# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 3 Results and Discussion  
This section presents and illustrates the results of the four scenarios. The results will then be used to support SPIR engineers in the worksheet and determine the most value-driven spare parts policy. It is worth noting that all-time plots in this section are in hours and for the entire lifetime (20 years, which is 175,200 h), and all cost figures are in Norwegian drone (NOK). The results of the simulated scenarios are summarized in Table 2. The mean availability as a percentage and the total up-time hours are listed.  
Looking at Table 2, it can be observed that scenario 1 provides the lowest availability and highest emissions, since it depends on long distance and supply lead time. The low spare parts stock policy applied in this scenario reduced the holding cost; however, it also reduced the availability. Scenario 2, with a digital warehouse and no physical stock, achieved the best results in terms of holding cost (zero stock) and emissions credits; however, the availability did not reach the desired target. Scenario 3 has perfectly improved availability and reached the desired target of 98.5%; however, this was at the expense of a high holding cost (NOK 1,309,577) and a considerable amount of emissions (1020 kg CO\({}_{2}\)). Scenario 4 has managed to keep availability at the desired target, while reducing the holding cost (from 1.3 to 0.899 MNOK) and reduce supply emissions to a minimal amount.  
Regarding green activity, the digital warehouse scenario managed to completely reduce all emissions related to global spare parts supply; however, this was at the expense of reducing availability by 15.1%. However, the hybrid warehouse scenario managed to increase availability by 11.5% while completely reducing all emissions related to global spare parts supply. Cost-wise, the digital warehouse scenario had the lowest operating expenses; however, this was at the expense of the availability. In this case, the hybrid warehouse scenario provides the highest operating expenses due to the estimated subscription fees, which are still open for discount negotiations. This high operating expense can still be justified, as this scenario provides the highest availability and emission reduction. It is worth mentioning that the cost estimation in this study is naive and requires better cost  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Criteria** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\  & **Physical** & **Digital** & **Enhanced Physical Warehouse** & **Hybrid** \\  & **Warehouse** & **Warehouse** & **Enhanced Physical Warehouse** & **Warehouse** \\ \hline Mean availability \% & 87 & 71.9 & 98.4 & 98.5 \\ Total availability hours & 152,373 & 125,906 & 172,351 & 172,575 \\ Mean supply lead time (Hr) \% & 799 & 209 & 803 & 206.87 \\ Total no. of shipments & 134 & 0 & 51 & 0 \\ Total emissions in kg CO\({}_{2}\) & 2680 & 0 & 1020 & 0 \\ Total transportation cost (NOK) & 402,000 & 0 & 153,000 & 0 \\ Spare parts cost (NOK) & 531,000 & 391,500 & 543,000 & 492,000 \\ Holding cost (NOK) & 412,124 & 0 & 1,309,577 & 898,943 \\ Total subscription cost (NOK) & 0 & 315,000 & 0 & 630,000 \\ Total expenses & 1,345,124 & 706,500 & 2,005,577 & 2,020,943 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the simulation results.  
estimates and coverage of the cost categories. However, the intention behind this study was to provide a model that quantifies the technical KPIs such as availability, supply lead time, number of hidden spares, number of shipment, and emission amount, and to enable SPIR engineers to use their own economic models to quantify the economic KPIs and evaluate the expenses against availability and emission figures.