Value Chain Control Tower Using AI and 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, 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 technologies like 5G networks, IoT systems, cognitive networking, and digital twins.
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 amount of available data increases dramatically, but the complexity and volume of data overwhelm organizations, making it difficult to convert data into actionable insights
Solution Approach 1:
The patent segments the overwhelming data into structured categories (operational data, market data, supply chain data, etc.) and organizes it through a hierarchical data architecture with data lakes, data warehouses, and specialized databases. This segmentation transforms unmanageable data volume into organized, accessible information structures that can be efficiently queried and analyzed.
Solution Approach 2:
The patent introduces an intermediary layer of data integration platforms, APIs, and processing systems that mediate between raw data sources and end-user applications. This intermediary infrastructure includes data normalization layers, transformation services, and integration platforms that convert heterogeneous data formats into standardized structures, reducing complexity while preserving data quantity.
2Reliability
If organizations implement comprehensive monitoring and management systems for value chain networks, then operational control and awareness are enhanced, but the system complexity and infrastructure requirements increase
Solution Approach 1:
The patent implements a universal control tower platform that performs multiple functions including data collection, analysis, visualization, decision support, and execution monitoring across diverse value chain networks. This multi-functional system consolidates what would otherwise require separate specialized systems for each monitoring task, reducing overall system complexity while enhancing operational control through integrated capabilities.
Solution Approach 2:
The patent incorporates real-time feedback loops where monitoring data flows back to control decisions, which then adjust operational parameters. This closed-loop feedback mechanism includes automated alerting systems, performance dashboards, and decision support systems that continuously refine operations based on incoming data, enhancing reliability through adaptive control while managing complexity through automated feedback processing.
3Ease of manufacture
If organizations use traditional linear supply chain management approaches, then implementation is straightforward, but the ability to respond to complex market demands and convert data into insights is limited
Solution Approach 1:
The patent transforms static, linear supply chain processes into dynamic, adaptive systems that automatically adjust to changing conditions. This includes real-time demand sensing, dynamic route optimization, adaptive inventory management, and flexible production scheduling that respond to current market conditions rather than following predetermined plans, significantly improving operational efficiency while maintaining manageable complexity through automated decision rules.
Solution Approach 2:
The patent implements preliminary actions through predictive analytics and scenario planning that prepare organizations for future conditions before they occur. This includes demand forecasting, risk assessment, contingency planning, and pre-positioning of resources based on predicted future states, enabling faster response times and improved productivity without requiring complex real-time adjustments during critical events.
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.