Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning

2024-08-11

Algorithms

AI Summary

Identifying root causes of supply chain delivery delays remains challenging due to complex interdependencies and the lack of causal interpretability in conventional correlation-based analysis. Method: This paper proposes a novel root-cause attribution framework integrating causal discovery with reinforcement learning (RL). It improves the PC algorithm to construct a causal graph discovery module; designs a reward-driven RL policy network—first introducing RL into causal structure search to dynamically optimize graph learning; and incorporates a causal strength quantification mechanism for directional, measurable root-cause localization. Results: Experiments on real-world supply chain data demonstrate that the method successfully uncovers latent causal pathways—e.g., how transportation mode and order volume affect delivery status—with a 37% improvement in root-cause identification accuracy. The framework enables precise intervention and proactive risk management by delivering interpretable, causally grounded insights.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Managing delivery risks is a critical challenge in modern supply chain management due to the increasing complexity and interdependencies of global supply networks. Existing methods often rely on correlation-based approaches, which fail to uncover the true causes behind delivery delays. This limitation makes it difficult for supply chain managers to identify actionable factors that can mitigate risks effectively. To address these challenges, we propose a novel method that integrates causal discovery with reinforcement learning to identify the root causes of delivery risks. Unlike traditional correlation-based methods, our approach uncovers both the direction and strength of causal relationships between variables, allowing for more accurate identification of the key drivers behind delivery delays. By applying causal strength quantification, we further measure the impact of each factor on delivery performance. Using real-world supply chain data, our results demonstrate that the proposed method reveals hidden causal relationships between factors such as shipping mode, order size, and delivery status. These insights enable supply chain managers to implement more targeted interventions, significantly improving risk mitigation strategies.

Problem

• Supply Chain Efficiency
• Delivery Delay
• Risk Identification

Innovation

• Causal Discovery
• Reinforcement Learning
• Supply Chain Optimization