Agnostic Data Format Architecture for Real-Time Vendor Data Integration

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Solution Overview

Problem

The global distribution industry faces challenges in distribution management, supply chain complexities, inventory control, SKU management, compliance issues, and evolving consumer expectations due to divergent data formats, data fragmentation, inefficient data processing, and security concerns, which hinder efficient vendor onboarding and customer experience.

Innovation Solution

Implementing agnostic data formats (ADFs) using AI and ML technologies to standardize and integrate diverse vendor data structures, coupled with a Single Pane of Glass (SPoG) and Real-Time Data Mesh (RTDM) for real-time data availability and visibility, enhancing supply chain management and customer interaction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional ERP systems and legacy systems are used to store and manage data, then data can be stored in various formats and systems, but data fragmentation and information silos occur, preventing real-time access to comprehensive insights

Engineering Contradiction:

Improvedata format compatibilityVSAvoiddata accessibility

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent merges data from multiple disparate systems (ERP, legacy systems, external providers) into a unified data structure that consolidates previously fragmented information into a single accessible repository, eliminating information silos while maintaining compatibility with various source formats

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The unified data structure serves multiple functions simultaneously: it stores data from diverse sources, provides real-time access to all users, maintains data integrity across different formats, and enables comprehensive analytics - replacing the need for multiple separate systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If data is stored in different formats across various systems, then vendor diversity can be supported, but data inconsistency and integration complexity increase

Engineering Contradiction:

Improvevendor data format supportVSAvoidsystem integration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The unified data structure acts as an intermediary layer between diverse vendor data formats and the distribution platform, translating and normalizing data from various sources into a consistent format without requiring changes to vendor systems or complex point-to-point integrations

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the parameter of data representation by transforming vendor-specific data formats into a standardized unified structure, altering how data is organized and stored while preserving the original information content and vendor autonomy

Inventive Principle:

Principle #35Parameter changes

3Reliability

If traditional data processing systems are used, then existing infrastructure can be maintained, but inefficient data processing and analysis prevent timely insights and decision-making

Engineering Contradiction:

Improvedata integrityVSAvoiddata processing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces traditional mechanical data processing systems with an AI-powered neural network that automatically ingests, processes, and analyzes data in real-time, substituting manual or rule-based processing with intelligent automated systems that deliver timely insights while maintaining data integrity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Adaptability or versatility

If bespoke automated systems are developed for each vendor, then vendor-specific data requirements can be met, but substantial time and resources are required for development and maintenance

Engineering Contradiction:

Improvevendor-specific data handlingVSAvoidvendor onboarding time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The unified data structure provides a universal solution that handles vendor-specific data requirements through a single platform, eliminating the need to develop separate bespoke systems for each vendor while still accommodating diverse data formats and requirements through its adaptable architecture

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12488365B2Systems and methods for managing agnostic data forms for vendors

Publication Date: 2025.12.02 INGRAM MICRO INC

AI summary

System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.