## 3 Product Service System Case Study
### Optimization Results and Discussion  
Table 5 shows a partial list of the optimized field repair kit spare parts shown in Table 3. The list for scenario 1 shows the original quantities of spare parts proposed by the design team. Second column shows the optimized spare parts list for 95% availability requirement. The last column shows a partial list of spare parts, optimized for 90% availability requirement. Observation of the list reveals some key insights. In the original repair kit composition, two units of spare part A were kept on the customer site. However, the optimum composition eliminated spare part A altogether from the field repair kit, mainly because of the part unit cost, which was significantly higher than the cost of any other part in the field repair kit. On the other hand, part J, which has a very low part unit cost, saw an increase in quantity from the original quantity proposed by the design team. The observation of the optimization results is that in balancing the field repair kit cost and the availability requirement, the optimized field repair kit is composed in a way that the PSS will only shut down for a part that is too expensive to be held onsite, and not for a low cost part. Figure 4 shows, for each scenario, the total of the cost of the PSS field repair kit. The results show that in order to meet the PSS availability requirement of 95%, the total cost of the field repair kit must increase by more than 60% over that of the originally proposed field repair kit composition in Scenario 1. However, when the PSS availability requirement constraint is relaxed to 90%, the cost of the field repair kit was significantly reduced, nearly down to 50% of the field repair kit cost shown in Scenario 2. In other words, increasing the availability requirement by 5% resulted in a 50% increase in the total cost of the field repair kit. Additionally, it is observed that if 90% PSS availability is acceptable to the customer, the optimized field repair kit can be composed for lesser cost than the original field repair kit proposed by the design team. \begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario** & **Scenario Description** \\ \hline
1: Base & PSS field repair kit composition is provided by the PSS design team. \\
2: 95\% availability & PSS field repair kit is cost optimized with PSS availability requirement set to 95\%. \\
3: 90\% availability & PSS field repair kit is cost optimized with PSS availability requirement set to 90\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key parameters and their value ranges for simulation and optimization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Spare Part**} & \multirow{2}{*}{**Unit Cost (Normalized)**} & \multicolumn{3}{c}{**Spare Parts Quantity**} \\ \cline{3-5}  & & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline A & 100.00 & 2 & 0 & 0 \\ B & 2.89 & 1 & 5 & 4 \\ C & 1.23 & 1 & 12 & 4 \\ D & 0.78 & 6 & 12 & 10 \\ E & 0.77 & 1 & 16 & 7 \\ F & 0.29 & 2 & 19 & 11 \\ G & 0.19 & 1 & 14 & 6 \\ H  
As in the last step, using the optimized field repair kit composition, Monte Carlo simulation was performed to estimate the confidence level for which the PSS can meet the availability requirement for each scenario. Two parameters were used. The first parameter was the failure rate of each spare part. Distribution of spare parts failure rates were set based on the historical reliability data of the part or similar parts. The second parameter was the PSS usage. Since PSSs are used by many customers, but with different monthly usage, the PSS monthly usage was set with triangular distribution with a minimum value of 5.7 million uses and a maximum value of 10.2 million uses, with a mean usage of 8.0 million per month. This was based on the estimate from the design team. The resulting distribution of availability requirement was analyzed to determine the confidence level for the optimized field repair kit to meet the target PSS availability.