# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## 4 Results and Discussion
### Simulation Results
To summarize, for this scenario and lifecycle-demand curve, the MO model presented the best performance for the service level and inventory costs compared with both other models. As can be seen in Table 13, the MAD was almost three times lower for the market-oriented (MO) model due to the selection of the forecasting technique based on the set of methods presented in Section 3. In addition, inventories were lower for the EOQ model than for the other models, with the MO model presenting almost 10% lower stocks than the EOQ model and almost 20% compared with the RP model. The service level and OTD were 100% for the RP and the MO models, while the EOQ model had an 84% OTD and 78% service level due to 88 weeks with 75 units backlogged on average. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Key Indicator**} & **1. EOQ** & **2. Reorder-Point** & **3. Market-Oriented** \\  & & **Simulation Model** & **Simulation Model** & **Simulation Model** \\ \hline
1 & \(\sum\) Demand (products) & 189,173 & 189,173 & 189,173 \\
2 & MAD of the forecast (products) & 45.2 & 45.2 & 17.6 \\
3 & \(\mathcal{O}\) WIP stock (products) & 14,704 & 16,766 & 13,338 \\
4 & \(\mathcal{O}\) stock at Tier 1 producer (products) & 11,304 & 6939 & 4542 \\
5 & \(\mathcal{O}\) stock at OEM producer (products) & 3400 & 9827 & 8796 \\
6 & Utilization rate of employees (\%) & 70.1 & 73.2 & 99.1 \\
7 & On-time delivery (\%) & 84.2 & 100.0 & 100.0 \\
8 & Service level (\%) & 78.0 & 100.0 & 100.0 \\
9 & \(\mathcal{O}\) Procurement quantity (products) & 2253 & 3432 & 2432 \\
10 & \(\mathcal{O}\) Customer backlog (products) & 74.9 & 0 & 0 \\
11 & \(\sum\) Weeks with customer backlog (weeks) & 88 & 0 & 0 \\
12 & \(\sum\) Inventory costs (mil. USD) & 4356 & 3832 & 3277 \\
13 & \(\sum\) Procurement costs (mil. USD) & 1946 & 1927 & 1929 \\
14 & \(\sum\) Capital commitment costs (mil. USD) & 1670 & 1763 & 1246 \\
15 & \(\sum\) Storage costs (mil. USD) & 141 & 141 & 101 \\
16 & \(\sum\) Stock-out costs (mil. USD) & 600 & 0 & 0 \\
17 & \(\sum\) Investment costs (mil. USD) & 273 (t = 150) & 280 (t = 141) & 324 (t = 63) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Simulation results for the fifth demand scenario. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Key Indicator**} & **1. EOQ** & **2. Reorder-Point** & **3. Market-Oriented** \\  & & **Simulation Model** & **Simulation Model** & **Simulation Model** \\ \hline
1 & \(\sum\) Demand (products) & 217,581 & 217,581 & 217,581 \\
2 & MAD of the forecast (products) & 48.8 & 48.8 & 18.0 \\
3 & \(\mathcal{O}\) WIP stock (products) & 12,110 & 15,901 & 14,610 \\
4 & \(\mathcal{O}\) stock at Tier 1 producer (products) & 8460 & 6858 & 4309 \\
5 & \(\mathcal{O}\) stock at OEM producer (products) & 3651 & 9043 & 10,301 \\
6 & Utilization rate of employees (\%) & 75.1 & 79.0 & 99.2 \\
7 & On-time delivery (\%) & 86.7 & 100.0 & 100.0 \\
8 & Service level (\%) & 82.5 & 100.0 & 100.0 \\
9 & \(\mathcal{O}\) Procurement quantity (products) & 2400 & 2897 & 2702 \\
10 & \(\mathcal{O}\) Customer backlog (products) & 60.9 & 0 & 0 \\
11 & \(\sum\) Weeks with customer backlog (weeks) & 70 & 0 & 0 \\
12 & \(\sum\) Inventory costs (mil. USD) & 4242 & 4035 & 3689 \\
13 & \(\sum\) Procurement costs (mil. USD) & 2217 & 2209 & 2197 \\
14 & \(\sum\) Capital commitment costs (mil. USD) & 1387 & 1676 & 1379 \\
15 & \(\sum\) Storage costs (mil. USD) & 150 & 150 & 113 \\
16 & \(\sum\) Stock-out costs (mil. USD) & 487 & 0 & 0 \\
17 & \(\sum\) Investment costs (mil. USD) & 263 (t = 158) & 276 (t = 146) & 344 (t = 60) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Simulation results for the fourth demand scenario. The EOQ model had an average procurement quantity of around 2250 units, while the RP model had around 3400 units, and the MO model was in the middle but closer to the value of the EOQ model. 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.