Supply Chain Risk Interface for Root Cause Analysis

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Solution Overview

Problem

Conventional supply chain management systems are inefficient and lack the ability to analyze large-scale data for identifying and mitigating risks, often focusing on predicting disruptive events rather than understanding root causes, and fail to provide tools for designing supply chains to address potential disruptions, while relying on incorrect assumptions about supplier risks and inventory management.

Innovation Solution

A supply chain management operating platform that collects, processes, and visualizes data from multiple sources to determine risk values and optimize network nodes, incorporating third-party data and advanced analytics to identify and mitigate risks, and provide actionable insights for supply chain design and management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional supply chain management systems are used, then basic supply chain operations can be maintained, but the systems are inefficient and cannot analyze large-scale data for identifying and mitigating risks

Engineering Contradiction:

Improverisk identification and mitigation capabilityVSAvoiddata processing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces conventional mechanical/manual supply chain management systems with an automated electronic platform that uses algorithms and computational methods to process supply chain data, identify risks, and generate mitigation strategies automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms supply chain data into actionable risk insights by changing the parameters of data analysis - moving from basic operational tracking to advanced analytics that evaluate multiple risk factors simultaneously and generate predictive risk assessments

Inventive Principle:

Principle #35Parameter changes

2Reliability

If conventional risk management approaches focus on predicting disruptive events, then some risk awareness can be achieved, but the ability to understand root causes is insufficient

Engineering Contradiction:

Improveroot cause analysis capabilityVSAvoidloss of underlying cause information

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The patent inverts the conventional approach by not just predicting disruptive events but by working backwards to identify and analyze the root causes of potential disruptions, examining underlying factors that lead to supply chain risks

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent performs preliminary analysis of root causes before disruptions occur, using historical data and pattern recognition to identify underlying issues that may lead to future disruptions, enabling proactive rather than reactive risk management

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If conventional supply chain management systems are used, then basic management functions can be performed, but tools for designing supply chains to address potential disruptions are not available

Engineering Contradiction:

Improvesupply chain design flexibilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent creates a multi-functional platform that combines supply chain design, risk assessment, data analysis, and mitigation strategy generation into a single integrated system, enabling multiple functions without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12518230B2Systems and methods for interfaces to a supply chain management system

Publication Date: 2026.01.06 JABIL INC

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.