Each specific organization has its exclusive constraints and problems that hinder achieving the required productivity. Analyzing all given data and parameters to find the root cause of the problems and defining the bottleneck is the first step for problem-solving. This study is adopted in an Egyptian poultry feed factory. Feed manufacturing has different processes of feed manufacturing as shown in Fig. 2. This production line has a bottleneck in the pelleting machine which increases the total downtime in the production line. Defining some pelleting machine operating parameters is very important. Analyzing the effect of different parameters on the overall quantity and quality of the pellet is also important. To reach the best production rates, improvement of the parameters must take place within the factory through experimentation. The parameters to be considered for improvement; are the operating conditions (pressure and temperature), and the die hole size of 4 mm is considered to solve the jamming problems and improve productivity. The methodology flowchart of the experimental work is presented in Fig. 3.
Figure 2Production line flowchart.
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Figure 3Exprimental work methodology flowchart.
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The most frequent stoppage in the production line is called jamming of the pelleting machine, where the die can no longer pass the mash feed and therefore, it is clogged and no pellets are being produced. This specific jamming stoppage is representing 58% of the total stoppages. The frequency of the jamming contributed is 44 h per Month (20 working days). During this duration, the production of the machine was reduced from an average of 11.58 tons per hour to zero consequently; the average total production loss is 509.52 Tons/month.
One of the possible solutions to the jamming problem would be the change of the hole size of the dies. This factory uses 3 mm dies. According to the experimental results achieved in previous work4,5,6,7, using 4 mm dies hole sizes has a greater feed quality than using (8 and 6) mm hole size. Therefore it is important to study the experiment with dies having a 4 mm die hole size and compare the results and repeat the experiment with another feed type. The following data is collected from the pelleting machine it shows the different PDI and production rates for the two different die-size holes.
The study was carried out on two types of feeds, the grower and the finisher. The PDI ratio is calculated for the different types of feeds for the two die-hole sizes. The obtained results showed that the PDI for the 3 mm dies hole size was 87.31% with a variance of 0.00014 while the average PDI for the 4 mm dies hole size was 88.3% with a variance of 0.00011 as shown in Fig. 4. The average of the PDI for the 3 mm dies hole size was 87.27% with a variance of \(7\times {10}^{-5}\) while the average PDI for the 4 mm dies hole size was 88.01% with a variance of \(9\times {10}^{-5}\) with finisher type of feed as shown in Fig. 5.
Figure 4PDI of the grower feed of the two die holes size.
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Figure 5PDI of Finisher Feed for the two hole sizes.
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The collected data shows the productivity of the machine in Tons/Hr. for the two die holes sizes. The experimental study is conducted on finisher feed, as it is the main type of feed. Twenty production samples are measured randomly for the two die hole sizes during one week. The achieved results showed that the average productivity for the 3 mm dies hole size is 11.58 Tons/Hr. with a variance of 5.136 while, the average productivity for the 4 mm dies hole size is 15.35 Tons/Hr. with a variance of 1.397 as shown in Fig. 6. The achieved results also reveals that significant decrease in stoppage time in pelleting machine due to jamming from 44 to 10 h per month by using 4 mm die hole size. While the current situation looks to favor the use of 4 mm dies. A decision cannot be taken until further analysis is conducted. Therefore, some statistical tests will be conducted to ensure that there is a significant difference regarding the PDI and productivity at different die-hole sizes. One way analysis of variance is used to determine whether the die hole size is significant or not. In case it is significant, a post-ANOVA analysis will be conducted to point out the better option for both PDI and productivity.
Figure 6Productivity for the two hole sizes.
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Minitab software is used to determine the significance of the die-hole size for product quality. A confidence level of 95% was used for the calculations and the null hypothesis is no significant difference. The obtained results showed that the null P-value is less than the ‘α’ level (0.008 < 0.05), therefore, the null hypothesis is rejected shown in Table 1. Then a significant difference in the quality occurs when different die sizes are used.
Table 1 One way ANOVA table for grower feed.Full size table
It can be seen in Fig. 7 that the PDI for the 4 mm dies hole size tends to be higher with a higher mean. For the grower feed type, since a significant difference exists, it is apparent that the use of the 4 mm dies will result in a better quality product. The next study is conducted to compare the uses of the two dies hole sizes using a different type of feed. The date in the following table is for the finisher feed type.
Figure 7Interval plot for the PDI at the two die holes sizes for the grower feed.
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A confidence level of 95% is used for the calculations and the null hypothesis was that there is no significant difference. In this case, it is observed that the null P-value is less than the α level (0.000 < 0.05) as shown in Table 2. Therefore, the null hypothesis is rejected, a significant difference n the quality occurs when different dies sizes are used. It is worth mentioning that the actual P-value is not equal to an absolute zero. However, computer software usually rounds the actual value to zero for simplicity. It can be seen in Fig. 8 that the PDI for the 4 mm die hole size tends to be higher with a higher mean. For the finisher feed type, since a significant difference exists, it is apparent that the use of the 4 mm die will result in a better quality product.
Table 2 One way ANOVA table for finisher feed.Full size table
Figure 8Interval plot for the PDI at the two die hole sizes for the finisher feed.
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The next step is to compare the productivity for the two die hole sizes to study the significance of the die hole size. The study was conducted only on the finisher type. The following table is the ANOVA table for the productivity at the two die hole sizes for the finisher feed as a main type of feed. A confidence level of 95% is used for the calculations and the null hypothesis was that there is no significant difference. In this case, it is observed that the null P-value is less than the α level (0.000 < 0.05). Therefore, the null hypothesis was rejected, a significant difference in productivity occurs when different die sizes are used. Once more, it is worth mentioning that the actual P-value is not equal to an absolute zero as shown in Table 3. However, computer software usually rounds the actual value to zero for simplicity. It can be seen in Fig. 9 that the use of the 4 mm dies results in larger productivity with a higher mean for the finisher feed type.
Table 3 ANOVA table for the productivity of finisher feed at the two die hole sizes .Full size table
Figure 9Productivity for the two die hole sizes.
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A proposed solution for the limited productivity of the factory is to study some operating parameters (pressure and temperature) and find the optimum level for each parameter. The proposed operating parameters for the experiments are taken according to the recommendation of the latest optimizing operating parameters by the quality manager. Then these parameters would be standardized. The experiments are carried out on the pelleting machine. A machine was checked by the factory to ensure that it is in suitable conditions for the experiments to take place. The productivity will be measured at different levels of these two parameters. Two-way ANOVA will then be conducted to determine the significance of each parameter. Finally, the software will be used to select the optimum operation parameters. The data was collected through many trials. The number of replications for each level is three replications after removing the failed trials or interrupted trials. There are four levels of the pressure parameter; 1.5, 1.7, 2, and 2.2 bar. The temperature consisted of three levels; these levels are intervals rather than individual values. These intervals are 75–77, 78–79, and 80–81 °C. The selected levels of the parameters are going to show the trend of what levels of each parameter are best. The obtained results from the experiments by using the ANOVA test to identify the significance of each parameter and the significance of the interaction between the two parameters are presented in Table 4. These observations can be used in the optimization process later where the best parameters will be identified to be later on standardized. Each of the interactions between the two parameters has 3 replications. These observations are entered into Minitab software so the ANOVA calculations and the suitability of the model to this case can be measured. Minitab was used to understand the significance of the parameters and the obtained results are shown in Table 5.
Table 4 observations from the conducted experiments.Full size table
Table 5 ANOVA calculations on results of obtained results.Full size table
A confidence level of 95% was used for the calculations and the null hypothesis was that there is no significant difference. In this case, it is observed that the null P-value is less than the ‘α’ level (0.000 < 0.05) for the two parameters and the interaction between them. Therefore the null hypothesis that no significant difference was rejected and the two parameters and the interaction between them are significant. The results obtained from these calculations and future analysis conducted on this data can be used with confidence as a test was run to determine the fit of the model to the data and the most important results are presented in Table 6.
Table 6 Summary of the model results.Full size table
The interpretation of the results is shown in Table 6, the value of S is used to test how the model responds to the data used. It is measured in the units of the response variable and represents how far the data values are from the fitted values. The lower the value of S, the better the model describes the response. R-sq is the percentage of variation in the response calculated by the model. The higher the R-sq value, the better the model fits the data. R-sq adjusted is used when it is desired to compare models that have different numbers of predictors. R-sq predicted is used to determine how well the model would predict the response for new observations. The model responds well to the data and therefore, it can be concluded with a confidence level of 95% that the temperature, pressure, and the interaction between both of them have a significant effect on productivity. With the results of the data collection and analysis phase in consideration, the implementation and results phase can be started.
We obtained ethical approval from the WADI poultry feed factory.
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