# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 1 Introduction  
In today's global economy, supply chain management (SCM) is essential for ensuring the efficient delivery of goods from suppliers to customers. Effective SCM helps businesses boost productivity, enhance customer satisfaction, and stay competitive [1]. For example, Apple Inc. has optimized its supply chain to lower costs and speed up delivery times, contributing to its success in the tech industry [2]. The importance of SCM became even more evident during the COVID-19 pandemic, which caused shortages of critical goods like medical supplies and PPE [3]. Similarly, industries like automotive manufacturing, which use just-in-time (JIT) inventory systems, were heavily impacted by the 2011 Tohoku earthquake, as disrupted supply chains halted global production [4]. These examples highlight how crucial SCM is in reducing disruptions, cutting costs, and ensuring business continuity. Managing supply chain risks has become increasingly challenging due to the complexity of modern supply chains, which often span multiple countries and involve numerous stakeholders. A key challenge is identifying the root causes of these risks. Traditional methods, which rely on expert judgment and statistical analysis, often fail to handle this complexity effectively [5]. These methods can produce misleading correlations, leading to misguided decisions. For example, delays may be wrongly attributed to specific suppliers when the real cause lies in broader issues such as transportation bottlenecks or production slowdowns [6]. External factors, like geopolitical tensions and natural disasters, can also cause unexpected disruptions. For instance, the U.S.-China trade war forced companiesto rethink their sourcing strategies due to global supply chain disturbances [7]. The increasing pressure to meet shorter delivery times, as seen with companies like Amazon, further complicates SCM. Amazon's same-day delivery service depends on advanced technologies and real-time data analytics, but maintaining this efficiency requires continuous adjustments to inventory and logistics systems [8]. As a result, supply chain managers must balance efficiency with risk management in a fast-changing environment, where unexpected disruptions can have significant financial and reputational consequences. To address these challenges, we propose a novel approach that integrates causal discovery with reinforcement learning to identify the root causes of delivery risks. Causal discovery uncovers relationships between variables using observational data, providing a clearer understanding of what drives delivery delays [9]. Reinforcement learning enhances this process by refining the causal graph to focus on the most relevant factors [10]. Unlike traditional methods, our model introduces a causal strength quantification mechanism, which measures both the direction and magnitude of relationships. This is essential for understanding how factors like supplier delays and bottlenecks impact decision-making and risk management. Our method also leverages domain-specific knowledge, making it more adaptable to the variability and dependencies common in supply chains. Alternative methods, such as the PC algorithm and Fast Greedy Search (FGS), have significant limitations. For instance, the PC algorithm assumes no hidden variables and outputs equivalence classes, leading to potential ambiguity [11]. FGS struggles with noisy and dynamic supply chain data [12]. Our approach, which combines reinforcement learning and causal strength quantification, overcomes these limitations by offering more accurate causal insights. By incorporating domain-specific knowledge, our model understands the directional nature of relationships, such as how product specifications influence demand but not the reverse. This allows the model to continuously refine the causal graph, improving its precision over time. For example, it can identify whether factors like shipping modes, shipment times, or order characteristics directly or indirectly contribute to delivery delays.