Search results

1 – 10 of 229
Article
Publication date: 10 August 2015

D. R. Prajapati and Sukhraj Singh

The purpose of this paper is to counter autocorrelation by designing the chart, using warning limits. Various optimal schemes of modified chart are proposed for various…

Abstract

Purpose

The purpose of this paper is to counter autocorrelation by designing the chart, using warning limits. Various optimal schemes of modified chart are proposed for various sample sizes (n) at levels of correlation (Φ) of 0.00, 0.475 and 0.95. These optimal schemes of modified chart are compared with the double sampling (DS) chart, suggested by Costa and Claro (2008).

Design/methodology/approach

The performance of the chart is measured in terms of the average run length (ARL) that is the average number of samples before getting an out-of-control signal. Ultimately, due to the effect of autocorrelation among the data, the performance of the chart is suspected. The ARLs at various sets of parameters of the chart are computed by simulation, using MATLAB. The suggested optimal schemes are simpler schemes with limited number of parameters and smaller sample size (n=4) and this simplicity makes them very helpful in quality control.

Findings

The suggested optimal schemes of modified chart are compared with the DS chart, suggested by Costa and Claro (2008). It is concluded that the modified chart outperforms the DS chart at various levels of correlation (Φ) and shifts in the process mean. The simplicity in the design of modified chart, makes it versatile for many industries.

Research limitations/implications

Both the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for chart can be developed for variable sample size and for variable sampling intervals. The optimal schemes can also be explored for cumulative sum and exponentially weighted moving average charts.

Practical implications

The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation the suggested control chart parameters can be applied. The understandable and robust design of modified chart makes it usable for industrial quality control.

Social implications

The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society, as suggested by Taguchi (1985).

Originality/value

Although it is the extension of previous work but it can be applied to various manufacturing industries as well as service industries, where the data are positively correlated and normally distributed.

Details

The TQM Journal, vol. 27 no. 5
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 15 March 2011

D.R. Prajapati

The concept of the proposed R chart is based on the sum of chi squares (χ2). The average run lengths (ARLs) of the proposed R chart are computed and compared with the ARLs…

Abstract

Purpose

The concept of the proposed R chart is based on the sum of chi squares (χ2). The average run lengths (ARLs) of the proposed R chart are computed and compared with the ARLs of a standard R chart, Shewhart variance chart proposed by Chang and Gan, a CUSUM range chart (with and without FIR feature) proposed by Chang and Gan and also with an EWMA range chart proposed by Crowder and Hamilton for various chart parameters. This paper aims to show that only FIR CUSUM schemes perform better than the proposed R chart but other CUSUM and EWMA schemes are less efficient than the proposed R chart.

Design/methodology/approach

The concept of the proposed R chart is based on the sum of chi squares (χ2). The proposed R chart divides the plot area into three regions, namely: outright rejection region; outright acceptance region; and transition region. The NULL hypothesis is rejected if a point falls beyond the control limit, and accepted if it falls below the warning limit. However, when a point falls beyond the warning limit, but not beyond the control limit, the decision is taken on the basis of individual observations of the previous H samples, which are considered to evaluate statistic U, that is the sum of chi squares. The NULL hypothesis is rejected if U exceeds a predefined value (U*) and accepted otherwise.

Findings

The comparisons also show that the CUSUM, EWMA and proposed R charts outperform the Shewhart R chart by a substantial amount. It is concluded that only FIR CUSUM schemes perform better than the proposed R chart, as it is second in ranking. The other CUSUM and EWMA schemes are less efficient than the proposed R chart.

Research limitations/implications

CUSUM and EWMA charts can catch a small shift in the process average but they are not efficient to catch a large shift. Many researchers have also pointed out that these charts' applicability is limited to the chemical industries. Another limitation of CUSUM and EWMA charts is that they can catch the shift only when there is a single and sustained shift in the process average. If the shift is not sustained, then they will not be effective.

Practical implications

Many difficulties related to the operation and design of CUSUM and EWMA control charts are greatly reduced by providing a simple and accurate proposed scheme. The performance characteristics (ARLs) of the proposed charts described in this paper are very much comparable with FIR CUSUM, CUSUM, EWMA and other charts. It can be concluded that, instead of considering many chart parameters used in CUSUM and EWMA charts, it is better to consider a simple and more effective scheme, because a control chart loses its simplicity with multiple parameters. Moreover, practitioners may also experience difficulty in using these charts in production processes.

Originality/value

It is a modification of the Shewhart Range Chart but it is more effective than the Shewhart Range chart, as shown in the research paper.

Details

International Journal of Quality & Reliability Management, vol. 28 no. 3
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 6 June 2016

D.R. Prajapati and Sukhraj Singh

It is found that the process outputs from most of the industries are correlated and the performance of X-bar chart deteriorates when the level of correlation increases…

Abstract

Purpose

It is found that the process outputs from most of the industries are correlated and the performance of X-bar chart deteriorates when the level of correlation increases. The purpose of this paper is to compute the level of correlation among the observations of the weights of tablets of a pharmaceutical industry by using modified X-bar chart.

Design/methodology/approach

The design of the modified X-bar chart is based upon the sum of χ2s, using warning limits and the performance of the chart is measured in terms of average run lengths (ARLs). The ARLs at various sets of parameters of the modified X-bar chart are computed; using MATLAB software at the given mean and standard deviation.

Findings

The performance of the modified X-bar chart is computed for sample sizes of four. ARLs of optimal schemes of X-bar chart for sample size of four are computed. Various optimal schemes of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.00, 0.25, 0.50, 0.75 and 1.00 are presented in this paper. Samples of weights of the tablets are taken from a pharmaceutical industry and computed the level of correlation among the observations of the weights of the tablets. It is found that the observations are closely resembled with the simulated observations for the level of correlation of 0.75 in this case study. The performance of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.50 and 0.75 is also compared with the conventional (Shewhart) X-bar chart and it is concluded that the modified X-bar chart performs better than Shewhart X-bar chart.

Research limitations/implications

All the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for modified X-bar chart can also be used for other industries; where the manufacturing time of products is small. This scheme may also be used for any sample sizes suitable for the industries

Practical implications

The optimal scheme of modified X-bar chart for sample size (n) of four is used according to the computed level of correlation in the observations. The simple design of modified X-bar chart makes it more useful at the shop floor level for many industries where correlation exists. The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation, the suggested control chart parameters can be used.

Social implications

The design of modified X-bar chart uses very less numbers of parameters so it can be used at the shop floor level with ease. The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society as suggested by Taguchi (1985).

Originality/value

Although; it is the extension of previous work but it can be applied to various manufacturing and service industries; where the data are correlated and normally distributed.

Details

International Journal of Quality & Reliability Management, vol. 33 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 22 May 2009

D.R. Prajapati and P.B. Mahapatra

The purpose of this paper is to introduce a new design of an R chart to catch smaller shifts in the process dispersion as well as maintaining the simplicity so that it may…

Abstract

Purpose

The purpose of this paper is to introduce a new design of an R chart to catch smaller shifts in the process dispersion as well as maintaining the simplicity so that it may be applied at shopfloor level.

Design/methodology/approach

Here a new R chart has been proposed which can overcome the limitations of Shewhart, CUSUM and EWMA range charts. The concept of this R chart is based on chi‐square (χ2) distribution. Although CUSUM and EWMA charts are very useful for catching the small shifts in the mean or standard deviation, they can catch the process shift only when there is a single and sustained shift in process average or standard deviation.

Findings

It was found that the proposed chart performs significantly better than the conventional (Shewhart) R chart, CUSUM range schemes proposed by Chang and Gan for most of the process shifts in standard deviation. The ARLs of the proposed R chart is higher than ARLs of CUSUM schemes for only ten cases out of 40. The performance of the proposed R chart has also been compared with the variance chart proposed by Chang and Gan for various shifts in standard deviation. The ARLs of the proposed R chart are compared with Chang's R chart for sample sizes of 3 and it can be concluded from the comparisons that the proposed R chart is much better than Chang's variance chart for all shift ratios for sample size of three. Many difficulties related to the operation and design of CUSUM and EWMA control charts are greatly reduced by providing a simple and accurate proposed R chart scheme. The performance characteristics (ARLs) of the proposed charts are very comparable to a great degree with FIR CUSUM, simple CUSUM and other variance charts. It can be concluded that, instead of considering many parameters, it is better to consider single sample size and single control limits because a control chart loses its simplicity with a greater number of parameters. Moreover, practitioners may also find difficulty in applying it in production processes. On the other hand, CUSUM control charts are not effective when there is a single and sustained shift in the process dispersion.

Research limitations/implications

A lot of effort has been done to develop the new range charts for monitoring the process dispersion. Various assumptions and factors affecting the performance of the R chart have been identified and taken into account. In the proposed design, the observations have been assumed independent of one another but the observations may also be assumed to be auto‐correlated with previous observations and the performance of the proposed R chart may be studied.

Originality/value

The research findings could be applied to various manufacturing and service industries as it is more effective than the conventional (Shewhart) R chart and simpler than CUSUM charts.

Details

International Journal of Quality & Reliability Management, vol. 26 no. 5
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 30 December 2019

Deoraj Prajapati and Gaurav Suman

The purpose of this paper is to implement Six Sigma approach to decrease the length of stay (LOS) of neonatal jaundice patients in an Indian government rural hospital…

Abstract

Purpose

The purpose of this paper is to implement Six Sigma approach to decrease the length of stay (LOS) of neonatal jaundice patients in an Indian government rural hospital situated in northern hill region.

Design/methodology/approach

Six Sigma’s Define–Measure–Analyse–Improve–Control procedure is applied in order to decrease the LOS of neonatal jaundice patients. The mean and standard deviation have been computed as 34.53 and 20.01 h, respectively. The cause and effect diagram is used in the “Analyse” phase of the Six Sigma. The regression analysis and GEMBA observation techniques are used to validate the causes identified through cause and effect diagram.

Findings

The waiting time for registration, waiting time for tests, waiting time for phototherapy and time for discharge implementation are the main factors that are responsible for longer LOS. Based on the identified root causes, some recommendations are suggested to the hospital administration and staff members in order to reduce the LOS.

Research limitations/implications

The present research is limited to provide recommendations to the hospital administration to reduce LOS and it entirely depends upon the implementation of the administration. However, target of administration is to reduce the LOS up to 24 h.

Practical implications

Six Sigma model will reduce bottlenecks in LOS and enhance service quality of hospital. The developed regression model will help the doctors and staff members to assess and control the LOS by controlling and minimising the independent variables.

Social implications

The project will directly provide benefits to society, as LOS will decrease and patients’ satisfaction will automatically increase.

Originality/value

Six Sigma is a developed methodology, but its application in paediatric department is very limited. This is the first ever study of applying Six Sigma for neonatal jaundice patients in India.

Details

International Journal of Health Care Quality Assurance, vol. 33 no. 1
Type: Research Article
ISSN: 0952-6862

Keywords

Article
Publication date: 26 July 2013

Sukhraj Singh and D.R. Prajapati

The purpose of this paper is to study the performance of the X‐bar chart on the basis of average run lengths (ARLs) for the positively correlated data. The ARLs at various…

Abstract

Purpose

The purpose of this paper is to study the performance of the X‐bar chart on the basis of average run lengths (ARLs) for the positively correlated data. The ARLs at various sets of parameters of the X‐bar chart are computed by simulation. The performance of the chart at the various shifts in the process mean is compared with the X‐bar chart suggested by Zang and residual chart proposed by Zang. The optimal schemes suggested in this paper are also compared with variable parameters (VP) chart and double sampling (DS) X‐bar chart suggested by Costa and Machado.

Design/methodology/approach

Positively correlated observations having normal distribution are generated with the help of the MATLAB software. The performance of the X‐bar chart in terms of ARLs at the various shifts in the process mean is compared with the X‐bar chart suggested by Zang and residual chart proposed by Zang. The optimal schemes are also compared with VP X‐bar chart and DS X‐bar chart suggested by Costa and Machado.

Findings

The suggested optimal schemes of X‐bar chart perform better at the various shifts in the process mean than the X‐bar chart suggested by Zang and residual chart suggested by Zang. It was concluded that, although the suggested schemes for X‐bar chart detect shifts later than the VP and DS X‐bar charts proposed by Costa and Machado, they involved a much smaller number of parameters that are to be adjusted. So the time required for adjustment in case of optimal scheme is very small compared to the VP and DS charts.

Research limitations/implications

The optimal schemes of X‐bar chart are developed for the normally distributed autocorrelated data. But this assumption may also be relaxed to design these schemes for autocorrelated data. Moreover, the optimal schemes for chart can be developed for variable sample size and for variable sampling intervals.

Originality/value

Although it is the extension of previous work, it can be applied to various manufacturing industries as well as service industries where the data is positively correlated and normally distributed.

Details

International Journal of Quality & Reliability Management, vol. 30 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 23 May 2008

D.R. Prajapati and P.B. Mahapatra

The purpose of this paper is to introduce a new design of the chart to catch smaller shifts in the process average as well as to maintain the simplicity like the…

Abstract

Purpose

The purpose of this paper is to introduce a new design of the chart to catch smaller shifts in the process average as well as to maintain the simplicity like the Shewhart chart so that it may be applied at shopfloor level.

Design/methodology/approach

In this paper, a new chart with two strategies is proposed which can overcome the limitations of Shewhart, CUSUM and EWMA charts. The Shewhart chart uses only two control limits to arrive at a decision to accept the Null Hypothesis (H0) or Alternative Hypothesis (H1), but in the new chart, two more limits at “K” times sample standard deviation on both sides from center line have been introduced. These limits are termed warning limits. The first strategy is based on chi‐square distribution (CSQ), while the second strategy is based on the average of sample means (ASM).

Findings

The proposed chart with “strategy ASM” shows lower average run length (ARL) values than ARLs of variable parameter (VP) chart for most of the cases. The VP chart shows little better performance than the new chart; but at large sample sizes (n) of 12 and 16. The VSS chart also shows lower ARLs but at very large sample size, which should not be used because, as far as possible, samples should be taken from a lot produced under identical conditions. The inherent feature of the new chart is its simplicity, so that it can be used without difficulty at shopfloor level as it uses only a fixed sample size and fixed sampling interval but it is very difficult to set the various chart parameters in VP and VSS charts.

Research limitations/implications

A lot of effort has been expended to develop the new strategies for monitoring the process mean. Various assumptions and factors affecting the performance of the chart have been identified and taken into account. In the proposed design, the observations have been assumed independent of one another but the observations may also be assumed to be auto‐correlated with previous observations and performance of the proposed chart may be studied.

Originality/value

The research findings could be applied to various manufacturing and service industries as it is more effective than the Shewhart chart and simpler than the VP, VSS and CUSUM charts.

Details

International Journal of Quality & Reliability Management, vol. 25 no. 5
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 4 January 2013

Sushil Kumar, P.S. Satsangi and D.R. Prajapati

The purpose of this paper is to identify the influencing factors which cause casting defects and determination of optimum value of factors to minimize these defects in a…

Abstract

Purpose

The purpose of this paper is to identify the influencing factors which cause casting defects and determination of optimum value of factors to minimize these defects in a melt shop industry, situated in north India. Percentage contribution of these factors is also estimated to develop an empirical expression between process performance and independent input variables.

Design/methodology/approach

Optimization technique for melt shop process parameters of a cast iron differential housing cover based on the Taguchi method is proposed. The focus of this paper is on the robustness of the sand casting process and the case study is based upon a leading automobile foundry industry, located in north India. Taguchi's experimental design and regression analysis techniques are used to optimize the control factors, resulting in improvement of the product quality and stability. The various confirmation tests are also carried out in the range of process parameters.

Findings

The outcome of this case study is to optimize the process parameters of the melt shop process, which leads to minimizing the casting defects. The process parameters considered are: mild steel, pig iron, cast iron, ferrosilicon, lime stone, ferromanganese, cock and ferrochrome. Best proportions of charge constituents that are contributing to casting defects in melt shop are identified in the first stage. These identified factors are analyzed using “Design of Experiments” approach in the second stage. ANOVA analysis is also performed for robust design of factor values and an appropriate empirical model is formulated.

Research limitations/implications

A lot of effort has been put into developing the appropriate empirical model for the automobile foundry industry but additional work may also be done for gating design of the casting industry.

Practical implications

The paper shows that the process parameters of any casting industry can be optimized and casting defects in the melt shop can be identified in the first stage.

Originality/value

The research findings could be applied to various manufacturing industries, especially the casting industries.

Details

International Journal of Quality & Reliability Management, vol. 30 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 20 April 2010

D.R. Prajapati and P.B. Mahapatra

The purpose of this paper is to make economic comparison of the proposed chart with the economic and economic‐statistical design of a multivariate exponentially…

515

Abstract

Purpose

The purpose of this paper is to make economic comparison of the proposed chart with the economic and economic‐statistical design of a multivariate exponentially weighted moving average (MEWMA) control chart proposed by Linderman and Love, using Lorenzen‐Vance cost model.

Design/methodology/approach

The economic design of proposed chart, using Lorenzen‐Vance cost model, is discussed in the paper. It is observed that sampling interval (h) and expected cost/hour (C) depend on various parameters of the chart, used in this model. When there is any change in any parameter of the chart, obviously both sampling interval and expected cost will be different. So it is suggested that one should use Lorenzen and Vance cost model (equation 1) to compute sampling interval and expected cost/hour for the proposed chart.

Findings

The economic design of the proposed chart has been compared with the economic and economic‐statistical design of the multivariate exponentially weighted moving average (MEWMA) control chart proposed by Linderman and Love. It is found that the proposed chart performs better than MEWMA chart proposed by Linderman and Love for sample sizes of 7, 9 and 10 for first set of parameters. The proposed chart also shows lower expected cost/hour than the MEWMA chart for sample size of 2 and 3 and for shifts of 2 and 3 for the second set of parameters.

Research limitations/implications

A lot of effort has been made to develop the proposed chart for monitoring the process mean. Although optimal sampling intervals are calculated only for two sets of parameters for shifts in the process average of 1, 2 and 3, it can be computed for any set of parameters using the Lorenzen‐Vance cost model.

Originality/value

The research findings could be applied to various manufacturing and service industries, as it is more effective than the Shewhart and EWMA charts.

Details

International Journal of Quality & Reliability Management, vol. 27 no. 4
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 19 April 2013

Sukhraj Singh and D.R. Prajapati

The purpose of this paper is to study the effect of correlation on the performance of CUSUM and EWMA charts. The performance of the CUSUM and EWMA charts is measured in…

Abstract

Purpose

The purpose of this paper is to study the effect of correlation on the performance of CUSUM and EWMA charts. The performance of the CUSUM and EWMA charts is measured in terms of average run lengths (ARLs) for the positively correlated data. The ARLs at various set of parameters of the CUSUM and EWMA charts are computed, using MATLAB. The behavior of the CUSUM and EWMA chart at the various shifts in the process mean is studied, analyzed and compared at different levels of correlation (Φ). The optimum schemes for both the charts are suggested for various levels of correlation (Φ).

Design/methodology/approach

Positively correlated observations having normal distribution are generated with the help of the MATLAB. Performance of both the charts in terms of ARLs is measured and compared at various levels of correlation (Φ). The optimal schemes of charts which give the desired in‐control ARLs are suggested for various levels of correlation (Φ).

Findings

For each level of correlation (Φ) various schemes of both the charts are suggested. Moreover those suggested schemes which give quick response to the shifts in the process mean is termed as optimal scheme. It is concluded that CUSUM schemes are preferred as compared to the EWMA schemes for quicker response. The optimal schemes of CUSUM and EWMA chart are also compared with the EWMAST chart suggested by Winkel and Zhang (2004).

Research limitations/implications

Both the schemes are optimized by assuming the autocorrelated numbers to be normally distributed. But this assumption may also be relaxed to design these schemes for autocorrelated data. Moreover sample size of four is taken while developing these schemes; various other schemes can also be developed for different sample sizes. Control charts for attribute type of data can also be developed for different level of correlation (Φ).

Practical implications

For a specific control chart, if the in‐control ARL of the process outputs of any industry is in accordance with the simulated in‐control ARL. It means the process outputs must have same level of correlation (Φ) corresponding to the simulated in‐control ARL and the suggested optimal schemes, corresponding to that level of correlation (Φ), must be adopted to avoid the false alarm rate. The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation the suggested control chart parameters can be applied. Thus false alarms generated, will be minimum for the suggested schemes at different level of correlation (Φ).

Social implications

If the optimal CUSUM schemes are employed in process/service industry, there will be a considerable amount of saving in time and money expended in search of causes behind frequent false alarms. The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society, as suggested by Taguchi.

Originality/value

The research findings could be applied to various manufacturing industries as well as service industries where the data is positively correlated and normally distributed.

1 – 10 of 229