## 1 Introduction  
Random disruptions in key supply chain processes (i.e., procurement, production, and distribution) are neither controllable nor predictable [1; 2; 3]. This motivates decision makers to implement specific strategies to both ensure continuous production and timely fulfill orders. Overall, most of these strategies seek to increase inventory of raw materials, equipment, and machine parts [4]. However, there are carrying costs of holding and storing inventory over a period of time. To determine the right inventory levels to keep in key supply chain processes, companies must conduct proper analyses, supported by inventory management and control models, such as Economic Order Quantity (EOQ), ABC, XYZ, maximum, and minimum, among others [5; 6; 7; 8].  
The duration of a production disruption greatly depends on the source of said disruption (e.g., lacking materials). When machine parts fail or wear out and must be replaced, production disruptions might last considerably if spare parts are not in stock [9; 10]. In this study, we introduce the case of a glass bottle manufacturing company--here referred to as BPE (Bottle Processing Enterprise) for confidentiality purposes. BPE relies on the EOQ inventory model to procure production materials and equipment, such as gloves and helmets. For machine parts, however, the company cannot rely on the same inventory model, since it would be rather expensive to have inventory for all types of spare parts. Therefore, BPE manages a general inventory of those parts that often wear out naturally and must be replaced periodically during maintenance sessions. Nevertheless, machine failures resulting from damaged parts may occur randomly and suddenly, thus implying that BPE must make special purchases when specific spare parts are not in stock. BPE's special spare part purchase process disrupts the production flow. It comprises the following stages: (1) a machine part fails, (2) said part is technically inspected, (3) the company confirms the price of the spare part, (4) the corresponding department approves the price and delivery time set by the supplier, and (5) the spare part is installed in the machine.  
In order to understand the importance of spare part inventory, authors such as [PERSON] et al. [11] implemented forecasting techniques, whereas [PERSON] et al. [12] sought to determine the number of technicians necessary to save costs. Additionally, [PERSON] and [PERSON] [13] compared two inventory management models based on intervals between maintenance sessions. Likewise, [PERSON] and [PERSON] [14] used the Kolmogorov-Smirnov test to find the probability distribution that best fits spare part demand. In conclusion, current research works analyze the importance of maintenance and propose several types of models to predict machine part failures. However, we did not find evidence that current research has analyzed the effects of lacking spare part inventory on key supply chain processes, such as order shipping. To evaluate the hypothesis on the importance of spare part inventory during mechanical failures, we propose a System Dynamics (SD) model to both analyze the probability of failure of machine parts and determine the impact of lacking spare parts inventory on product deliveries. Additionally, we perform a sensitivity analysis with multiple variables to improve order shipping rates during production disruptions. The remainder of this paper is organized as follows: Section 2 discusses related work, whereas Section 3 describes our research methodology. Section 4 introduces the research results, whereas Section 5 states the research conclusions and our suggestions for future work.