Graph Neural Network Supply Prediction for Complex Networks
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Solution Overview
Problem
Accurate supply and inventory predictions in complex supply chain networks are challenging due to dynamic node interactions, cascading supply delays, resource availability, production, and logistic capabilities, leading to inaccuracies in planned shipments.
Innovation Solution
The Graph-based Supply Prediction (GSP) model uses an attention-based graph neural network (GNN) to predict supplies, inventory, and imbalances by analyzing graph-structured historical data, demand forecasting, and original supply plan inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional S&OP shipment plans are used, then planning simplicity is maintained, but prediction accuracy deteriorates due to limited state considerations and inability to account for dynamic interactions
Solution Approach 1:
The patent introduces a Graph Neural Network (GNN) model as an intermediary between traditional S&OP planning and actual supply execution. The GNN processes graph-structured historical data, demand forecasts, and supply plans to generate corrected predictions, acting as a mediator that translates limited traditional plans into accurate supply and inventory predictions while accounting for dynamic node interactions and cascading delays
Solution Approach 2:
The patent transforms the supply chain planning problem from traditional tabular data into graph-structured data with nodes representing supply chain entities and edges representing relationships. This dimensional change allows the model to capture multi-hop dependencies and dynamic interactions that traditional flat planning methods cannot represent, significantly improving prediction accuracy
2Measurement precision
If detailed analysis of dynamic node interactions and cascading delays is performed, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing historical supply chain data into graph structures and pre-training the GNN model on historical patterns. This allows the model to quickly make predictions on new data without performing complex real-time analysis of all node interactions, reducing processing time while maintaining accuracy
Solution Approach 2:
The patent implements a dynamic GNN model that adapts to changing supply chain conditions. The model learns from historical data how node interactions and cascading delays evolve over time, allowing it to make accurate predictions without explicitly calculating all dynamic interactions for each prediction, thus balancing accuracy with computational efficiency
Data Source
AI summary
Successful supply chain optimization must mitigate imbalances between supply and demand over time. To successfully perform supply planning for optimal and viable execution, the predictability for both demand and supply is essential. However, in complex supply chain networks with numerous nodes and edges, accurate supply predictions are challenging due to dynamic node interactions, cascading supply delays, resource availability, production, and logistic capabilities. Consequently, supply executions often deviate from their initial plans. A Graph-based Supply Prediction (GSP) probabilistic model is presented. The attention-based graph neural network (GNN) model predicts supplies, inventory, and imbalances using graph-structured historical data, demand forecasting, and original supply plan inputs. The experiments, conducted using historical data from a global consumer goods company's large-scale supply chain, demonstrate that GSP significantly improves supply and inventory prediction accuracy, potentially offering supply plan corrections to optimize executions.