## 4 Results and Discussions
The general accuracy of forecasting is somewhat not very high, which is 72.41%. However, what really matters in this study is the type I error value, which is 0.07. This means that the accuracy in this regard is 93%. In other words, when it is expected that a certain order will come the next week, there is a chance that it comes 93% of the time. If better forecasting accuracy is needed, then a higher threshold value, such as 0.55 can be used instead of 0.5 for logistic regression. Type II error is indeed larger, with an average of 0.2, but this kind of error is not the critical one in this paper. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Delivery Date**} & \multirow{2}{*}{**P \({}^{*}\)**} & \multirow{2}{*}{**DNW**} & \multirow{2}{*}{**TSD**} & \multirow{2}{*}{**SDNW**} & \multirow{2}{*}{**ADNW**} & \multirow{2}{*}{**FA**} & \multicolumn{2}{c}{**Type II**} & \multicolumn{2}{c}{**Type I**} & \multicolumn{2}{c}{**\%**} & \multirow{2}{*}{**PSD**} & \multirow{2}{*}{**LRED**} & \multicolumn{2}{c}{**Average**} \\  
TSD is the real demand, but SDNW is the expected one. The average difference between them is small. A metric to measure the accuracy of using SDNW to predict TSD is ADNW. The \"% CFFS\" term represents the effect of the FCFS rule. If the rule is completely followed by suppliers, then it should be 100%. However, it is only followed 74.56% of the time. This is why FA is not so high. The average value of PSD is 29.72%, which means that most of the current demand will not be satisfied in the next week because the lead time is long. However, logistic regression gives a percentage of 16.81 for LRED, which is almost half of PSD. This is to reduce the type I error. The average order-processing period so far (OPSF) is 15.72. This waiting, so far, is for the not satisfied demand in the current week. That means that all these orders must wait at least until the next week. The general results show a reasonable accuracy, especially when it comes to the critical error, which is the type I error. This paper provides the decision maker with a way to know if the current order by a customer should wait for the next week for possible consolidation or not. This is, of course, if that order belongs to a customer having other orders. In case that a certain customer has only one order that comes in the current delivery, then there is no need for that order to wait. The forecasting process should be performed for all strategic suppliers and all orders in process. Since many customers are asking for orders from different suppliers, even with a lower level of accuracy, the model can provide an advantage. This is because if there are, for example, seven orders of one customer needed by supplies suppliers with an average accuracy of only 70%, then the accuracy for the probability that at least one of the seven orders will be satisfied in the next week will be much larger than 70%. Actually, it will be almost 100%. However, decision-makers should be aware of the impact of that on the service level. For example, if the due date is exceeded if consolidation is performed, then it should not be performed. Therefore, in the following cases, consolidation should not be performed, and therefore, there is no need for forecasting (for consolidation purposes) for that particular order:  
1. If the due date is exceeded;
2. If there is only one order in process by the customer;
3. If inventory holding cost for one more week will cost more than the savings of reducing the number of shipments;
4. If the size of the current shipment is large enough to transship it without aggregating it with any further shipments.