## 4 Results and Discussions  
The demand of the last three months over time is shown in Figure 2. In the first few weeks, the previous three months' data are not complete and therefore the values are lower than usual. This is why these few weeks are excluded from the calculations in this paper. The last few weeks were also excluded. Generally, the demand follows a way in which it increases in the first few months of the year as shown. However, the percent of satisfied demand is more stable, except when the demand is more than 2500 orders, as shown in Figure 3. The average percent of satisfied demand, when the three-month demand is less than 2500, is 73.4%. However, when demand is higher than 2500, this percentage is reduced to be 68.1%. That means higher demand cannot be satisfied with the same level of lower demand. This fact can be useful to predict this percentage. Since there is no trend in the data, the seasonal effect can be found by finding the demand with the same week number from the previous year. The results were obtained from February until May, and the MAPE was about 9%. However, the peak at the beginning of 2015 was flatter than that for 2016. Therefore, another way for better forecasting accuracy is the moving average (with \(n=3\)). In this case, MAPE is about 4%, which is more accurate. This is because the rate of changes in demand from one week to another is relatively slow. The average percent of satisfied demand in the year 2016 was about 68%. To forecast it, a moving average was used with \(n=3\). In this case, the average mean absolute deviation (MAD) value was about 0.02 for the months from February to May. If we take all the data for the last three months, then about 70% of the orders are usually satisfied. The older orders are usually satisfied. The average number of orders, which are not satisfied, is 759. For those, which are not satisfied until today, about 30% will be satisfied in the next week. For SDNW, the weekly size of arriving orders is rapidly changing, and it can be from 50 to 400. However, it can be predicted with some reasonable accuracy with two methods, namely, the moving average and simple regression, where the independent variable is DNW. Figure 4 shows the results of the two methods. The MAD for both of them is almost the same for the supply chain under consideration. Figure 2: Total demand in the last three months. In Figure 5, the numbers inside the circles are the number of orders, which are in that date and with that specified lead time. Lead time follows a line with a slope that decreases with time. This is because customer orders are every day, but the delivery is usually every week. There are some parallel hypothetical lines these points follow, where each line represents a new week delivery. For example, on 12 February, 32 orders were triggered. Only 22 orders from them were satisfied with a lead time of 20 days. After one week, three of these orders were satisfied. The rest of the orders have to wait two more weeks. It is very clear that the exact time of the order is not possible to be predicted. However, if dynamic forecasting is to be used, the probability that a certain order will come in the next week can  
Figure 4: Prediction of satisfied demand in the next delivery (SDNW). Figure 3: The effect of demand on the percentage of satisfied orders in the three-month period. be obtained with reasonable accuracy. Most of the orders are satisfied within three or four successive weeks. Four of the orders coming on 15 February arrived earlier than usual. Table 1 shows the main results of the study. It shows 10 weeks' data and also the grand average. Usually, the time between different deliveries is seven days, and sometimes one or two days earlier or later. The assumption here is that such a difference will not affect the data significantly. DNW's and FA's grand average values are significantly greater than the average values for the 10 weeks data. Therefore, the consideration will be for the grand averages.