# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 3 Results and Discussion
### Proposed SPIR Framework for Digital and Hybrid Warehouse  
After analyzing several scenarios for physical, digital, and hybrid warehouse options, SPIR engineers should have enough information to determine the relevant concept of spare parts inventory and complete the SPIR worksheet with the required specifications. As mentioned earlier, the current worksheet for SPIR studies is mainly for the physical warehouse option (scenario 1). In addition, the spare parts risk matrix, as illustrated in Figure 2, is also mainly for the physical warehouse option (scenario 1). The digital and hybrid warehouse can also affect the spare parts risk matrix, as illustrated in Table 3. The digital warehouse can replace the central warehouse stocks and it can also replace the at-site stock for low-consequence items. The hybrid warehouse option is very helpful for high-consequence items to minimize the stock level. The new emerging matrix in Table 3 can be helpful for SPIR engineers in determining the location of each spare parts group, considering the options of digital and hybrid warehouse. It is worth mentioning that the spare part risk matrix that the NORSOK Z-008 standard and the case company use are not complex. It is used only to determine the location of each spare parts group (on-site, local warehouse, global warehouse) based on the risk of not having the required spare parts. It is not meant to be used to determine the quantity of spare parts. To determine the optimal quantity of spare parts stock and the reorder point, SPIR engineers are recommended to use the developed simulation model and customize it  
Figure 11: Total spare parts cost and subscription fee over lifetime, with fees and costs in NOK. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Demand Rate** & \multicolumn{3}{c}{**Consequence**} \\ \cline{2-4}  & **Low** & **Medium** & **High** \\ \hline \multirow{3}{*}{**First line spare parts frequently used**} & Digital warehouse, no stock & Minimum stock at central warehouse & Minimum stock at additional spare parts at central warehouse \\ \hline \multirow{3}{*}{**Not frequently used**} & Digital warehouse, no stock & Digital warehouse, no stock & Minimum stock at central warehouse \\ \hline \multirow{3}{*}{**Capital spare parts seldom or never used**} & Digital warehouse, no stock & Digital warehouse, no stock & Holding optimized by use of risk assessment case by case by case \\ \cline{2-4}  & \multicolumn{1}{p{56.9 pt}}{} & \multicolumn{1}{p{56.9 pt}}{} & \multicolumn{1}{p{56.9 pt}}{} & \\ \hline \hline \end{tabular} Red colored cell refers to critical risk, yellow cells refer to medium risk and blue coloured cells refer to low risk.  
\end{table} 
Table 3: The suggested spare parts risk matrix considering digital warehousing. for their purposes and case. The model is helpful for enabling SPIR engineers to select from the warehouse options, whether the optimal warehouse is physical, digital, or hybrid. Certainly, an ad hoc or naive physical warehouse (like scenario 1) that is not based on demand rate and supply lead time should be avoided. The simulation method used in this paper, together with the methods mentioned in the introduction section, e.g., BDSPNs, can handle stochastic demand rates and stochastic supply lead times, which is essential for cost-effective spare part management. SPIR engineers are also advised to use the data-driven approach, improve their estimate of demand rates and supply lead times, and reinforce them in simulation models and worksheets. The last recommended step is to revise the SPIR worksheet. If SPIR engineers decide to go for a hybrid warehouse (scenario 4), which is the most cost-effective scenario based on simulation analysis, they need an upgraded SPIR worksheet as shown in Table 4.  
In summary, the upgraded SPIR study framework recommends the following steps:  
1. Use statistical modeling and machine learning techniques to better estimate demand rates and supply lead times. 2.