AI Logistics Control Tower With Digital Twin Simulation
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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 volume and completeness of available data increases, but the complexity and difficulty of converting data into actionable insights increases
Solution Approach 1:
The patent introduces an AI system as an intermediary layer between raw data collection and decision-making processes. This AI intermediary automatically processes, analyzes, and converts complex multi-source data into actionable insights, eliminating the need for manual data processing while maintaining data completeness and volume.
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 from multiple IoT devices and sources into actionable insights, reducing the operational complexity burden on organizations while preserving full data utilization.
2Speed
If manual data processing and analysis methods are used, then system complexity remains manageable, but the speed and timeliness of converting data into actionable insights decreases
Solution Approach 1:
The patent replaces manual mechanical data processing methods with an automated AI system. This substitution dramatically increases the speed of converting data into actionable insights by eliminating human processing bottlenecks, while the AI system manages the increased complexity through automated algorithms and machine learning models.
Solution Approach 2:
The system changes the operational parameters from manual processing speeds to AI-driven automated processing speeds. This parameter change enables real-time or near-real-time conversion of data into insights, while the AI system's adaptive algorithms dynamically adjust to manage the complexity of processing large volumes of multi-source data.
3Reliability
If comprehensive data from multiple sources is collected, then the quality and completeness of insights improves, but the difficulty of managing and processing the data increases
Solution Approach 1:
The AI system serves as an intermediary that automatically manages the complexity of collecting and processing comprehensive data from multiple sources. It handles data integration, cleaning, and validation processes, thereby maintaining high insight quality while shielding users from the operational complexity of managing diverse data sources.
Solution Approach 2:
The AI system performs self-service by autonomously managing the complete data lifecycle from multiple sources. It automatically collects, processes, validates, and analyzes data to generate high-quality insights, eliminating the need for manual data management interventions while maintaining comprehensive data utilization for improved reliability.
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.