AI Control Tower Platform for Container Fleet Digital Twin Planning
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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, incorporating interfaces for feature access, network connectivity, adaptive intelligence, data storage, and monitoring facilities, along with applications for demand and supply chain management, enables enterprises to manage value chain network entities from origin to customer use, utilizing 5G networks, IoT systems, cognitive networking, and digital twins for automation and data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If organizations collect and store large amounts of data from IoT devices and various data sources, then the quantity of available information increases, but the complexity and volume of data management increases, making it difficult to convert data into actionable insights
Solution Approach 1:
The patent segments the complex data management system into distinct functional modules including data collection layer, data storage layer, data processing layer with machine learning models, and application layer. This modular architecture allows each layer to handle specific tasks independently, reducing overall system complexity while managing large volumes of data effectively.
Solution Approach 2:
The patent introduces intermediary components such as data lakes, data warehouses, and machine learning processing layers that act as mediators between raw data collection and actionable insights generation. These intermediaries organize, process, and transform raw data into structured information, making it manageable and convertible into insights without overwhelming the system.
2Ease of manufacture
If organizations use traditional data management approaches, then implementation is simpler, but the ability to convert data into actionable insights for timely operations is insufficient
Solution Approach 1:
The patent implements preliminary action by pre-processing and organizing data in data lakes and warehouses before analytical needs arise. Data is collected, cleaned, structured, and stored in ready-to-analyze formats using machine learning models, so when insights are needed, the processing is already complete or near-complete, reducing implementation complexity while maximizing insight generation.
Solution Approach 2:
The patent replaces traditional mechanical data management approaches with intelligent systems including machine learning models, artificial intelligence algorithms, and automated data processing pipelines. These intelligent systems automatically transform raw data into actionable insights without manual intervention, maintaining ease of implementation while significantly improving the conversion of data into useful information.
3Reliability
If organizations implement comprehensive monitoring and management systems across the value chain, then operational visibility improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent implements a universal data management platform that serves multiple functions across the entire value chain including data collection from diverse sources, storage in scalable infrastructure, processing through machine learning models, and generation of actionable insights. This multi-functional system provides comprehensive operational visibility while managing complexity through a unified architecture that handles various data types and operations through standardized processes.
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.