Supply Chain Risk Diagnostics Using Third-Party Data
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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 relying on incorrect assumptions and lacking visibility into large-scale data, including social media and third-party data sources, which can indicate potential disruptions.
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 and optimize network efficiency, using advanced analytics and visualization to identify and mitigate risks in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supply chain management systems are used, then basic supply chain operations can be maintained, but the systems are complex, disorganized, and fail to provide efficient tools for designing supply chains to mitigate risks
Solution Approach 1:
The system segments supply chain data into multiple hierarchical levels (global, regional, local) and organizes third-party data sources into distinct categories (social media, news, weather, etc.). This segmentation allows complex risk assessment to be broken down into manageable components that can be processed independently and then integrated, reducing overall system complexity while improving reliability.
Solution Approach 2:
The patent introduces an intermediary processing layer that collects, standardizes, and analyzes third-party data from multiple external sources before integrating it with internal supply chain data. This intermediary layer acts as a buffer that simplifies the integration process and provides a unified interface for risk assessment, reducing the complexity of directly connecting numerous disparate data sources.
2Reliability
If manual supply chain risk management processes are used, then potential losses can be identified through formal processes, but the processes are time-consuming and lack real-time visibility
Solution Approach 1:
The system implements continuous automated data collection and analysis from third-party sources, replacing manual periodic assessments with ongoing real-time monitoring. Data is continuously ingested from social media, news outlets, weather services, and other sources, enabling uninterrupted risk identification and immediate detection of emerging threats without time loss.
Solution Approach 2:
The system incorporates feedback loops where analyzed risk data is continuously fed back into the supply chain management platform, triggering automated alerts and updates. This feedback mechanism enables real-time risk identification by comparing current data against historical patterns and thresholds, significantly reducing the time required to identify potential losses compared to manual processes.
3Loss of information
If traditional data sources are used for supply chain analysis, then internal operations can be monitored, but visibility into large-scale external data including social media and third-party sources is lacking
Solution Approach 1:
The system implements a universal data processing framework that handles multiple types of third-party data sources (social media, news, weather, market data) through a single integrated platform. This multi-functional approach consolidates what would otherwise require separate processing systems, reducing overall complexity while maximizing data visibility across all external information sources.
Solution Approach 2:
The system creates standardized data copies and representations from diverse third-party sources, transforming various external data formats into a unified internal structure. By copying and standardizing data from multiple sources rather than directly integrating each source, the system achieves comprehensive data visibility while simplifying the processing complexity through a consistent data model.
4Measurement precision
If conventional risk assessment methods are used, then supplier attributes can be measured, but the methods rely on incorrect assumptions and fail to leverage third-party data sources
Solution Approach 1:
The system dynamically changes assessment parameters by incorporating real-time third-party data that reflects actual external conditions (social sentiment, news events, weather patterns) rather than relying on static historical assumptions. This parameter adjustment allows risk assessment to adapt to current realities, improving measurement precision while demonstrating flexibility in utilizing diverse data sources.
Solution Approach 2:
The risk assessment methodology transitions from static conventional methods to dynamic adaptive assessment that continuously incorporates new third-party data. The system dynamically adjusts risk evaluations based on real-time information from external sources, enabling both precise measurement through current data and versatility in adapting to different data source types and emerging risk patterns.
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.