# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 3 Results and Discussion
### Observations on Asset Availability and Supply Lead Time  
The asset availability is the most important performance indicator from the point of view of the asset operator. The mean availability rate under naive physical warehouse conditions (scenario 1) is about 87%, which means that the asset was unavailable and required waiting for spare parts having a long supply lead time. The supply lead time for this scenario is on average about 799.79 h. However, the supply lead time histogram in Figure 4 illustrates the lead time distribution and categories. It can be seen that there are four categories with different supply lead times of 600, 700, 800-1000, and 1100 h, indicating that the lead time is scattered and not resilient.  
The mean availability rate under the digital warehouse conditions (scenario 2) is about 72%, which means that the asset was unavailable and waiting for spare parts. In this case, although the supply lead time is shorter than in scenario 1, the no-stock condition made the situation of asset availability worse. The supply lead time histogram in Figure 5 illustrates the lead time distribution, which is almost uniform and resilient (between 150 and 300 h). The mean availability rate under the enhanced physical warehouse conditions (scenario 3) is about 98.4%. This high availability rate was secured by a higher level of safety stock. In this case, although the supply lead time is longer than in scenario 2, the high safety stock condition made the situation of asset availability much better. The supply lead time histogram, shown in Figure 6, illustrates that the values are almost scattered and not resilient (between 450 and 1100 h).  
The mean availability rate under the hybrid warehouse conditions (scenario 4) is about 98.5%. This high availability rate was secured by a higher level of safety stock and a short supply lead time. The supply lead time histogram in Figure 7 illustrates an almost normal and resilient distribution (between 150 and 300 h).  
Figure 4: Asset availability and supply lead time histograms for scenario 1. _The availability histogram (**left**) has two values—value 1 (available) and 0 (unavailable)—and the mean value shows the availability rate as percent. The supply lead time histogram (**right**) present the distribution and mean value in hours._  
Figure 5: Asset availability and supply lead time histograms for scenario 2.  
Figure 6: Asset availability and supply lead time histograms for scenario 3.  
Figure 7: Asset availability and supply lead time histograms for scenario 4.  
Comparing Figures 4 and 5, one can observe that ad hoc stock with a long supply lead time (799 h) provides better availability than on-demand manufacturing with a shorter supply lead time (209 h). Asset availability with the on-demand manufactured spare parts policy and no stock dropped to 71.9%, whereas it was 87% for ad hoc stocking with a long supply lead time. In addition, comparing Figures 4 and 6, one can observe that minimal stocking is much better than ad hoc stocking to match the desired asset availability--it has the potential to increase availability from 87% to 98.4%. However, with minimal stock options, the number of spare parts in stock increases from three (ad hoc stock) to seven spare parts. Increasing the number of stocks will definitely have an impact on the holding cost; in this case, the cost of holding increases almost three times. The increase in holding costs is not just due to the increase in the stock, but also because increasing the stock leads to better availability and more operating hours, which may lead to more failures and more spares. Furthermore, comparing Figures 6 and 7, one can observe that on-demand manufacturing reduces the minimal stock while keeping the asset availability as high as possible. In addition, on-demand manufacturing cut off the CO2 emissions related to global logistic operations.