Personalized Bundling Platform With Real-Time Data Mesh Integration
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Solution Overview
Problem
Traditional ERP systems face inefficiencies due to data fragmentation, lack of effective data integration, and inadequate data security, leading to inaccurate decision-making and compliance challenges in complex distribution and supply chain environments.
Innovation Solution
An automated Personalized Bundling platform integrating a Real-Time Data Mesh (RTDM) and Single Pane of Glass (SPoG) UI, utilizing advanced AI and ML algorithms to streamline product and service bundling, ensure data security, and comply with evolving regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ERP systems are used to manage distribution and supply chain processes, then comprehensive data storage and departmental access are achieved, but data fragmentation and lack of real-time visibility occur
Solution Approach 1:
The patent merges multiple ERP systems and data sources into a unified data integration layer that consolidates fragmented data from different departments and systems. This integration layer uses standardized data models and real-time synchronization to provide comprehensive visibility while maintaining data accuracy across the entire supply chain network.
Solution Approach 2:
The patent introduces an intermediary data integration layer between traditional ERP systems and the bundling platform. This intermediary layer acts as a mediator that transforms, standardizes, and harmonizes data from multiple sources, enabling real-time visibility without compromising the reliability of source systems.
2Productivity
If traditional ERP systems are used for data management, then centralized data storage is achieved, but data integration capabilities and information flow efficiency deteriorate
Solution Approach 1:
The patent segments the data integration architecture into distinct modular layers: data collection layer, transformation layer, standardization layer, and consumption layer. Each layer performs specific functions independently, reducing overall system complexity while improving information flow efficiency through specialized processing at each stage.
Solution Approach 2:
The patent creates a universal data integration layer that handles multiple data types, sources, and destinations through standardized interfaces and protocols. This multi-functional layer simplifies integration complexity by providing a single point of integration that can accommodate various ERP systems and data formats without requiring complex point-to-point connections.
3Reliability
If manual data transformation processes are used to standardize data, then data consistency is improved, but processing time and decision-making delays increase
Solution Approach 1:
The patent replaces manual mechanical data transformation processes with automated electronic data transformation and standardization systems. These automated systems use predefined transformation rules, validation algorithms, and real-time processing capabilities to standardize data instantly, eliminating delays associated with manual intervention while maintaining data consistency.
Solution Approach 2:
The patent implements preliminary data standardization and validation rules that are pre-configured in the system. Data is transformed and standardized automatically as it enters the system, before it reaches the bundling platform. This preliminary action ensures data consistency is established upfront, eliminating the need for time-consuming manual transformation later in the decision-making process.
4Reliability
If traditional ERP systems are used for supply chain management, then basic data security features are provided, but robust security capabilities and compliance adaptability are insufficient
Solution Approach 1:
The patent implements dynamic security and compliance management that automatically adapts to changing regulations and threats. The system uses configurable security policies, real-time threat detection, and automated compliance rule updates that can be adjusted without system reconfiguration. This dynamic approach provides robust security while maintaining high adaptability to evolving compliance requirements.
Data Source
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AI summary
Computerized systems and methods are described for executing personalized bundling processes. Methods include receiving user inputs specifying preferences for product bundles and utilizing a Real-Time Data Mesh (RTDM) to retrieve relevant real-time data. An Advanced Analytics and Machine Learning (AAML) Module analyzes these inputs alongside market data to generate personalized bundle recommendations. Recommendations are then displayed to the user via a Single Pane of Glass User Interface (SPoG UI) and, upon user confirmation, transferred as orders to a vendor system. Validation steps use algorithms within the AAML Module to ensure accuracy and relevance of the bundles. Real-time reports on user engagement and bundle success rates are generated. The system, accessible on multiple devices, integrates machine learning models that continually refine the bundling process based on evolving data patterns and user feedback, enhancing personalization and efficiency.