AI Control Tower Platform for IoT Logistics Data Complexity
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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 network management.
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
1Quantity of substance
If organizations collect and store data from IoT devices and various data sources, then the quantity and variety of available data increases, but the complexity and volume of data management increases making it difficult to convert data into actionable insights
Solution Approach 1:
The patent introduces an AI system as an intermediary layer between raw data from IoT devices and decision-makers. This AI intermediary automatically processes, analyzes, and transforms vast quantities of raw data into actionable insights, thereby managing data volume without proportionally increasing management complexity.
Solution Approach 2:
The AI system performs self-service by automatically collecting, processing, and analyzing data without requiring manual intervention. The system autonomously converts raw data into insights, reducing the burden on human operators to manage data complexity while maintaining high data volume processing capabilities.
2Productivity
If manual processes are used for value chain network management, then system complexity is lower, but productivity and timeliness of operations decrease
Solution Approach 1:
The patent replaces manual mechanical processes with an AI-driven automated system. The AI system processes value chain network management tasks automatically, significantly improving productivity and timeliness. The complexity increase is offset by the intelligence and autonomy of the AI system, which handles complex decisions that would otherwise require extensive manual coordination.
3Measurement precision
If more sensors and data collection devices are deployed, then measurement precision and data availability improve, but device complexity and cost increase
Solution Approach 1:
The patent merges multiple sensor inputs and data sources into a unified AI processing system. By combining data from various sensors and IoT devices through a centralized AI platform, the system maintains high measurement precision while reducing the overall complexity of managing individual sensor networks. The AI system integrates and harmonizes data from diverse sources into a coherent whole.
Data Source
AI summary
A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.