Enterprise Data Lake Control Tower for Real-Time Supply Chain Recalibration

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Solution Overview

Problem

Existing enterprise application (EA) and supply chain management (SCM) systems face challenges in connecting demand with supply due to data silos, cumbersome real-time data extraction and cleansing processes, and inaccuracies in data storage, leading to inefficiencies and increased costs.

Innovation Solution

A self-driven system that utilizes a data lake to store data from diverse sources, a control tower to manage data attributes, and an AI-based prediction and recommendation engine to automatically recalibrate application functions in real-time, addressing data silos and inaccuracies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data is stored in silos in existing ERP systems, then data storage is maintained, but real-time data access and collaboration are prevented

Engineering Contradiction:

Improvedata accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent merges previously siloed data storage into a unified data lake architecture that consolidates structured and unstructured data from multiple sources (ERP systems, supply chain partners, external sources) into a single centralized repository, enabling real-time access while maintaining data accuracy through unified governance

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces an intermediary layer consisting of data lakehouse architecture with unified query interfaces and collaboration platforms that mediate between diverse data sources and end-users, enabling real-time data access and collaboration without requiring structural modifications to existing ERP systems

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If data extraction and cleansing is performed manually on siloed data, then data accuracy can be improved, but the process becomes extremely cumbersome and time-consuming

Engineering Contradiction:

Improvedata accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements self-service data cleansing capabilities through automated data quality monitoring, validation rules, and self-healing mechanisms that automatically detect and correct data inaccuracies without requiring manual intervention, maintaining high data accuracy while dramatically reducing processing time

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical data extraction and cleansing processes with automated computational systems including AI-driven data quality algorithms, automated ETL pipelines, and intelligent data validation frameworks that continuously monitor and cleanse data in real-time

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If technical modifications are made to ERP system architecture to enable real-time data processing, then real-time collaboration is enabled, but system complexity increases

Engineering Contradiction:

Improvereal-time processing capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the system architecture into distinct layers: data ingestion layer, data lakehouse layer, processing layer, and application layer, allowing real-time processing capabilities to be added without modifying the core ERP system architecture, thus enabling productivity improvement while controlling complexity through clear separation of concerns

Inventive Principle:

Principle #1Segmentation

4Reliability

If data is cleaned and restructured outside the ERP system, then data quality improves temporarily, but data gets dirty again quickly and the process becomes increasingly expensive and difficult as data volume increases

Engineering Contradiction:

Improvedata qualityVSAvoidease of data maintenance

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The patent implements continuous data quality maintenance through ongoing automated cleansing operations, real-time validation rules, and continuous monitoring mechanisms that operate continuously within the unified data lake architecture, preventing data from becoming dirty again rather than relying on periodic external cleanup operations

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent introduces an intermediary data lakehouse architecture that sits between external data sources and the ERP system, providing a permanent home for cleaned and structured data with automated governance mechanisms that maintain data quality continuously without requiring repeated external intervention

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12242566B2Data lake and self-driven system for operating enterprise and supply chain applications

Publication Date: 2025.03.04 NB VENTURES INC

AI summary

The present invention provides self-driven Artificial Intelligence based system and method for operating one or more applications including enterprise application and supply chain management applications. The system includes centralized data lake for storing data received from plurality of distinct sources, a control tower configured for sensing change in attribute of the received data and determining impact of the change on plurality of functions of EA and SCM applications.