solution Approach 2:
The patent introduces an intermediary data processing layer that collects and standardizes data from multiple sources (social media, supply chain databases, news feeds) before feeding it into risk assessment algorithms. This intermediary layer simplifies the interface between diverse data sources and the core risk management functions, reducing system complexity.
2Reliability
If conventional systems focus on predicting disruptive events rather than analyzing root causes, then they can prepare mitigation strategies, but they fail to identify actual risk sources accurately
Solution Approach 1:
Instead of starting with predictive models and working forward to identify risks, the patent inverts the approach by first collecting and analyzing comprehensive data from multiple sources, then using this processed information to assess risks. This backward approach ensures that risk identification is based on actual observed data and root cause analysis rather than theoretical predictions.
Solution Approach 2:
The patent implements feedback loops where risk assessment results are continuously refined based on actual supply chain performance data and emerging information from social media and news sources. This feedback mechanism improves both root cause analysis accuracy and prediction accuracy by learning from actual outcomes rather than relying solely on predictive models.
3Adaptability or versatility
If conventional supply chain management relies on traditional data sources only, then they maintain established processes, but they fail to account for modern information sources like social media
Solution Approach 1:
The patent creates a universal data collection framework that can handle multiple types of data sources simultaneously - traditional supply chain databases, social media platforms, news feeds, and market data. The system is designed to process and integrate these diverse sources through a common interface, enabling comprehensive information gathering without requiring separate systems for each data type.
Solution Approach 2:
The patent implements dynamic data collection capabilities that can adapt to emerging information sources in real-time. The system continuously monitors and incorporates new data types as they become available, such as social media sentiment analysis and real-time news feeds, ensuring the supply chain risk assessment remains current and complete without being constrained by traditional static data sources.
4Device complexity
If conventional risk management assumes highest risk resides with highest spend suppliers, then they simplify risk assessment, but they misidentify actual risk locations in the supply chain
Solution Approach 1:
The patent applies local quality assessment by evaluating risk factors specific to each supply chain node and relationship rather than applying a uniform assessment based on spend alone. Each supplier, manufacturer, and distributor is assessed according to its specific characteristics, data availability, and role in the supply chain, enabling accurate identification of risk locations regardless of spend level.
Solution Approach 2:
The patent changes the assessment parameters from spend-based metrics to multiple diverse parameters including data source quality, social media sentiment, news coverage, supply chain criticality, and node connectivity. This parameter transformation enables accurate risk identification across all suppliers regardless of spend level, revealing that small suppliers and sole source items often pose higher risks than conventional spend-based assumptions would suggest.
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.