Pub Date : 2015-05-13DOI: 10.1504/ijqet.2015.069235
Srinivasan Balan
Several approaches are used for simultaneous optimisation of multi-response experiments to predict the significant parameters in the area of design of experiments. Researchers focus more on the mathematical models and numerical analysis to find the significant factors contribute to the problem statement both in problem solving approaches and process characterisation. Shainin proposed a set of tools and techniques to improve the quality both offline and online quality assurance. The purpose of using all these set of tools is to find the vital few dominant cause called red X and second dominant cause called pink X which affects the manufacturing system or quality assurance. This paper deals with the application of Grey relational analysis to combine the multi responses into a single response which affects the problem statement and predicts the optimum parameter levels for confirmation experiment. A case study is illustrated to explain the steps and discussed in detail. This shows the application feasibility of the Grey relational analysis with Shainin design of experiments in combination to improve the quality characteristics and process characterisation.
{"title":"Multi-response optimisation using Grey relational analysis and Shainin design of experiments","authors":"Srinivasan Balan","doi":"10.1504/ijqet.2015.069235","DOIUrl":"https://doi.org/10.1504/ijqet.2015.069235","url":null,"abstract":"Several approaches are used for simultaneous optimisation of multi-response experiments to predict the significant parameters in the area of design of experiments. Researchers focus more on the mathematical models and numerical analysis to find the significant factors contribute to the problem statement both in problem solving approaches and process characterisation. Shainin proposed a set of tools and techniques to improve the quality both offline and online quality assurance. The purpose of using all these set of tools is to find the vital few dominant cause called red X and second dominant cause called pink X which affects the manufacturing system or quality assurance. This paper deals with the application of Grey relational analysis to combine the multi responses into a single response which affects the problem statement and predicts the optimum parameter levels for confirmation experiment. A case study is illustrated to explain the steps and discussed in detail. This shows the application feasibility of the Grey relational analysis with Shainin design of experiments in combination to improve the quality characteristics and process characterisation.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijqet.2015.069235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692412","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 : 2015-05-13DOI: 10.1504/IJQET.2015.069244
A. Badiru
Learning curves are used extensively in business, science, technology, engineering, and industry to predict system performance over time. Although most of the early developments and applications were in the area of production engineering, general applications can be found in all areas of applications. Quality engineering and technology, in particular, offer a fertile area for the application of learning curves. This paper applies the concept of half-life of learning curves to quality management. This is useful for predictive measures of quality system performance. Half-life is the amount of time it takes for a quantity to diminish to half of its original size through natural processes. The approach of half-life computation provides additional decision tool for researchers and practitioners in quality engineering and technology. Derivation of the half-life equations of learning curves can reveal more about the properties of the various curves, with respect to the unique life-cycle property of product quality.
{"title":"Quality Insights: Learning, forgetting, and relearning quality: a half-life learning curve modelling approach","authors":"A. Badiru","doi":"10.1504/IJQET.2015.069244","DOIUrl":"https://doi.org/10.1504/IJQET.2015.069244","url":null,"abstract":"Learning curves are used extensively in business, science, technology, engineering, and industry to predict system performance over time. Although most of the early developments and applications were in the area of production engineering, general applications can be found in all areas of applications. Quality engineering and technology, in particular, offer a fertile area for the application of learning curves. This paper applies the concept of half-life of learning curves to quality management. This is useful for predictive measures of quality system performance. Half-life is the amount of time it takes for a quantity to diminish to half of its original size through natural processes. The approach of half-life computation provides additional decision tool for researchers and practitioners in quality engineering and technology. Derivation of the half-life equations of learning curves can reveal more about the properties of the various curves, with respect to the unique life-cycle property of product quality.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.069244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692456","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 : 2015-05-13DOI: 10.1504/IJQET.2015.069231
Adebolaji A. Jobi-Taiwo, E. Cudney
Fault identification is fundamental to condition monitoring. An identification method for a single fault is unbalanced as there are usually multiple possible failures involved when considering a system. This paper presents a method for applying the Mahalanobis-Taguchi system (MTS) in a multiclass problem space. MTS provides a means of extracting information in a multidimensional system and integrating information from different variables into a single composite metric. MTS is used to construct reference scales by creating individual measurement scales for each class. These measurement scales are based on the Mahalanobis distance (MD) for each sample. Orthogonal arrays (OA) and signal-to-noise (SN) ratio are used to identify variables of importance and these variables are used to construct a reduced model of the measurement scale. By reducing the dimensionality of the problem, less variables are tracked which reduces the cost of the system monitoring. A classification threshold based on 1.5 sigma shift from the centre of the measurement scales was utilised for each class. In order to evaluate the effectiveness of the method presented, a case on multiple fault class of manufacturing a steel plate is studied, and results indicate the practicality of the method in industrial applications.
{"title":"Mahalanobis-Taguchi System for Multiclass Classification of Steel Plates Fault","authors":"Adebolaji A. Jobi-Taiwo, E. Cudney","doi":"10.1504/IJQET.2015.069231","DOIUrl":"https://doi.org/10.1504/IJQET.2015.069231","url":null,"abstract":"Fault identification is fundamental to condition monitoring. An identification method for a single fault is unbalanced as there are usually multiple possible failures involved when considering a system. This paper presents a method for applying the Mahalanobis-Taguchi system (MTS) in a multiclass problem space. MTS provides a means of extracting information in a multidimensional system and integrating information from different variables into a single composite metric. MTS is used to construct reference scales by creating individual measurement scales for each class. These measurement scales are based on the Mahalanobis distance (MD) for each sample. Orthogonal arrays (OA) and signal-to-noise (SN) ratio are used to identify variables of importance and these variables are used to construct a reduced model of the measurement scale. By reducing the dimensionality of the problem, less variables are tracked which reduces the cost of the system monitoring. A classification threshold based on 1.5 sigma shift from the centre of the measurement scales was utilised for each class. In order to evaluate the effectiveness of the method presented, a case on multiple fault class of manufacturing a steel plate is studied, and results indicate the practicality of the method in industrial applications.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.069231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692343","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 : 2015-05-13DOI: 10.1504/ijqet.2015.069234
R. Afolabi, P. A. Osanaiye, O. Akpa
In statistical process control, detecting if the process is in control and the position of shift in an out-of-control process are critical research problems. If the normality assumption is satisfied, work has advanced in detecting shifts in mean and/or variance. However, the normality assumption is often not satisfied in many real life situations. We suggest a non-parametric Lepage-type change-point (LCP) control chart for jointly detecting process shifts in mean and variance, under non-normality. A comparison between our proposed method and a generalised likelihood ratio (GLR)-based method was made. Process data were simulated following normal and Laplace distributions. The performances of LCP and GLR were assessed and presented, using evaluated average run lengths, under the distributions considered. The LCP competed favourably with the GLR in a normal distribution. However, LCP outperformed GLR under the heavy-tailed distribution considered. We recommend the new approach for short-run situations where the underlying distributions are usually unknown.
{"title":"Non-parametric change-point approach for monitoring shifts in process location and variability","authors":"R. Afolabi, P. A. Osanaiye, O. Akpa","doi":"10.1504/ijqet.2015.069234","DOIUrl":"https://doi.org/10.1504/ijqet.2015.069234","url":null,"abstract":"In statistical process control, detecting if the process is in control and the position of shift in an out-of-control process are critical research problems. If the normality assumption is satisfied, work has advanced in detecting shifts in mean and/or variance. However, the normality assumption is often not satisfied in many real life situations. We suggest a non-parametric Lepage-type change-point (LCP) control chart for jointly detecting process shifts in mean and variance, under non-normality. A comparison between our proposed method and a generalised likelihood ratio (GLR)-based method was made. Process data were simulated following normal and Laplace distributions. The performances of LCP and GLR were assessed and presented, using evaluated average run lengths, under the distributions considered. The LCP competed favourably with the GLR in a normal distribution. However, LCP outperformed GLR under the heavy-tailed distribution considered. We recommend the new approach for short-run situations where the underlying distributions are usually unknown.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijqet.2015.069234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692394","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 : 2015-05-13DOI: 10.1504/ijqet.2015.069206
V. S. Patyal, S. Modgil, Maddulety Koilakuntla
This paper presents the findings of an experimental investigation conducted for understanding the effect of pH and temperature on three response variables viz. 'loss on ignition' (LoI), zinc oxide (ZnO) and borate (B2O3) in the output of a chemical process. The Taguchi technique and response surface methodology (RSM) have been used to accomplish the objectives of the study. The L4 orthogonal array with two replications for the Taguchi technique and central composite design (CCD) with two replications for response surface were used for conducting the experiments. The analysis was performed using the Taguchi technique, RSM with general linear model (ANOVA) revealed that temperature and its quadratic effect produced significant impact in achieving target values of LoI and B2O3 content. Further, the data analysis reported that effect of pH on three response variables (LoI, ZnO, B2O3) was insignificant in nature, Also, ZnO content was not significant either with pH or temperature.
{"title":"Method-comparison studies with Taguchi and response surface methodologies for chemical process optimisation: a case study","authors":"V. S. Patyal, S. Modgil, Maddulety Koilakuntla","doi":"10.1504/ijqet.2015.069206","DOIUrl":"https://doi.org/10.1504/ijqet.2015.069206","url":null,"abstract":"This paper presents the findings of an experimental investigation conducted for understanding the effect of pH and temperature on three response variables viz. 'loss on ignition' (LoI), zinc oxide (ZnO) and borate (B2O3) in the output of a chemical process. The Taguchi technique and response surface methodology (RSM) have been used to accomplish the objectives of the study. The L4 orthogonal array with two replications for the Taguchi technique and central composite design (CCD) with two replications for response surface were used for conducting the experiments. The analysis was performed using the Taguchi technique, RSM with general linear model (ANOVA) revealed that temperature and its quadratic effect produced significant impact in achieving target values of LoI and B2O3 content. Further, the data analysis reported that effect of pH on three response variables (LoI, ZnO, B2O3) was insignificant in nature, Also, ZnO content was not significant either with pH or temperature.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijqet.2015.069206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692318","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 : 2015-01-01DOI: 10.1504/IJQET.2015.075773
M. Bashiri, F. Sogandi
There are many real world applications consisting of finding optimal design variables that yields desirable values for stochastic responses. Finding the levels of controllable factors is an important process control stage to achieve desired specification of the output. On other hand, finding the optimal setting will be more important when there are more than one signal in dynamic systems. Also difficulties may arise particularly for multiple responses when there are stochastic target profiles in each level of signal factor. In this regard, linear regression models for response mean, variance and covariance of multiple responses in each level of signals are obtained. Then optimal settings of controllable factors are extracted by the goal programming applied in an example from the literature. Then optimal setting of controllable factors is determined by stochastic optimisation. Also, the sensitivity analysis is performed to show efficiency of the proposed approach in case of existing stochastic target profiles in multiple response optimisation.
{"title":"The optimisation of bi-signal multi-response systems with stochastic target profiles using goal programming","authors":"M. Bashiri, F. Sogandi","doi":"10.1504/IJQET.2015.075773","DOIUrl":"https://doi.org/10.1504/IJQET.2015.075773","url":null,"abstract":"There are many real world applications consisting of finding optimal design variables that yields desirable values for stochastic responses. Finding the levels of controllable factors is an important process control stage to achieve desired specification of the output. On other hand, finding the optimal setting will be more important when there are more than one signal in dynamic systems. Also difficulties may arise particularly for multiple responses when there are stochastic target profiles in each level of signal factor. In this regard, linear regression models for response mean, variance and covariance of multiple responses in each level of signals are obtained. Then optimal settings of controllable factors are extracted by the goal programming applied in an example from the literature. Then optimal setting of controllable factors is determined by stochastic optimisation. Also, the sensitivity analysis is performed to show efficiency of the proposed approach in case of existing stochastic target profiles in multiple response optimisation.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.075773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692848","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 : 2015-01-01DOI: 10.1504/IJQET.2015.075781
Boutheina Bannour
This paper presents a bootstrapped efficiency score through estimating the DEA model. We focus on an input-oriented approach using returns to scale. By using input and output data on 20 Tunisian banks over the period 1990-2010, we construct more accurate bootstrapped confidence intervals; hence, we provide more reliable and useful results for decision-makers. Moreover, we find that 4 out of 20 Tunisian banks are perfectly efficient using a data-driven estimate of the smoothing parameter.
{"title":"How to bootstrap in non-parametric frontier models? Technical efficiency of Tunisian banks","authors":"Boutheina Bannour","doi":"10.1504/IJQET.2015.075781","DOIUrl":"https://doi.org/10.1504/IJQET.2015.075781","url":null,"abstract":"This paper presents a bootstrapped efficiency score through estimating the DEA model. We focus on an input-oriented approach using returns to scale. By using input and output data on 20 Tunisian banks over the period 1990-2010, we construct more accurate bootstrapped confidence intervals; hence, we provide more reliable and useful results for decision-makers. Moreover, we find that 4 out of 20 Tunisian banks are perfectly efficient using a data-driven estimate of the smoothing parameter.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.075781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692930","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 : 2015-01-01DOI: 10.1504/IJQET.2015.075771
A. Badiru, John J. Elshaw, A. BadiruIbrahim
Customer preference is a key element of determining the characteristics of a product. Manufacturers who strive to satisfy customer needs must adequately assess the preferences of the customers. In the distant past, manufacturers might not have paid sufficient attention to customer preferences. They, rather, supplied the market with what their manufacturing capabilities could provide. The market is far more astute and discriminating in the modern era of global competition. Manufacturers must, thus, respond accordingly. This paper presents an analytical approach to assessing customer preferences. A case study from the US automotive industry is presented in the paper as a good benchmark or template for pursuing customer preference assessment using a quantification methodology. Such a quantification approach is useful for an analytical assessment of customer needs, wants, and desires in the marketplace.
{"title":"Quality Insights: Systems-based product quality assessment for customer preferences","authors":"A. Badiru, John J. Elshaw, A. BadiruIbrahim","doi":"10.1504/IJQET.2015.075771","DOIUrl":"https://doi.org/10.1504/IJQET.2015.075771","url":null,"abstract":"Customer preference is a key element of determining the characteristics of a product. Manufacturers who strive to satisfy customer needs must adequately assess the preferences of the customers. In the distant past, manufacturers might not have paid sufficient attention to customer preferences. They, rather, supplied the market with what their manufacturing capabilities could provide. The market is far more astute and discriminating in the modern era of global competition. Manufacturers must, thus, respond accordingly. This paper presents an analytical approach to assessing customer preferences. A case study from the US automotive industry is presented in the paper as a good benchmark or template for pursuing customer preference assessment using a quantification methodology. Such a quantification approach is useful for an analytical assessment of customer needs, wants, and desires in the marketplace.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.075771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692792","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 : 2015-01-01DOI: 10.1504/IJQET.2015.075774
D. K. Gangeshwer, G. Shankar
This paper presents graphical evaluation and review technique (GERT) analysis of the deferred life test sampling plan based on gamma failure. GERT approach which has been successfully used by Ohta (1978) for studying some quality control problems. The idea of using what is known as deferred sampling plan is due to Vaerst (1980). A brief account of GERT methodology and its application in quality control has been given by Shankar (1993). The advantage of the GERT analysis in the present context is two folded. Firstly, this procedure gives a visual picture of the dynamics of the inspection system and secondly, it offers a through characterisation of the plan. The formula of operating characteristics (OC) function and average sample number (ASN) function is derived and illustrated numerically. Lastly, tables have been provided to determine the smallest sample size n necessary to assure a certain mean life or quality of the product.
{"title":"GERT analysis of the deferred life test sampling plan based on gamma failure","authors":"D. K. Gangeshwer, G. Shankar","doi":"10.1504/IJQET.2015.075774","DOIUrl":"https://doi.org/10.1504/IJQET.2015.075774","url":null,"abstract":"This paper presents graphical evaluation and review technique (GERT) analysis of the deferred life test sampling plan based on gamma failure. GERT approach which has been successfully used by Ohta (1978) for studying some quality control problems. The idea of using what is known as deferred sampling plan is due to Vaerst (1980). A brief account of GERT methodology and its application in quality control has been given by Shankar (1993). The advantage of the GERT analysis in the present context is two folded. Firstly, this procedure gives a visual picture of the dynamics of the inspection system and secondly, it offers a through characterisation of the plan. The formula of operating characteristics (OC) function and average sample number (ASN) function is derived and illustrated numerically. Lastly, tables have been provided to determine the smallest sample size n necessary to assure a certain mean life or quality of the product.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.075774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692870","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 : 2015-01-01DOI: 10.1504/IJQET.2015.075776
A. Sutrisno, I. Gunawan, H. Khorshidi, Stenly Tangkuman
Implementing sustainable manufacturing practice requires efficiency of the resource utilisation and activities which add value to the operations. From this point of view, development of an improved methodology to access the criticality of non-added value (waste) is important and believed to support the realisation of sustainable manufacturing operation. While previous studies on improving methodology to support sustainable operation from product and process design are abundantly available in the references, the contribution from maintenance field is in contrary. The goal of this study is to modify the quality improvement tool, failure mode and effect analysis (FMEA) to access the criticality of waste in maintenance operation. In an attempt to realise the above goal, an empirical study to propose the theoretical and actual maintenance waste from industrial practice is undertaken. In order to assist maintenance decision maker to quantify criticality of maintenance waste occurrence, a new model to rank waste maintenance mode, called the waste priority number (WPN) is given. Illustrative on using model for practical purpose is given.
{"title":"A new modified FMEA model for ranking the risk of maintenance waste considering hierarchy of root causes and effects","authors":"A. Sutrisno, I. Gunawan, H. Khorshidi, Stenly Tangkuman","doi":"10.1504/IJQET.2015.075776","DOIUrl":"https://doi.org/10.1504/IJQET.2015.075776","url":null,"abstract":"Implementing sustainable manufacturing practice requires efficiency of the resource utilisation and activities which add value to the operations. From this point of view, development of an improved methodology to access the criticality of non-added value (waste) is important and believed to support the realisation of sustainable manufacturing operation. While previous studies on improving methodology to support sustainable operation from product and process design are abundantly available in the references, the contribution from maintenance field is in contrary. The goal of this study is to modify the quality improvement tool, failure mode and effect analysis (FMEA) to access the criticality of waste in maintenance operation. In an attempt to realise the above goal, an empirical study to propose the theoretical and actual maintenance waste from industrial practice is undertaken. In order to assist maintenance decision maker to quantify criticality of maintenance waste occurrence, a new model to rank waste maintenance mode, called the waste priority number (WPN) is given. Illustrative on using model for practical purpose is given.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJQET.2015.075776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66692916","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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