Supply Chain Risk Management via AI-Driven Data Integration

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Solution Overview

Problem

Current risk management systems for supply chains face challenges in accurately assessing and mitigating risks due to siloed approaches, inadequate data processing, and lack of integration with external systems, leading to inaccurate and cumbersome risk evaluations.

Innovation Solution

A method and system utilizing an AI engine and machine learning to identify and generate risk data objects, predict inherent risks, and create actionable controls, integrated with a data lake and user interface for structured data processing and automated risk management processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional siloed risk assessment approaches are used, then each function can assess its own risks independently, but the overall risk assessment becomes inaccurate and incomplete due to lack of integration across supply chain functions

Engineering Contradiction:

Improverisk assessment accuracyVSAvoidintegration complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent merges risk assessment across multiple supply chain functions (sourcing, inventory, transportation, etc.) into a unified integrated system. The risk assessment module consolidates data from all functions and applies comprehensive risk models to evaluate overall supply chain risk, eliminating siloed assessments and providing accurate holistic risk evaluation.

Inventive Principle:

Principle #5Merging (Combining)

2Measurement precision

If comprehensive data processing is performed across all supply chain functions, then complete risk assessment is achieved, but the processing time and computational resources required become excessive

Engineering Contradiction:

Improverisk assessment completenessVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary data processing and validation at the point of data entry across all supply chain functions. Data is pre-processed, validated against schemas, and prepared in advance using Apache NiFi workflows. This preliminary action ensures data readiness before comprehensive risk assessment, reducing actual processing time while maintaining completeness.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The comprehensive data processing is segmented into modular stages: data collection from individual functions, pre-processing and validation, risk factor extraction, and final risk assessment. Each segment can be processed independently and in parallel, reducing overall processing time while maintaining complete risk assessment coverage.

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If manual risk assessment processes are used, then detailed evaluation can be performed, but the process becomes cumbersome and time-consuming

Engineering Contradiction:

Improverisk evaluation detailVSAvoidassessment speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical risk assessment processes with automated computational systems. Machine learning models automatically analyze supply chain data, identify risk factors, and generate risk assessments without manual intervention. The system maintains detailed evaluation capability through comprehensive data analysis while achieving high-speed automated processing, eliminating the trade-off between detail and speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Adaptability or versatility

If external integration with supply chain functions is implemented, then comprehensive risk coverage is achieved, but the system becomes inaccurate and cumbersome

Engineering Contradiction:

Improveintegration coverageVSAvoidrisk assessment accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent introduces Apache NiFi as an intermediary layer between external supply chain functions and the risk assessment system. NiFi handles data collection, validation, transformation, and routing from various supply chain functions, ensuring data quality and compatibility before feeding into risk models. This intermediary ensures comprehensive integration coverage while maintaining accuracy through systematic data processing and validation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11803792B2Risk management

Publication Date: 2023.10.31 NB VENTURES INC DBA GEP

AI summary

The present invention discloses a method, a system and a computer program product for risk management in supply chain. The invention includes structuring of risk data fields on an application user interface for creating risk data objects based on a SCM task. The invention includes predicting inherent risk associated with execution of the SCM task and generating control data fields on the application user interface for mitigating the inherent risk.