## 4 Results and Discussion
### Multiple-Failure Scenario  
If customer orders increase from 600,000 to 620,000 glass bottles, production must also increase from 3650 to 3800 bottles per hour in order to successfully meet the demand. However, as depicted in the causal diagram, higher production rates can lead to greater probability of machine failure. Figure 5 depicts a simulation scenario in which two machine failures occur in the production line, and the machine's spare parts are not in stock. The first failure occurs in hour 871 and lasts for 599 h. The second failure occurs in hour 5106 and lasts for 889 h. The two disruptive events have a direct impact on both the level of finished goods inventory and the rate of orders fully shipped to customers. Figure 4: Time of disruption in line of production without spare parts in inventory. Figure 5: Disruption time in line of production with multiple failures. We ran the model several times to determine its robustness and find out what could happen if three disruptive events occurred in BPE's production line within a year. As Figure 6 illustrates, the first disruption occurred in hour 78 and lasted for 754 h. The second disruptive event occurred in hour 4332 and lasted for 719 h. Finally, the third disruptive event happened in hour 7059 and lasted for 600 h.  
Since BPE claims to have endured a maximum of four disruptive events in a year, we ran the model several times until such scenario arose. As can be observed from Figure 7, the first disruption occurred in hour 97 and lasted for 696 h. The second disruptive event occurred in hour 3346 and lasted for 807 h. The third disruptive event occurred in hour 6015 and lasted for 619 h. Finally, the fourth disruptive event appeared in hour 7910 and lasted for 657 h. As can be observed from the graphs, each of the four disruptions occurred at a different time interval and had a different duration. Figure 6: Disruption time in line of production with three failures. Figure 7: Disruption time in line of production with four failures. Figure 8 compares the five aforementioned scenarios. During a disruptive event derived from a damaged or worn out part, BPE can meet 100% of its demand if the spare part needed to fix the machine is on-site. Nevertheless, if the spare part is out of stock, only 93% of customer orders can be fulfilled. If two, three, or four disruptive events occur in a year, and if the spare parts needed to fix the machines are out of stock, BPE can only fulfill 85%, 79%, and 71% of customer orders, respectively. In conclusion, two or more scenarios compromise BPE's 98%-100% complete delivery. As depicted in Figure 1, BPE's spare part procurement protocol comprises seven processes, whose duration may vary from one another. Table 2 shows how the duration of each process can affect the Complete Shipment indicator at BPE. For instance, with a Finished Product Inventory of 100,000 glass bottles and one mechanical failure, the indicator Complete Shipment can occur in an interval with a minimum value of 90.98% and a maximum value of 96.98%. Conversely, if two mechanical failures occur, Complete Shipment has an interval of 79.77% to 91.35%. If three mechanical failures happen, BPE is able to meet complete shipment in an interval of 69.25% to 86.69%. Finally, in the light of four mechanical disruptions, Complete Shipment is reached only in 60.35% to 81.78%. To evaluate and validate the maximum and minimum percentages discussed below, we ran 10 scenarios per failure, which also are shown in Table 2, and the values that occur are between the minimum and maximum values found.