Use of MDEWMA in Other Industries

Oct 13, 2025 by recoveryshake.com 6 views

Introduction

In the industrial world, maintaining product quality is a crucial aspect of ensuring customer satisfaction and loyalty. One of the methods used for quality control is the Double Exponential Weighted Moving Average (MDEWMA) bootstrap. This study aims to develop alternative methods for controlling product quality using MDEWMA in the multivariate control process. The data used in this study is secondary data from the PDAM water production process, where observed variables include quality characteristics such as turbidity, pH, and remaining chlorine.

Background

Quality control is a critical aspect of any industrial process. It involves monitoring and controlling the quality of products to ensure that they meet the required standards. In the multivariate control process, multiple variables are monitored simultaneously to detect any deviations from the target values. The MDEWMA bootstrap is a statistical method used to control the average process in the multivariate control process. It uses a weighted average of past observations to detect any changes in the process.

Methodology

The MDEWMA bootstrap method involves the following steps:

Data Collection: Secondary data from the PDAM water production process was used in this study. The data included quality characteristics such as turbidity, pH, and remaining chlorine.
Data Analysis: The data was analyzed using the MDEWMA bootstrap method. The method uses a weighted average of past observations to detect any changes in the process.
Weight Determination: The weight of the MDEWMA bootstrap method was determined using the ARL (Average Run Length) value. The ARL value represents the average number of observations required to detect a change in the process.
Resampling: The data was resampled twice and four times to determine the sensitivity of the MDEWMA bootstrap method.

Results

The results of the study showed that the MDEWMA bootstrap method was sensitive to changes in the process. The determination of weight of 0.6 and ARL 370 proved to be the most sensitive control diagram for resampling twice. On the other hand, when resampling four times, weight 0.5 with ARL 200 showed high sensitivity, where all observations statistically were well controlled.

Further Analysis

Further analysis revealed that in the previous phase, monitoring using T² Hoteling showed that the process did not meet the standards, which is incapable and not in accordance with the target value. This indicates that there is still a need to increase the level of accuracy in the implementation process. However, when entering the next stage by using the Bootstrap Dewma Multivariat (MDEWMA) method, it was found that this process could provide more reliable control diagrams, especially in detecting errors outside of control.

Advantages of MDEWMA

The advantage of the MDEWMA method lies in its ability to pay more attention to newer data, so that it can provide faster indications of changes that may occur in the process. This is very important in quality control because fast handling can prevent greater losses.

Conclusion

Overall, this study shows that the use of MDEWMA in the multivariate control process can increase the reliability of product quality control. By utilizing the right weight and analysis, companies can increase effectiveness in maintaining and ensuring the expected quality standards. The use of this method is not only beneficial for PDAMs but can also be adapted to various other industries that require strict quality control.

Recommendations

Based on the results of this study, the following recommendations are made:

Use of MDEWMA: The MDEWMA bootstrap method should be used in the multivariate control process to increase the reliability of product quality control.
Weight Determination: The weight of the MDEWMA bootstrap method should be determined using the ARL value to ensure that the method is sensitive to changes in the process.
Resampling: The data should be resampled multiple times to determine the sensitivity of the MDEWMA bootstrap method.
Implementation: The MDEWMA bootstrap method should be implemented in various industries that require strict quality control.

Limitations

This study has the following limitations:

Secondary Data: The study used secondary data from the PDAM water production process, which may not be representative of other industries.
Limited Resampling: The study only resampled the data twice and four times, which may not be sufficient to determine the sensitivity of the MDEWMA bootstrap method.

Future Research

Future research should focus on the following areas:

Use of MDEWMA in Other Industries: The MDEWMA bootstrap method should be used in other industries to determine its effectiveness in increasing the reliability of product quality control.
Weight Determination: The weight of the MDEWMA bootstrap method should be determined using different ARL values to determine the optimal weight for the method.
Resampling: The data should be resampled multiple times to determine the sensitivity of the MDEWMA bootstrap method.

Conclusion

Q: What is the Double Exponential Weighted Moving Average (MDEWMA) bootstrap method?

A: The MDEWMA bootstrap method is a statistical method used to control the average process in the multivariate control process. It uses a weighted average of past observations to detect any changes in the process.

Q: What are the advantages of using the MDEWMA bootstrap method?

A: The advantages of using the MDEWMA bootstrap method include its ability to pay more attention to newer data, providing faster indications of changes that may occur in the process. This is very important in quality control because fast handling can prevent greater losses.

Q: How does the MDEWMA bootstrap method work?

A: The MDEWMA bootstrap method involves the following steps:

Data Collection: Secondary data from the PDAM water production process was used in this study. The data included quality characteristics such as turbidity, pH, and remaining chlorine.
Data Analysis: The data was analyzed using the MDEWMA bootstrap method. The method uses a weighted average of past observations to detect any changes in the process.
Weight Determination: The weight of the MDEWMA bootstrap method was determined using the ARL (Average Run Length) value. The ARL value represents the average number of observations required to detect a change in the process.
Resampling: The data was resampled twice and four times to determine the sensitivity of the MDEWMA bootstrap method.

Q: What are the limitations of the MDEWMA bootstrap method?

A: The limitations of the MDEWMA bootstrap method include:

Secondary Data: The study used secondary data from the PDAM water production process, which may not be representative of other industries.
Limited Resampling: The study only resampled the data twice and four times, which may not be sufficient to determine the sensitivity of the MDEWMA bootstrap method.

Q: Can the MDEWMA bootstrap method be used in other industries?

A: Yes, the MDEWMA bootstrap method can be used in other industries that require strict quality control. The method can be adapted to various other industries by adjusting the weight and analysis to suit the specific needs of the industry.

Q: What are the future research directions for the MDEWMA bootstrap method?

A: Future research directions for the MDEWMA bootstrap method include:

Use of MDEWMA in Other Industries: The MDEWMA bootstrap method should be used in other industries to determine its effectiveness in increasing the reliability of product quality control.
Weight Determination: The weight of the MDEWMA bootstrap method should be determined using different ARL values to determine the optimal weight for the method.
Resampling: The data should be resampled multiple times to determine the sensitivity of the MDEWMA bootstrap method.

Q: What are the implications of the study for quality control in industries?

A: The study has significant implications for quality control in industries. The use of the MDEWMA bootstrap method can increase the reliability of product quality control by providing faster indications of changes that may occur in the process. This can prevent greater losses and improve customer satisfaction.

Q: What are the recommendations for implementing the MDEWMA bootstrap method in industries?

A: The recommendations for implementing the MDEWMA bootstrap method in industries include:

Use of MDEWMA: The MDEWMA bootstrap method should be used in the multivariate control process to increase the reliability of product quality control.
Weight Determination: The weight of the MDEWMA bootstrap method should be determined using the ARL value to ensure that the method is sensitive to changes in the process.
Resampling: The data should be resampled multiple times to determine the sensitivity of the MDEWMA bootstrap method.
Implementation: The MDEWMA bootstrap method should be implemented in various industries that require strict quality control.