## 2 Literature Review  
Supply chains become complex structures because of the large number of suppliers and vendors that take a part in it [15; 16], hence, any disruption at any echelon can severely affect the performance of the entire supply chain. As an example, in 2011, a strong earthquake hit Japan and caused a microcontroller manufacturing company to stop its operations temporally, and thus suspend any deliveries to Denso, Toyota's first-level part supplier. Consequently, Toyota suffered from severe production stoppages, whose side effects were only overcome three months following the earthquake [17]. Production disruptions can occur in key logistic processes of the supply chain when raw materials are lacking. In their research, [PERSON] et al. [18] developed a discrete event simulation model to assess the impact of supply and transportation disruptions on key corporate performance indicators. Also, [PERSON] et al. [19] proposed an agent-based and heuristic inventory replenishment approach for minimizing the effects of production disruptions on key indicators, including late orders and unfulfilled orders, among others. [PERSON] [20] used a discrete event simulation model to analyze recovery time after production disruptions, whereas authors in [21] analyzed supply disruptions in terms of their duration and impact. Then, the authors proposed a series of proactive and reactive strategies for supply disruption management using a cost minimization model. Additionally, [PERSON] et al. [22] analyzed both the probability of a disruption in oil industry disruption and the costs derived from said disruption. Disruptions in supply chains can also occur as a result of mechanical failures. In their research, [PERSON] and [PERSON] [23] examined a series of spare part-inventory management models and concluded that a company can both save significant costs and minimize production stoppages by not having to expedite a part when in fact it is already on-site in their warehouses. From a similar perspective, authors in [24] proposed a Bayesian model to improve maintenance of industrial machinery. Namely, the model is conceived as a decision-making tool for supporting the choice of a policy and procurement planning of spare parts. [PERSON] and [PERSON] [25] found that maintenance policies and control of spare part inventory are usually managed separately. Hence, companies tend to overstock spare parts to ensure their availability when required in the production process. The authors also claimed that developing a mathematical model of stochastic failures is extremely difficult. Thus, they proposed a simulation model to analyze and understand the relation between maintenance and spare part inventory. Researchers [PERSON], [PERSON], and [PERSON] [26] found that dual supply in assemble-to-order (ATO) environments could significantly minimize spare part inventory costs. Additionally, authors in [27] developed an evolutionary algorithm and a heuristic model to know the impact of sudden disruptions--namely their length--on both the production process and inventory. In the end, the authors found that costs and shortage parameters are essential for production process recovery. Finally, [PERSON] et al. [28] introduced a joint optimization of spare parts inventory and preventive maintenance. While minimizing CO\({}_{2}\) emissions, this approach is based on an optimal production plan achieved thanks to the HMMS model. Predicting machine failures, and thus potential production stoppages, as a result of damaged machine parts can be difficult. Hence, multiple research works propose simulation models to study and assess production disruption aspects, such as duration, costs, and impact, on both the supply chain and key corporate indicators. Following this trend, SD has become an important simulation methodology. If compared to mathematical programming methods, SD is a convenient decision-making tool that helps researchers analyze and thoroughly understand the dynamic behavior of the system being studied [29].