Digital Twin Control Tower for Value Chain Data-to-Insight Conversion
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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
1Loss of information
If organizations collect and store large 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 increases, making it difficult to convert data into actionable insights
Solution Approach 1:
The patent creates digital twins - virtual replicas of physical entities, processes, and systems - that copy and simulate the behavior of real-world objects. These digital copies allow organizations to analyze, simulate, and gain insights from data without directly managing the complexity of the physical systems, thereby converting raw data into actionable insights while reducing management burden
Solution Approach 2:
The control tower platform serves as an intermediary layer between diverse data sources and organizational decision-making processes. It aggregates, standardizes, and processes data from multiple IoT devices and systems, transforming heterogeneous data into unified, actionable insights that can be easily consumed by stakeholders
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 control tower platform is designed as a universal system that can monitor and manage multiple types of value chain entities (manufacturing facilities, warehouses, distribution centers, transportation assets) through a single unified interface. This multi-functional approach provides comprehensive operational visibility without requiring separate complex systems for each entity type
Solution Approach 2:
By creating digital twins of physical value chain entities, the system provides comprehensive monitoring and control capabilities while managing complexity through virtual representations. These digital copies enable detailed tracking and analysis without the need for equally complex physical monitoring infrastructure
3Productivity
If organizations use traditional linear supply chain management approaches, then implementation is simpler, but the ability to respond to real-time changes and optimize operations across the value chain is reduced
Solution Approach 1:
The control tower platform enables dynamic, real-time monitoring and optimization of value chain operations through digital twins that continuously update to reflect current states. This allows organizations to adapt to changing conditions and optimize operations dynamically, moving from static linear approaches to dynamic responsive management
Solution Approach 2:
The system implements continuous feedback loops where data from physical entities is captured, analyzed, and used to generate insights and recommendations that feed back into operational decision-making. This feedback mechanism enables real-time optimization and continuous improvement of value chain operations
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.