Pub Date : 2016-01-01DOI: 10.1504/IJQET.2016.081641
Dushyant Tyagi, B. Singh
In this paper, the CUSUM chart has been proposed to monitor the small shift in production process when the quality characteristic follows Rayleigh distribution. To achieve approximate normality of the Rayleigh distributed data, Nelson transformation method is used. The efficiency of CUSUM chart in detecting small shift in the process as compared to traditional Shewhart chart is demonstrated through a simulated study. Parameters for proposed chart has been determined for varying true mean at fix 'in control mean' and 'in control ARL'. Table of k and h values are provided to assist quality control engineers for designing an optimal CUSUM chart to detect δσ shift.
{"title":"Designing of CUSUM chart with transformed Rayleigh distributed data","authors":"Dushyant Tyagi, B. Singh","doi":"10.1504/IJQET.2016.081641","DOIUrl":"https://doi.org/10.1504/IJQET.2016.081641","url":null,"abstract":"In this paper, the CUSUM chart has been proposed to monitor the small shift in production process when the quality characteristic follows Rayleigh distribution. To achieve approximate normality of the Rayleigh distributed data, Nelson transformation method is used. The efficiency of CUSUM chart in detecting small shift in the process as compared to traditional Shewhart chart is demonstrated through a simulated study. Parameters for proposed chart has been determined for varying true mean at fix 'in control mean' and 'in control ARL'. Table of k and h values are provided to assist quality control engineers for designing an optimal CUSUM chart to detect δσ shift.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2016.081641","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJQET.2016.081615
F. Uzoka, B. Akinnuwesi, Oluwole Adegoke Nuga, A. F. Adekoya, Oluwadamilola Y. Egbekunle
One vital aspect of software project management is the initial evaluation of project proposals. Many authors have proposed varying sets of criteria for information technology (IT) software project evaluation. There is the need to develop a common framework for evaluating software projects, based on a manageable set of criteria. In this paper, we identified 83 software project proposal evaluation variables from literature and direct interviews with project evaluators; 31 variables were extracted under ten constructs using exploratory factor analysis. Product characteristics, user characteristics and vendor experience were found to be very crucial in software project proposal evaluation. The parameters identified in this work could serve as a basis for the development of a software system for evaluating software project proposals.
{"title":"Identifying factors for evaluating software project proposals","authors":"F. Uzoka, B. Akinnuwesi, Oluwole Adegoke Nuga, A. F. Adekoya, Oluwadamilola Y. Egbekunle","doi":"10.1504/IJQET.2016.081615","DOIUrl":"https://doi.org/10.1504/IJQET.2016.081615","url":null,"abstract":"One vital aspect of software project management is the initial evaluation of project proposals. Many authors have proposed varying sets of criteria for information technology (IT) software project evaluation. There is the need to develop a common framework for evaluating software projects, based on a manageable set of criteria. In this paper, we identified 83 software project proposal evaluation variables from literature and direct interviews with project evaluators; 31 variables were extracted under ten constructs using exploratory factor analysis. Product characteristics, user characteristics and vendor experience were found to be very crucial in software project proposal evaluation. The parameters identified in this work could serve as a basis for the development of a software system for evaluating software project proposals.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2016.081615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJQET.2016.081627
S. A. Vaghefi, A. Amiri
In this paper, a control chart is proposed to detect changes in the covariance matrix of a multivariate normal process, when sample size is one. The proposed chart statistic is constructed based on the exponentially weighted form of sample covariance matrix given by individual observation over time. Distance between the values of variance and covariance components in this multivariate exponentially weighted moving sample covariance matrix and, the in-control corresponding elements of process variance-covariance matrix provides a basis for process variability monitoring. The statistical performance of the proposed method is evaluated through the use of a Monte Carlo simulation. The results show the superiority of the proposed control chart performance especially in the case of incremental changes in covariance matrix.
{"title":"Multivariate exponentially weighted moving sample covariance control chart for monitoring covariance matrix","authors":"S. A. Vaghefi, A. Amiri","doi":"10.1504/IJQET.2016.081627","DOIUrl":"https://doi.org/10.1504/IJQET.2016.081627","url":null,"abstract":"In this paper, a control chart is proposed to detect changes in the covariance matrix of a multivariate normal process, when sample size is one. The proposed chart statistic is constructed based on the exponentially weighted form of sample covariance matrix given by individual observation over time. Distance between the values of variance and covariance components in this multivariate exponentially weighted moving sample covariance matrix and, the in-control corresponding elements of process variance-covariance matrix provides a basis for process variability monitoring. The statistical performance of the proposed method is evaluated through the use of a Monte Carlo simulation. The results show the superiority of the proposed control chart performance especially in the case of incremental changes in covariance matrix.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2016.081627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJQET.2016.081636
M. Maleki, F. Maleki, Adel Khati Dizabadi
In recent years, the quality practitioners have concentrated on exploring the effect of measurement errors on the performance of control charts. To the best of our knowledge, the effect of measurement errors on simultaneous monitoring of the process mean and the process variability is neglected in the literature. In this paper, the effect of measurement errors on detecting and diagnosing performance of one of the most common simultaneous monitoring approaches in the literature is investigated. Two approaches namely multiple measurement approach as well as increasing sample size are suggested for compensating for the effect of measurement errors. The results of simulation study show that the measurement errors can adversely affect the detecting performance of maximum exponentially weighted moving average and mean squared deviation (MAX-EWMAMS) control chart while the effect of measurement errors on diagnosing performance of this control chart is negligible. The results also represent that both tasking multiple measurement on each sample point and increasing sample size can adequately compensate for the measurement errors effect.
{"title":"Decreasing the effect of measurement errors on detecting and diagnosing performance of MAX-EWMAMS control chart in Phase II","authors":"M. Maleki, F. Maleki, Adel Khati Dizabadi","doi":"10.1504/IJQET.2016.081636","DOIUrl":"https://doi.org/10.1504/IJQET.2016.081636","url":null,"abstract":"In recent years, the quality practitioners have concentrated on exploring the effect of measurement errors on the performance of control charts. To the best of our knowledge, the effect of measurement errors on simultaneous monitoring of the process mean and the process variability is neglected in the literature. In this paper, the effect of measurement errors on detecting and diagnosing performance of one of the most common simultaneous monitoring approaches in the literature is investigated. Two approaches namely multiple measurement approach as well as increasing sample size are suggested for compensating for the effect of measurement errors. The results of simulation study show that the measurement errors can adversely affect the detecting performance of maximum exponentially weighted moving average and mean squared deviation (MAX-EWMAMS) control chart while the effect of measurement errors on diagnosing performance of this control chart is negligible. The results also represent that both tasking multiple measurement on each sample point and increasing sample size can adequately compensate for the measurement errors effect.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2016.081636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1504/IJQET.2016.081647
S. Bommer, A. Badiru
Quality is a multidimensional characteristic of a product. As such, the various factors that impinge on quality must be taken into account when designing new products or managing the production of an existing product. The cost of quality is a tangible and noticeable testament to the upfront investments that an organisation has committed to a product. The cost of achieving and sustaining an acceptable level of product quality must, therefore, be recognised as the cornerstone of manufacturing operations. Of the many factors of importance in the pursuit of better product quality, cognitive loading is the one that is most often ignored or not recognised. In this paper, we present a methodology of assessing the impact of cognitive loading on the manufacturing cost of quality.
{"title":"Quality Insights: Impact of cognitive load on the manufacturing cost of quality","authors":"S. Bommer, A. Badiru","doi":"10.1504/IJQET.2016.081647","DOIUrl":"https://doi.org/10.1504/IJQET.2016.081647","url":null,"abstract":"Quality is a multidimensional characteristic of a product. As such, the various factors that impinge on quality must be taken into account when designing new products or managing the production of an existing product. The cost of quality is a tangible and noticeable testament to the upfront investments that an organisation has committed to a product. The cost of achieving and sustaining an acceptable level of product quality must, therefore, be recognised as the cornerstone of manufacturing operations. Of the many factors of importance in the pursuit of better product quality, cognitive loading is the one that is most often ignored or not recognised. In this paper, we present a methodology of assessing the impact of cognitive loading on the manufacturing cost of quality.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2016.081647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-11DOI: 10.1504/ijqet.2015.071657
A. Badiru, Anna E. Maloney
Measurement is as important in quality management as it is in many aspects of human endeavours. Take, for example, a business that is attempting to formulate a yearly budget. This business cannot create a budget out of thin air; it must use qualitative and quantitative assessments based on interpretations and equations. The exact methods businesses use to formulate their budgets may vary, but they should all include statistical analysis in order to achieve accuracy and precision. Budget management and quality management are analogous with respect to the application of analytical techniques to make decisions. This paper capitalises on that relationship to apply budget allocation and rationing techniques to quality. This paper presents an analytical approach to measurement and quality rationing in order to meet quality goals. Quality on a scale of measurement is the premise of this paper.
{"title":"Quality Insights: Measurement and quality rationing: an analytical approach","authors":"A. Badiru, Anna E. Maloney","doi":"10.1504/ijqet.2015.071657","DOIUrl":"https://doi.org/10.1504/ijqet.2015.071657","url":null,"abstract":"Measurement is as important in quality management as it is in many aspects of human endeavours. Take, for example, a business that is attempting to formulate a yearly budget. This business cannot create a budget out of thin air; it must use qualitative and quantitative assessments based on interpretations and equations. The exact methods businesses use to formulate their budgets may vary, but they should all include statistical analysis in order to achieve accuracy and precision. Budget management and quality management are analogous with respect to the application of analytical techniques to make decisions. This paper capitalises on that relationship to apply budget allocation and rationing techniques to quality. This paper presents an analytical approach to measurement and quality rationing in order to meet quality goals. Quality on a scale of measurement is the premise of this paper.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijqet.2015.071657","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-11DOI: 10.1504/ijqet.2015.071653
A. Amiri, samrad Jafarian-Namin
The C control chart is mostly used to monitor the number of non-conformities per inspection unit of constant size. It is known that the classic C-chart control limits often experience a high false alarm rate and thus lead to the increase of unnecessary costs of inspection. Among many works performed to improve C-charts, this paper presents the optimised control limits approach to the C-chart. In addition, different C-charts are evaluated through factors related to the design of the control chart. Optimally selecting design parameters depends on several process parameters from statistical and/or economic aspects in the literature. This study presents multi-objective economic-statistical design of different C control charts under single assignable cause. An algorithm using the data envelopment analysis (DEA) is employed to solve the models. A numerical example is used to illustrate the algorithm procedure and to evaluate the performances of different designs.
{"title":"Evaluating multi-objective economic-statistical design of attribute C control charts for monitoring the number of non-conformities","authors":"A. Amiri, samrad Jafarian-Namin","doi":"10.1504/ijqet.2015.071653","DOIUrl":"https://doi.org/10.1504/ijqet.2015.071653","url":null,"abstract":"The C control chart is mostly used to monitor the number of non-conformities per inspection unit of constant size. It is known that the classic C-chart control limits often experience a high false alarm rate and thus lead to the increase of unnecessary costs of inspection. Among many works performed to improve C-charts, this paper presents the optimised control limits approach to the C-chart. In addition, different C-charts are evaluated through factors related to the design of the control chart. Optimally selecting design parameters depends on several process parameters from statistical and/or economic aspects in the literature. This study presents multi-objective economic-statistical design of different C control charts under single assignable cause. An algorithm using the data envelopment analysis (DEA) is employed to solve the models. A numerical example is used to illustrate the algorithm procedure and to evaluate the performances of different designs.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijqet.2015.071653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-11DOI: 10.1504/IJQET.2015.071656
Karim Atashgar, A. Amiri, Mahdi Keramatee Nejad
Profile monitoring is effectively used in a case where the response variable is measured along with the corresponding value of an explanatory variable(s). Profile monitoring allows quality engineers to monitor performance of a process statistically considering a functional relationship at a given time. Although several papers can be found in the literature approached nonlinear profile monitoring, to the best of the authors' knowledge, there is not any researches in monitoring Allan variance nonlinear profile approaching artificial neural network (ANN). ANN capabilities help quality engineers to monitor complex nonlinear profiles in real cases effectively. In this paper an ANN model is proposed to monitor the nonlinear profile of Allan variance. Allan variance is a measure of stability of tools such as oscillator and amplifier. The proposed ANN model not only is capable to identify an out-of-control condition, but also the model is capable to diagnose the parameter(s) responsible to the out-of-control condition. A numerical example is considered to evaluate the performance of the proposed ANN when the process experiences different shift sizes. The evaluation of the performance is investigated using average run length (ARL) and correct classification criteria.
{"title":"Monitoring Allan variance nonlinear profile using artificial neural network approach","authors":"Karim Atashgar, A. Amiri, Mahdi Keramatee Nejad","doi":"10.1504/IJQET.2015.071656","DOIUrl":"https://doi.org/10.1504/IJQET.2015.071656","url":null,"abstract":"Profile monitoring is effectively used in a case where the response variable is measured along with the corresponding value of an explanatory variable(s). Profile monitoring allows quality engineers to monitor performance of a process statistically considering a functional relationship at a given time. Although several papers can be found in the literature approached nonlinear profile monitoring, to the best of the authors' knowledge, there is not any researches in monitoring Allan variance nonlinear profile approaching artificial neural network (ANN). ANN capabilities help quality engineers to monitor complex nonlinear profiles in real cases effectively. In this paper an ANN model is proposed to monitor the nonlinear profile of Allan variance. Allan variance is a measure of stability of tools such as oscillator and amplifier. The proposed ANN model not only is capable to identify an out-of-control condition, but also the model is capable to diagnose the parameter(s) responsible to the out-of-control condition. A numerical example is considered to evaluate the performance of the proposed ANN when the process experiences different shift sizes. The evaluation of the performance is investigated using average run length (ARL) and correct classification criteria.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.071656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-11DOI: 10.1504/IJQET.2015.071652
Shah M. Limon, O. Yadav, Bimal P. Nepal, L. Monplaisir
The rapid evolution in technology is leading to complex engineering systems confounding the problems of failure analysis processes. Earlier efforts have concentrated on failure analysis of components but have proven insufficient for analysing the complex physical systems. In this paper, a cognitive map-based approach suggested by Augustine et al. (2012) for system interaction failure analysis is further improved to develop populated network structure considering structural variation. The numerical values to causal arcs are assigned considering qualitative linguistic variables to represent causal relationship between two nodes. The structural variability in cognitive maps due to input from experts or sources is addressed by effectively combining the cognitive map diagrams (networks) and related information received from several sources into single network diagram. A combined adjacency matrix is developed considering credibility weight assigned to each experts. The proposed approach is capable of capturing system interaction failures as well as component failures, which is useful at the early stage of design. A relatively complex physical engineering system of fuel injection is presented as an example to demonstrate the effectiveness of the proposed approach.
技术的快速发展导致复杂的工程系统混淆了失效分析过程的问题。早期的工作集中在部件的失效分析上，但已被证明不足以分析复杂的物理系统。本文进一步改进了Augustine et al.(2012)提出的基于认知地图的系统交互失效分析方法，开发了考虑结构变异的填充网络结构。考虑定性语言变量来表示两个节点之间的因果关系，给出因果曲线的数值。通过有效地将认知地图图(网络)和从多个来源获得的相关信息组合成单个网络图，解决了由于专家或来源输入而导致的认知地图结构变异性。考虑每个专家的可信度权重，建立了一个组合邻接矩阵。所提出的方法能够捕获系统交互故障以及组件故障，这在设计的早期阶段非常有用。以一个较为复杂的燃油喷射物理工程系统为例，验证了该方法的有效性。
{"title":"Enabling comprehensive failure analysis of complex physical system using cognitive map-based approach","authors":"Shah M. Limon, O. Yadav, Bimal P. Nepal, L. Monplaisir","doi":"10.1504/IJQET.2015.071652","DOIUrl":"https://doi.org/10.1504/IJQET.2015.071652","url":null,"abstract":"The rapid evolution in technology is leading to complex engineering systems confounding the problems of failure analysis processes. Earlier efforts have concentrated on failure analysis of components but have proven insufficient for analysing the complex physical systems. In this paper, a cognitive map-based approach suggested by Augustine et al. (2012) for system interaction failure analysis is further improved to develop populated network structure considering structural variation. The numerical values to causal arcs are assigned considering qualitative linguistic variables to represent causal relationship between two nodes. The structural variability in cognitive maps due to input from experts or sources is addressed by effectively combining the cognitive map diagrams (networks) and related information received from several sources into single network diagram. A combined adjacency matrix is developed considering credibility weight assigned to each experts. The proposed approach is capable of capturing system interaction failures as well as component failures, which is useful at the early stage of design. A relatively complex physical engineering system of fuel injection is presented as an example to demonstrate the effectiveness of the proposed approach.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.071652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-11DOI: 10.1504/IJQET.2015.071651
Azadeh Adibi, C. Borror, D. Montgomery
In this study, a p-value-based method for monitoring polynomial and nonlinear profiles in Phase II process monitoring is proposed. Performance of the proposed method is evaluated using the average run length criterion under different shifts in the model parameters. In this approach, the p-values are calculated for all subgroups within a sample. If any p-value is less than a prespecified threshold, the chart signals out of control. The main advantage of the proposed method is its ease of implementation in practice. Moreover, in this method, only one control chart is needed for routine monitoring of the model parameters. Only if an out-of-control signal is observed, then individual monitoring of the regression model parameters is needed. Performance of the proposed approach is compared to the T² method. Results of a simulation study on the proposed p-value approach are provided.
{"title":"Phase II monitoring of polynomial and nonlinear profiles using a p-value approach","authors":"Azadeh Adibi, C. Borror, D. Montgomery","doi":"10.1504/IJQET.2015.071651","DOIUrl":"https://doi.org/10.1504/IJQET.2015.071651","url":null,"abstract":"In this study, a p-value-based method for monitoring polynomial and nonlinear profiles in Phase II process monitoring is proposed. Performance of the proposed method is evaluated using the average run length criterion under different shifts in the model parameters. In this approach, the p-values are calculated for all subgroups within a sample. If any p-value is less than a prespecified threshold, the chart signals out of control. The main advantage of the proposed method is its ease of implementation in practice. Moreover, in this method, only one control chart is needed for routine monitoring of the model parameters. Only if an out-of-control signal is observed, then individual monitoring of the regression model parameters is needed. Performance of the proposed approach is compared to the T² method. Results of a simulation study on the proposed p-value approach are provided.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.071651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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