Value Chain Control Tower Using Digital Twins for 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 vast amounts of data from IoT devices and various data sources, then the quantity and variety of available information increases, but the complexity and volume of data management overwhelms organizational capabilities
Solution Approach 1:
The patent introduces a cloud-based control tower platform as an intermediary system that sits between the diverse data sources (IoT devices, enterprise systems, third-party sources) and the organizational users. This platform aggregates, standardizes, and processes data from multiple sources, converting raw data into actionable insights through analytics engines and digital twins, thereby managing data complexity while preserving data quantity and variety
Solution Approach 2:
The control tower platform is segmented into multiple functional modules including data ingestion layers, analytics engines, digital twin environments, and user interface layers. This segmentation allows different components to handle specific aspects of data management independently, reducing overall system complexity while maintaining the ability to process vast quantities of diverse data
2Reliability
If organizations implement comprehensive monitoring and management systems for value chain networks, then operational visibility and control improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent creates digital twins - virtual copies of physical assets, processes, and value chain entities - that replicate their counterparts' behavior and characteristics. These digital copies provide comprehensive operational visibility and control capabilities without requiring complex physical monitoring infrastructure, as the digital twins can be monitored and controlled through software interfaces
Solution Approach 2:
The control tower platform is designed as a universal system that can monitor and manage diverse value chain entities (manufacturing facilities, logistics networks, supply chain partners) through a single integrated interface. This multi-functional platform reduces implementation complexity compared to deploying separate specialized systems for each value chain component
3Productivity
If organizations process and analyze large volumes of data in real-time, then decision-making speed and operational efficiency improve, but computational resources and processing complexity increase
Solution Approach 1:
The patent implements predictive analytics and simulation capabilities within the digital twin environment that perform preliminary analysis of potential outcomes before actual decisions are made. This allows organizations to pre-process and evaluate multiple scenarios, reducing the computational burden during real-time decision-making while maintaining high decision-making speed
Solution Approach 2:
The system replaces traditional mechanical data processing approaches with cloud-based computational resources and AI/ML algorithms. This substitution enables real-time processing of large data volumes by leveraging distributed cloud computing power rather than relying on on-premises hardware infrastructure, reducing local processing 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.