Automated Supply Chain Simulation Generation
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Solution Overview
Problem
Constructing large simulations of agent-based supply chain networks is challenging due to numerous rules and constraints, often requiring extensive engineering time and resources, making it uneconomical for many entities to build and execute simulations accurately reflecting real-world behavior.
Innovation Solution
An automated system generates supply chain simulations by processing historical data to create a network graph and agent rule models, allowing for quick simulation building and execution using natural language inputs, which can model both historical and hypothetical scenarios, optimizing supply chain performance across various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulations are constructed with numerous rules and constraints to accurately reflect real-world complexity, then measurement precision and reliability improve, but device complexity and loss of time increase significantly
Solution Approach 1:
The system automatically generates simulation rules, constraints, and agent behaviors by analyzing historical supply chain data without requiring manual coding. The simulation framework self-configures by learning patterns from historical data, allowing accurate representation of real-world complexity while eliminating the need for extensive manual rule construction
Solution Approach 2:
The patent replaces manual mechanical coding of simulation rules with an automated data-driven system. Instead of hand-coding constraints and agent behaviors, the system uses historical data to automatically generate these elements, substituting the mechanical process of rule creation with an automated computational approach
2Measurement precision
If simulations are constructed with numerous rules and constraints to accurately reflect real-world complexity, then measurement precision improves, but loss of time increases significantly
Solution Approach 1:
The system performs preliminary analysis of historical supply chain data to automatically generate simulation rules, constraints, and agent behaviors before the actual simulation is executed. This preliminary data processing and rule generation eliminates the need for time-consuming manual rule construction during simulation deployment
Solution Approach 2:
The simulation framework automatically configures itself by learning from historical data, generating all necessary rules and constraints without requiring manual intervention. This self-service capability dramatically reduces the engineering time needed to build accurate simulations
3Measurement precision
If manual coding is used to add peculiarities and deviations of specific locations and agents, then measurement precision improves, but device complexity and loss of time increase
Solution Approach 1:
The system automatically captures and models the unique characteristics and deviations of specific locations and agents by analyzing their individual patterns in historical data. Each agent and location receives customized behavior rules generated from its specific historical performance, maintaining high accuracy without manual customization
Solution Approach 2:
The patent replaces manual coding of agent-specific rules with automated data-driven generation. The system learns unique behaviors and deviations for each agent and location from historical data, substituting the mechanical process of individual rule writing with automated pattern recognition and rule generation
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic generation of a supply chain simulation. The methods, systems, and apparatus include actions of obtaining supply chain data of a supply chain, generating a supply chain network graph that represents relationships between locations indicated by the supply chain data, determining classifications of the locations indicated by the supply chain data, determining agent rule models based on the supply chain data, and generating a supply chain simulation based on the supply chain network graph, the classifications of the locations, and the agent rule models.