# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 1 Introduction
#### Existing Works in Supply Chain on Delivery Risk  
To understand the current state of research on delivery risk management, we review existing works that focus on addressing delays in supply chains. This section examines both reactive and predictive approaches, highlighting their limitations in uncovering the root causes of delivery risks.  
Most existing studies concentrate on reactive measures to address delivery risks after they occur. For example, some strategies aim to optimize production processes to reduce delays during manufacturing [13; 14], while others focus on improving delivery strategies once delays are detected [15; 16]. However, these approaches typically overlook the root causes of delivery risks, concentrating instead on short-term solutions.  
Several studies have applied machine learning and deep learning techniques to predict delivery risks and improve supply chain operations. Algorithms like random forests, support vector machines (SVMs), and neural networks have been used to analyze historical data and identify patterns associated with delayed shipments [17; 18; 19; 20; 21]. While these models effectively predict delays, their black-box nature limits their ability to explain the underlying causes [22], making them less suitable for long-term strategic decisions.  
Optimization techniques have also been employed to enhance supply chain resilience. Methods such as adjusting inventory levels, rerouting plans, and rescheduling production in response to predicted delays have shown promise in minimizing disruptions [23; 24; 25]. However, these techniques focus primarily on mitigating the immediate effects of delivery risks, such as rerouting shipments or increasing inventory buffers. They do not address the root causes of the risks, which may lead to recurring issues [26].  
The use of real-time data and IoT technologies has further advanced the dynamic management of delivery risks. IoT-enabled systems offer real-time visibility into the supply chain, allowing managers to react quickly to emerging risks [27; 28; 29]. However, while these systems improve real-time decision-making, they still fall short in identifying the root causes of delays, limiting their effectiveness in providing long-term solutions [30]. This underscores the need for methods that not only predict delivery risks but also uncover their root causes, enabling supply chain managers to develop more strategic and sustainable risk mitigation strategies.  
Table 1 summarizes the existing methods and their respective results in the context of delivery risk management. The cited studies encompass a wide range of approaches, from predictive modeling to IoT-enabled real-time management, highlighting the current emphasis on mitigating delivery risks. Despite the advancements in prediction and short-term interventions, as previously mentioned, a significant gap remains in understanding the root causes of these risks. Addressing these underlying causes is essential for developing long-term strategies that enhance the resilience of supply chains.  
By integrating causal discovery with reinforcement learning, our proposed method aims to fill this gap by identifying not just the patterns associated with delays but the true drivers behind them. This approach offers supply chain managers the ability to implement more effective, targeted interventions that go beyond temporary fixes, ultimately ensuring a more sustainable and resilient supply chain system.  
**Paper Organization** In Section 2, we outline our proposed method for identifying the root causes of delivery risks. In Section 3, we conduct an exploratory data analysis (EDA) to identify key patterns and relationships in the data and analyze the experimental results. In Section 4, we discuss the potential business impact of our work. We conclude in Section 5 and discuss future work.