IoT Control Tower Platform Using Digital Twins for Logistics Decisions
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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 vast amounts of data from IoT devices and various data sources, then the availability and quantity of information for decision-making is improved, but the complexity and difficulty of managing and processing this data increases
Solution Approach 1:
The patent introduces an enterprise management platform as an intermediary layer between raw data sources and decision-making processes. This platform aggregates, standardizes, and processes data from multiple IoT devices and sources, transforming raw data into actionable insights while managing the complexity of data integration and processing.
Solution Approach 2:
The patent creates digital twins - virtual copies of physical assets, processes, and systems - that replicate the state and behavior of physical entities. These digital replicas allow organizations to analyze, simulate, and optimize operations without directly manipulating physical systems, reducing the complexity of managing real-world data while maintaining accurate representations.
2Speed
If organizations process and analyze large volumes of data in real-time, then the speed and timeliness of decision-making is improved, but the computational resources and system complexity required increase
Solution Approach 1:
The patent divides the enterprise management platform into modular microservices and functional components that can independently process different aspects of data analysis. This segmentation allows parallel processing of data streams, enabling real-time analytics while distributing computational complexity across multiple specialized modules rather than requiring a monolithic complex system.
Solution Approach 2:
The patent implements self-learning and self-optimizing algorithms that automatically adapt to data patterns and improve processing efficiency over time. These systems autonomously adjust their computational requirements, reducing the need for manual system configuration and complexity management while maintaining high-speed real-time processing capabilities.
3Loss of information
If organizations implement comprehensive monitoring and control of value chain networks, then the visibility and control over operations is improved, but the complexity of managing interconnected systems increases
Solution Approach 1:
The patent creates a virtualized representation of the entire value chain network through digital twins and virtual models. These digital replicas provide comprehensive visibility into operations across the network while simplifying management by allowing controllers to interact with virtual representations rather than directly managing complex physical interconnected systems.
Solution Approach 2:
The enterprise management platform serves as an intermediary layer that abstracts the complexity of interconnected value chain networks. It consolidates monitoring data from multiple sources, standardizes information formats, and provides unified control interfaces, thereby improving information visibility while shielding users from the underlying network complexity.
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.