## 6 Discussions and Conclusions  
This paper proposed a computational framework for the order planning problem in a distributor. In the proposed model, values of response variables were simulated and calculated, and Minitab was used to determine the possible optimal setting for those responses using the central composite design and Box-Behnken design. In the case study, a combination of the review period (T), order quantity (Q), and safety stock (SS) was selected as independent variables of the model. Additionally, the total cost of the holding cost, shortage cost, over-storage cost, and transportation cost, and customer service level was selected as response variables. The objective of the paper was to minimize the total cost by planning on placing the right order quantities, in the right periods, with the possible safety stock in order to reach customer satisfaction around 90-95 percent using RSM. For solving an order planning problem of Spectite CW100, the statistical result from the factorial design shows that the curvature had a significant effect on the responses, therefore, two response surface designs, central composite design, and Box-Behnken design, were considered afterward to find the possible parameter settings. Based on the results in the response optimizer, the following values for the response surface design were as follows: \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Central Composite Design (CCD)** & \multicolumn{2}{c}{**Box-Behnken Design (BBD)**} \\ \hline  & **CSL (90\%)** & **CSL (95\%)** & **CSL (90\%)** & **CSL (95\%)** \\ \hline T & 3 & 3 & 3 & 3 \\ Q & 3428 & 3520 & 3603 & 3690 \\ SS & 1336 & 1354 & 1250 & 1250 \\ TC & 76,330 & 77,090 & 70,870 & 85,920 \\ \hline \hline \end{tabular} Note: calculated by the researchers.  
\end{table} 
Table 7: The summary of the response optimizer results of CCD and BBD. Figure 23: Variance dispersion graph for the CCD and BBD experiment. Central composite design (CCD), customer service level CSL was at 90%, the minimum total cost was 76,330 with the possible optimal settings were the time period (T) = 3, order quantity (Q) = 3428, safety stock (SS) = 1336; (2) customer service level CSL was at 95%, the minimum total cost was 77,090 with the possible optimal settings were T = 3, Q = 3520, and SS = 1354. Box-Behnken design (BBD), customer service level CSL was at 90%, the minimum total cost was 70,870 with the possible optimal settings were the time period (T) = 3, order quantity (Q) = 3603, and safety stock (SS) = 1250; (2) customer service level CSL was at 95%, the minimum total cost was 85,920 with the possible optimal settings were T = 3, Q = 3690, and SS = 1250. According to the scaled prediction variance (SPV), the CCD design had better SPV (smallest values) than the BBD design, therefore, the CCD experiment could be considered as the best model in this paper. On the other hand, based on the results of the response optimizer, BBD design gave the smallest total cost. When the CSL increased from 90% to 95%, the total cost also increased in both designs. The proposed computational framework can be applied to other products of the distributor (Specific WS, Spectite HP600, and Speccoat PE145), or any ordering process in businesses within the supply chain. In summary, our contributions include (1) the paper proposed a computational framework to solve the order planning problem by a heuristics approach using RSM, (2) the model was tested with a case study of a distributor to show the model's effectiveness, and (3) The insights implication of the model's results was to provide a useful reference for purchasing managers or decision-makers to obtain the most suitable order policy for a robust order planning process, which includes responses that were considered in monetary values. Besides, this could be used as a guideline for other related industries. However, there were some limitations to this paper.