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Amazon ScienceDec 7, 2025This paper introduces a novel causal attribution framework for supply chain simulations that combines Shapley values with Gaussian process emulators to decompose simulation outputs into individual input effects. The approach addresses the challenge of explaining complex simulation outputs by quantifying the contribution of each input feature. Experiments on synthetic and real-world supply chain data demonstrate the framework's ability to efficiently identify root causes of anomalies.
Pinpointing the root causes of supply chain anomalies just got easier: a Shapley value-based attribution mechanism rapidly decomposes simulation outputs into individual input effects.
Enterprise-level simulation platforms model complex systems with thousands of interacting components, enabling organizations to test hypotheses and optimize operations in a virtual environment. Among these, supply chain simulations play a crucial role in planning and optimizing complex logistics operations. As these simulations grow more sophisticated, robust methods are needed to explain their outputs and identify key drivers of change. In this work, we introduce a novel causal attribution framework based on the Shapley value, a game-theoretic approach for quantifying the contribution of individual input features to simulation outputs. By integrating Shapley values with explainable Gaussian process models, we effectively decompose simulation outputs into individual input effects, improving interpretability and computational efficiency. We demonstrate our framework using both synthetic and real-world supply chain data, illustrating how our method rapidly identifies the root causes of anomalies in simulation outputs.
