Digital Twin Control Tower for AI Sensor Placement in Value Chains
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Solution Overview
Problem
The increasing complexity and volume of data from IoT devices and various data sources overwhelm organizations, making it difficult to convert data into actionable insights for timely and efficient operations in value chain networks.
Innovation Solution
A cloud-based management platform with a micro-services architecture, including interfaces for configuration, network connectivity facilities like 5G and IoT systems, adaptive intelligence facilities such as digital twins and robotic process automation, and data storage using blockchain, to manage value chain network entities from origin to customer use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If organizations collect and store vast amounts of data from IoT devices and various data sources, then the availability and completeness of information for decision-making is improved, but the complexity and volume of data management overwhelms organizational capabilities
Solution Approach 1:
The patent introduces an AI-based data management platform as an intermediary layer between IoT devices and organizational decision-making systems. This platform automatically collects, stores, processes, and analyzes data from multiple sources, transforming raw data into actionable insights. The intermediary handles data complexity internally while presenting simplified information to users, resolving the contradiction between comprehensive data availability and manageable complexity.
2Reliability
If organizations implement comprehensive monitoring and data collection systems across value chain networks, then operational visibility and control are improved, but the time and resources required to process and analyze data increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-analyzing data as it is collected from IoT devices and data sources. The AI platform performs initial filtering, aggregation, and pattern recognition immediately upon data ingestion, preparing insights in advance before they are needed for decision-making. This eliminates the need for time-consuming analysis when decisions are required, maintaining both high visibility and rapid response.
Solution Approach 2:
The patent replaces manual or mechanical data processing methods with AI-based automated systems. Machine learning algorithms automatically analyze complex operational data, identify patterns, and generate insights without human intervention. This substitution dramatically reduces the time and human resources required for data processing while maintaining or improving the quality of operational visibility.
3Ease of operation
If traditional linear supply chain management methods are used, then operational simplicity and ease of management are maintained, but the ability to respond to complex modern value chain challenges and convert data into actionable insights is reduced
Solution Approach 1:
The patent implements a universal AI-based management platform that performs multiple functions: data collection, storage, processing, analysis, visualization, and decision support. This single multi-functional system replaces numerous separate traditional management tools and processes, maintaining ease of operation through a unified interface while dramatically improving productivity through intelligent automation and advanced analytics across the entire value chain.
Data Source
AI summary
An information technology generally including a set of monitoring facilities that are configured to monitor the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.