Adaptive Supply Network Modeling for Global Response Coordination
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Solution Overview
Problem
Modern enterprises face challenges in managing dynamically adaptive supply networks due to the lack of global context information among autonomous subsystems, leading to inefficiencies in responding to local perturbations and difficulties in implementing optimal production schedules across the network.
Innovation Solution
A method and system that simulate the supply network using an exogenous model in an analytical modeling language, providing candidate solutions based on sensory data processing, identifying a satisfiable solution in a global context, transforming the endogenous model, and modifying the supply network accordingly to optimize operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous subsystems operate independently with local optimization, then ease of operation and modular flexibility are improved, but loss of global context information and coordination efficiency deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising a sensory data processing framework and event condition management system that mediates between autonomous subsystems. This intermediary collects data from sensors across the supply network, processes it through analytical models, and generates coordinated responses without requiring subsystems to directly communicate with each other, thus maintaining autonomy while reducing information loss
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the supply network is constantly monitored, analyzed through exogenous and endogenous models, and used to generate corrective actions. This feedback mechanism ensures that global context information is maintained and distributed back to subsystems, enabling them to adjust their local operations based on network-wide conditions
2Productivity
If rapid response to local perturbations is implemented, then productivity and adaptability are improved, but manufacturing precision and coordination across the network deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-defining event conditions and their corresponding response strategies through analytical models. When perturbations occur, the system can rapidly execute pre-planned responses based on the nature of the event, avoiding the need for complex real-time calculations that would slow down the response while maintaining precision through model-based decision-making
Solution Approach 2:
The system dynamically changes operational parameters based on event conditions detected in the supply network. By using analytical models to determine optimal parameter adjustments, the system can rapidly respond to perturbations while maintaining manufacturing precision, as the parameter changes are calculated to achieve both speed and accuracy objectives
3Adaptability or versatility
If data-driven model-based approaches are used, then decision-making quality and adaptability are improved, but device complexity and computational requirements worsen
Solution Approach 1:
The system segments the complex decision-making process into distinct functional modules: a sensory data processing framework for data collection, an exogenous model layer for high-level analytical modeling, an endogenous model layer for detailed operational modeling, and an event condition management system for coordinated response. This segmentation reduces device complexity by organizing computational tasks into manageable, independent components that can be developed and maintained separately
Data Source
AI summary
This disclosure relates generally to system and method for managing dynamically adaptive supply network. The method includes simulating, by an exogenous model, the supply network in an analytical modeling language using at least a data populated from the supply network through a sensory data processing framework. The exogenous model provides a plurality of candidate analytical solutions corresponding to an event condition associated with the supply network based on the simulation. Corresponding to the event condition in a global context of the supply network, a satisfiable solution is identified. An endogenous model corresponding to the supply network is modified based on the satisfiable solution to obtain a modified endogenous model. The modified endogenous model is transformed into a programming language to obtain an updated endogenous model. The supply network is modified as directed by the updated endogenous model.