# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## 4 Results and Discussion
### Simulation Results
The service level and OTD were 100% for the RP and the MO models, while the EOQ model had a 96% OTD and 89% service level due to 46 weeks with 18 units backlogged on average. An area of difference in the models was the average procurement quantity, with the EOQ model closer to 2000 units, the RP model with almost 5000 units, and the MO model was in the middle with 3200 units. This can be explained by the fact that the MO model used and changed from the EOQ to the RP methods depending on the system conditions. Regarding the cost analysis, the MO model had better performance with almost USD 250 mil. less than the RP model and a slightly worse result than the EOQ model, with USD 36 mil. lower costs. The MO model opened a new warehouse in week 21, while the EOQ model did not open a warehouse, and the RP model opened one later in week 297. To summarize, for this scenario and lifecycle-demand curve, the MO model presented the best performance for service level as it equaled the RP model and had a slightly worse result in inventory costs compared with the EOQ model while securing higher service level and higher capacity utilization of employees as it adapted all areas of inventory management to the customer demand. As can be seen in Table 12, inventories were lower for the EOQ model compared with the other models, with the MO model presenting 8% lower stocks than the RP model. In addition, the service level and OTD were 100% for the RP and the MO models, while the EOQ model had an 87% OTD and 83% service level due to 70 weeks with 61 units backlogged on average. The EOQ model had an average procurement quantity of 2400 units, while the RP model had around 2900 units, and the MO model with 2700 units. This can be explained by the fact that the MO model used and changed from the EOQ to the RP methods depending on the system conditions. Regarding the cost analysis, the MO \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Key Indicator** & \begin{tabular}{c} **1. EOQ** \\ **Simulation Model** \\ \end{tabular} & \
\begin{tabular}{c} **2. Reorder-Point** \\ **Simulation Model** \\ \end{tabular} &
\begin{tabular}{c} **3. Market-Oriented** \\ **Simulation Model** \\ \end{tabular} \\ \hline \(1\) & \(\sum\) Demand (products) & 176,481 & 176,481 & 176,481 \\ \(2\) & MAD of the forecast (products) & 55.2 & 55.2 & 16.7 \\ \(3\) & \(\mathcal{O}\) WIP stock (products) & 9200 & 13,859 & 14,046 \\ \(4\) & \(\mathcal{O}\) stock at Tier 1 producer (products) & 6109 & 5261 & 3954 \\ \(5\) & \(\mathcal{O}\) stock at OEM producer (products) & 3091 & 8598 & 10,093 \\ \(6\) & Utilization rate of employees (\%) & 77.2 & 80.0 & 99.1 \\ \(7\) & On-time delivery (\%) & 96.0 & 100.0 & 100.0 \\ \(8\) & Service level (\%) & 88.5 & 100.0 & 100.0 \\ \(9\) & \(\mathcal{O}\) Procurement quantity (products) & 2077 & 4745 & 3214 \\ \(10\) & \(\mathcal{O}\) Customer backlog (products) & 17.7 & 0 & 0 \\ \(11\) & \(\sum\) Weeks with customer backlog (weeks) & 46 & 0 & 0 \\ \(12\) & \(\sum\) Inventory costs (mill. USD) & 3121 & 3396 & 3157 \\ \(13\) & \(\sum\) Procurement costs (mill. USD) & 1816 & 1759 & 1773 \\ \(14\) & \(\sum\) Capital commitment costs (mill. USD) & 1043 & 1516 & 1293 \\ \(15\) & \(\sum\) Storage costs (mill. USD) & 120 & 120 & 91 \\ \(16\) & \(\sum\) Stock-out costs (mill. USD) & 141 & 0 & 0 \\ \(17\) & \(\sum\) Investment costs (mill. USD) & 0 & 194 (t = 297) & 250 (t = 21) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Simulation results for the third demand scenario. model had better performance with 13% fewer costs compared with the EOQ model and with 9% less than the RP model. The MO model opened a new warehouse in week 60, while the EOQ model and the RP model opened a new warehouse later, around week 150. By using the MO model, the situation can be anticipated and the decision on a new investment based on the customer lifecycle of the product can take place.