Value Chain Digital Twin Planning for Inventory and Demand Prediction
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Solution Overview
Problem
Existing supply chain management systems lack efficient communication and data processing capabilities across various entities, leading to inefficiencies and challenges in demand prediction, inventory management, and logistics.
Innovation Solution
A method involving configuring secondary computing devices within a value chain network to communicate with a primary computing device, enabling them to receive primary commands and execute secondary commands, thereby optimizing system outputs and improving supply chain operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional linear supply chain management is used, then system simplicity is maintained, but communication efficiency and data processing capabilities across entities deteriorate
Solution Approach 1:
The patent segments the supply chain into multiple hierarchical levels (primary computing devices at enterprise operator level, secondary computing devices at entity level, and tertiary devices at operational level). Each level handles specific communication and data processing tasks independently, improving communication efficiency while distributing system complexity across manageable segments rather than concentrating it in a single linear structure.
Solution Approach 2:
The patent transitions from a traditional linear one-dimensional supply chain structure to a multi-dimensional hierarchical architecture. By adding vertical hierarchy (multiple computing device levels) and horizontal connectivity (networked entities across different levels), the system achieves improved communication efficiency and data processing while organizing complexity in a structured multi-dimensional framework.
2Productivity
If manual inventory management and demand prediction are used, then system simplicity is maintained, but productivity and resource allocation efficiency deteriorate
Solution Approach 1:
The patent implements self-service automation where secondary and tertiary computing devices automatically perform inventory management, demand prediction, and resource allocation tasks based on data collected from the network. The system autonomously processes information, generates predictions, and executes decisions without manual intervention, significantly improving productivity while the hierarchical structure manages automation complexity systematically.
Solution Approach 2:
The patent establishes feedback loops where data from operational entities flows upward through the hierarchical computing device levels, enabling continuous monitoring, analysis, and adjustment of inventory and demand parameters. This automated feedback mechanism improves productivity by enabling real-time decision-making while the structured hierarchy organizes the complexity of processing and responding to feedback from multiple sources.
3Loss of information
If centralized data processing is used, then system simplicity is maintained, but loss of information and processing time increase
Solution Approach 1:
The patent segments data processing across multiple hierarchical levels, with each computing device level processing specific types of data locally before aggregating results upward. This distributed segmentation preserves information by processing data closer to its source, reducing transmission losses, while the hierarchical organization manages processing architecture complexity systematically rather than requiring a monolithic centralized structure.
Solution Approach 2:
The patent adds vertical dimensionality to data processing architecture, creating multiple processing levels that handle different data types and processing stages. This multi-dimensional approach reduces information loss by enabling parallel processing at different hierarchical levels while organizing processing complexity in a structured framework rather than a flat centralized system.
Data Source
AI summary
A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.