# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 2 Materials and Methods
### Simulation Model and Scenario Modeling  
The modeling project started with an existing scenario (naive evaluation of spare parts without finding an optimal solution or using a digital warehouse); the second scenario was to find the optimal solution that matched the demand; the third scenario was to consider only a digital warehouse and local on-demand manufacturing; and finally, the fourth scenario considered both a digital warehouse and limited physical inventory. Based on the input of SPIR engineers, four scenarios need to be explored. Scenario 1 is the one recommended by the current SPIR analysis, where it is recommended that three spare parts be kept on site. Scenario 1 totally depends on the physical warehouse with delivery time from the original manufacturer that has a triangle distribution (Min 20 days, Mean 30 days, Max 50 days). Cost-wise, scenario 1 is based on spare parts cost and inventory holding cost. Scenario 2 aims to understand how the digital warehouse option works alone. Scenario 2 totally depends on a digital warehouse and on-demand manufacturing with delivery times based on a triangle distribution (Min 5 days, Mean 7 days, Max 14 days) and no safety stock. Cost-wise, scenario 2 is based on subscription fees and has no inventory holding cost. Scenario 3 is preferable in terms of production availability, where the supply of spare parts should meet the highest availability target. Scenario 3 totally depends on the physical warehouse with the delivery time from the original manufacturer which has a triangle distribution (Min 20 days, Mean 30 days, Max 50 days) and no safety stock. Cost-wise, scenario 2 is based on spare parts cost and inventory holding cost. Scenario 4 is another preferable scenario to keep production availability as high as possible by using hybrid warehousing. Scenario 4 is dependent on a digital warehouse with minimal physical stock and delivery times based on a triangle distribution (Min 5 days, Mean 7 days, Max 14 days). Cost-wise, scenario 4 is based on subscription fees that include the inventory holding cost for the minimal stock items. The considered lifetime for these scenarios is 20 years (175,200 h). The replacement time is 8 h and is the same for all scenarios. As shown in Table 1, the four scenarios differ in the amount of safety stock and the lead time of supply. Regarding CO\({}_{2}\) points, it is assumed that any spare part that is delivered from the original manufacturer outside of Norway will receive two points of CO\({}_{2}\), while spare parts delivered within Norway will be counted as zero CO\({}_{2}\) points. These CO\({}_{2}\) points can be converted to cost figures once the CO\({}_{2}\) fee and associated taxes are determined. Regarding the holding cost, it is assumed that there is a constant holding cost rate per spare part item per hour, which will be multiplied by the number of spares in stock to get the total holding cost. The developed simulation model aims to represent the asset availability and spare part supply process as shown in Figure 3.  
The state chart represents the asset agent, where it has several states; it should usually be in the working state (\"Asset Working\" in the figure) unless a failure is triggered or a spare part is demanded (demand transition in the figure). Once a spare part is requested, a quick check (illustrated as a diamond symbol in Figure 3) of the on-site stock will be performed to determine whether a spare part is available or should be ordered. If a spare part is available in the stock, a \"direct replacement\" task will be carried out in 8 h, and then the asset will return to the \"Working\" state. However, if there are no spare parts in stock, the spare parts shall be ordered and supplied by a \"supply\" agent. The supply agent is modeled as a discrete event process, as shown at the bottom of Figure 3, which starts with an \"order\" and ends with a specific batch size of spare parts. It has a specific supply lead time; the triangular distributions are described in Table 1.