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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.

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.

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.

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.

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.

Control charts have played an important role in monitoring the performance of operation processes, ever since their invention. Traditionally, according to Juran's idea and others, x‐bar charts are more sensitive than individual x‐charts. However, such a conclusion is valid only under certain conditions. Individual x‐charts can outperform x‐bar charts in some situations, especially in cases of minor and extreme changes of the center value. Since each chart has its own advantages and disadvantages, the idea of combining the results of these two charts is studied. The finding seems to be useful for practitioners in quality control.

The traditional Shewhart control chart, the X-bar and R/S chart, cannot give support to decide when it is not economically feasible to stop the process in order to remove special causes. Therefore, the purpose of this paper is to propose a new control chart design – a modified acceptance control chart, which provides a supportive method for decision making in economic terms, especially when the process has high capability indices.

The authors made a modeling expectation average run length (ARL), which incorporates the probability density function of the sampling distribution of C_pk, to compare and analyze the efficiency of the proposed design.

This study suggested a new procedure to calculate the control limits (CL) of the X-bar chart, which allows economic decisions about the process to be made. By introducing a permissible average variation and defining three regions for statistical CL in the traditional X-bar control chart, a new design is proposed.

A framework is presented to help practitioners in the use of the proposed control chart. Two new parameters (C_p and C_pk) in addition to m and n were introduced in the expected ARL equation. The C_pk is a random variable and its probability function is known. Therefore, by using a preliminary sample of a process under control, the authors can test whether the process is capable or not.

This study provides an analysis of the quality of service provided by a university health‐care center. Statistical process control charts are used to investigate the time spent by students at the various stages of the health‐care process. The mean, upper and lower control limits for the mean, and the variation of the waiting‐times at different stages of the health‐care process were determined. Based on the results of this study, specific recommendations are made to improve the performance of the center, and to reduce the waiting‐times at different stages of the health‐care delivery process. The implementation of these recommendations should also reflect a change in students’ perceptions about the quality of service provided by the center, as well as an increase in the efficiency of the health‐care delivery process.

Pattern recognition applied to control charts centers around the development and assessment of automated algorithms for detecting non‐random or unnatural patterns in observations collected from a production process. The work presented here marks the first examination of enhancements to an existing algorithm, of investigations into sensitivity analysis issues, of development of standard performance metrics, and of a comparative performance with the traditional Western Electric Run tests. The simulation results of the research presented here indicate that the modified algorithm performs markedly better than the original algorithm, is only slightly sensitive to the selection of the user specified algorithm parameters, and competes favorably with the Western Electric Run Tests especially when detecting repetitive patterns like cycles.

The purpose of this paper is to provide a conceptual framework which connects theory with straightforward application of statistical process control (SPC) in discovering and analyzing causes of variation to eliminate quality problems, which not only helps small and medium enterprises (SMEs) to improve their processes but also helps to attain competitive positioning.

Based on theory and methodological framework, an experimental study has been presented. Use of histograms, X (bar) and R control charts and process capability plots and cause-and-effect diagrams have been made to analyse the assignable causes. A case from an SME engaged in machining of automotive parts is investigated.

The results demonstrate the effectiveness of SPC in evaluating and eliminating quality problems. The machine capability (C_P) and the process capability (C_Pk) values are also obtained to know inherent variation in the process. If these quality tools are applied with management support and apt knowledge, attained through proper training and motivation, then in this cut-throat competitive world, SMEs can establish their market position by enhancing the quality and productivity of their products/processes.

From the study, the authors conclude that application of SPC requires thorough preparation, management commitment and human resource management through proper training, teamwork and motivation embedded with a sound measurement and control system.

The present study bridges the gap between theory and practice by developing a conceptual framework and providing a practical support by illustrating a case from an SME engaged in machining of automotive parts.

The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry.

Data were collected during production of a wheat-based dough in a food industry and the control charts were developed with these steps: to collect the master sample from different production batches; to verify, by graphical methods, the quantity and the characterization of the number of mixing probability distributions in the production batch; to adjust the theoretical model of probability distribution of each subpopulation in the production batch; to make a statistical model considering the mixture distribution of probability and assuming that the statistical parameters are unknown; to determine control limits; and to compare the mixture chart with traditional control chart.

A graph was developed for monitoring a multi-stream process composed by some parameters considered in its calculation with similar efficiency to the traditional control chart.

The control chart can be an efficient tool for customers that receive product batches continuously from a supplier and need to monitor statistically the critical quality parameters.

The purpose of this study is to investigate dimensional accuracy (Δd), surface roughness (Ra) and micro hardness (HV) of partial dentures (PD) prepared with synergic combination of fused deposition modelling (FDM) assisted chemical vapour smoothing (CVS) patterns and conventional dental casting (DC) from multi-factor optimization view point.

The master pattern for PD was prepared with acrylonitrile butadiene styrene (ABS) thermoplastic on FDM set-up (one of the low cost additive manufacturing process) followed by CVS process. The final PD as functional prototypes was casted with nickel–chromium-based (Ni-Cr) alloy by varying Ni% (Z). The other input parameters were powder to water ratio P/W (X) and pH value (Y) of water used.

The results of this study suggest that for controlling the Δd and R_a of the PD, most important factor is X, followed by Z. For hardness of PD, the most important factor is Z. But from overall optimization viewpoint, the best settings are X-100/12, Y-10 and Z-61% (in Ni-Cr alloy). Further, based upon X-bar chart (for HV), the FDM-assisted DC process used for preparation of PD is statistically controlled.

This study highlights that PD prepared with X-100/12, Y-10 and Z-61% gives overall better results from multi-factor optimization view point. Finally, X-bar chart has been plotted to understand the statistical nature of the synergic combination of FDM, CVS and DC.

The purpose of this paper is to explore the fundamental barriers/challenges, benefits, commonly used tools and techniques, organisational infrastructure and impact on organisational performance in three Indian manufacturing companies.

A multi-case study analysis using the exploratory case study research was adopted by the authors to obtain a deeper insight into the Six Sigma implementation within three distinctive manufacturing organisations in India. Interviews were conducted with relevant staff (Six Sigma Deployment Champions, Six Sigma Master Black Belts and Six Sigma Black Belts) in all three companies.

Some of the barriers in implementing and sustaining Six Sigma identified from the case studies include: lack of accuracy of data generated from the processes, lack of understanding of the benefits of Six Sigma in the early stages of its adoption, high-attrition rate of Six Sigma Black Belts and so on. The benefits of Six Sigma included improvement of process yield, reduction of rework and rejection, reduction of raw material inventory, improved on-time delivery, on-time availability of material for production and so on. Supplier-input-process-output-customer, cause and effect diagram, process mapping, hypothesis tests (two sample test, F-test, etc.), control charts (X-bar-R chart, individual chart, etc.), simple graphical tools such as histograms, box plots and dot plots were the most commonly used tools of Six Sigma across the companies that participated for this research. All three companies have reported that Six Sigma had a positive impact on organisational performance and moreover the study also revealed that Six Sigma had positive impact on customer satisfaction, return-on-investment, productivity and product quality.

The study was carried out in three Indian companies and therefore the findings cannot be generalised. The authors are extending the study to three more companies and the findings will be reported in the forthcoming months.

The findings of the study provide a good foundation to understand the fundamental barriers, benefits, commonly used tools and whether Six Sigma is having any impact on business performance in the Indian context. Very few empirical studies have been carried out on Six Sigma implementation in the Indian manufacturing companies and this research sets an agenda for a number of studies to follow on in the forthcoming years.

In authors’ opinion, this is possibly one of the first multi-case empirical studies on Six Sigma implementation in the Indian manufacturing companies. The results of the study can be used to benchmark with similar studies in other countries to understand the good and bad management practices of Six Sigma implementation.

Quality management (QM) cannot be assured unless some objective assessments are undertaken. A number of tools and techniques are available to conduct such analysis. Although some of them are product or service specific, however, a few basic tools and techniques are commonly used in manufacturing firms. This study focuses on the state of application of QM tools and techniques in small and medium industries (SMIs). The findings reveal that by‐and‐large, lack of methodical analysis is a major weakness of SMIs. Still some rule‐of‐thumb and subjective observations are dominating over objective evaluation in the process of quality control decisions. A few case studies which have been conducted, and one that has been briefly reported here, also support this conclusion. The methodology of the study has three folds: literature review, survey in SMIs and case studies.