## 3 Materials and Methods  
The main aim of the paper is to predict using machine learning which items already ordered will come in the next week. This forecasting is performed just after receiving the items in the consolidation center in the current week. This is for the purpose of temporal consolidation. Therefore, when there are no previous items already waiting in the warehouse for a certain customer, there is no need to make forecasting for the items of that customer. Usually, the supplier makes delivery every week, most probably on Wednesday. However, sometimes, the shipment comes to the warehouse one day later or earlier. In some special cases, the delay is one week, and that means that the delivery comes in two weeks. The warehouse has a notice from the supplier for that delay. The decision makers in the warehouse do not know which item will come in the next delivery. This is because the lead time is usually very long (some weeks), and any open order might not come in the next delivery. For example, only 30% of the orders might come in the next week. What makes it more difficult to forecast is that many orders do not come based on the first come first served (FCFS) principle, because the supplier has limited capacity for different production lines, and demand from other customers is not known. There are several reasons why the lead time is longer than usual, which are as follows:  
* The nature of the supply chain, which is make-to-order. In many cases, the design of furniture is unique. Therefore, it needs more time. * Customer demand can occur every day, but the supplier delivery is performed every week and sometimes every two weeks. The supplier does this because of the shipment consolidation inside the supplier factory to reduce transportation costs [4]. * Some of the products do not come based on FCFS, as mentioned before. * Other customers' orders compete for the same production lines of the suppliers. In this study, the main affecting factors on forecasting are the size of demand in the last few weeks, the number of orders not yet satisfied, and the period since the order is still opened. Other factors such as customer, product type, and model types were investigated in a primary stage, and it was found that their effects are negligible. Therefore, they were neglected in the analysis in this paper. In forecasting, it is important to distinguish two types of errors:  
* Type I error, in which the forecasting result indicates delivery in the next week, but delivery does not happen. * Type II error, in which the forecasting results indicate no delivery in the next week, but delivery is actually completed. In this study, type I error is the most important one because it is not good for the customer order in the consolidation center to wait the whole week, and then the next order does not come, and therefore, larger inventory holding costs occur. Therefore, the manager might set a value of 0.05 as an acceptable level for such an error. Type I error has a close relationship to the objective of the study. The manager wants to let some shipments wait, only if there is a high chance that another shipment is coming in the next week. The probability of expecting that a shipment will not come in the next delivery, while it will not come, does not have the same importance as type I error. This differentiation of types of errors cannot be measured using common indices such as RMSA and MSE. The available data are for the period from December 2014 until August 2016. In this study, one of the suppliers will be investigated. The approach of this study can be applied to any supplier. Forecasting is repeated after each delivery, which occurs almost every week. The dynamic forecasting in the last step will use logistic regression since it is appropriate for the purpose of the study. The results will be 1 or 0, where 1 means delivery of a certain order is expected to occur, and 0 means delivery does not occur. R software was used to make the calculations.