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

In this paper, a new X¯ 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 X¯ 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).

The proposed X¯ chart with “strategy ASM” shows lower average run length (ARL) values than ARLs of variable parameter (VP) X¯ 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 X¯ 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 X¯ charts.

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 X¯ 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.

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.

Control charts based on geometric distribution have shown to be useful when this is a better approximation of the underlying distribution than the Poisson distribution. The traditional c‐chart, if used, will cause too many false alarms. It is noted that for geometric distribution, the control limits are based on k times standard deviation which has been used previously, will cause a frequent false alarm, and cannot derive any reasonable lower control limits. Studies the use of probability limits to resolve these problems. Also discusses the use of geometric distribution for process control of high‐yield processes.

This paper proposes a simplified procedure to approximate optimal values for the sample size, control limits, and sampling interval of a control chart based quality control station. The procedure considers the specifications in evaluating the control limits, permits asymmetry in these limits and accounts for the cost structure of the control process. The proposed procedure is compared with the optimal approach and with the current approach used by the company from which production information was obtained. This information was used to generate simulated data on which the comparisons are based.

A variety of quantitative specifications, usually in terms of numerical limits, have been developed in industry for the description, prediction and control of product quality. As the theoretical foundations of these specifications are often beyond the working knowledge of many manufacturing engineers and managers, this article gives a non‐mathematical account of some of the limits commonly encountered in discussions related to product design, manufacture and inspection; emphasis is placed on the distinctions in their intended purposes and methods of application.

The purpose of this paper is to implement statistical process control (SPC) in service quality using three-level SERVQUAL, quality function deployment (QFD) and internal measure.

The SERVQUAL questionnaire is developed according to internal services of train. Also, it is verified by reliability scale and factor analysis. QFD method is employed for translating SERVQUAL dimensions’ importance weights which are derived from Analytic Hierarchy Process into internal measures. Furthermore, the limits of the Zone of Tolerance are used to determine service quality specification limits based on normal distribution characteristics. Control charts and process capability indices are used to control service processes.

SPC is used for service quality through a structured framework. Also, an adapted SERVQUAL questionnaire is created for measuring quality of train’s internal services. In the case study, it is shown that reliability is the most important dimension in internal services of train for the passengers. Also, the service process is not capable to perform in acceptable level.

The proposed algorithm is practically applied to control the quality of a train’s services. Internal measure is improved for continuous data collection and process monitoring. Also, it provides an opportunity to apply SPC on intangible attributes of the services. In the other word, SPC is used to control the qualitative specifications of the service processes which have been measured by SERVQUAL.

Since SPC is usually used for manufacturing processes, this paper develops a model to use SPC in services in presence of qualitative criteria. To reach this goal, this model combines SERVQUAL, QFD, normal probability distribution, control charts, and process capability. In addition, it is a novel research on internal services of train with regard to service quality evaluation and process control.

Presents an approach to determine the optimum control limits of the x‐bar chart for skewed process distributions. The approach takes both the control limits of the x‐bar chart and the specification limits of x into consideration, and relates the out‐of‐control status directly with the nonconforming products. The proposed approach may be applied to industries to reduce the average number of scrap products, without increasing the type I error in statistical process control (SPC).

The purpose of this paper is to demonstrate the use of artificial intelligence methods in quality control and improvement. The paper introduces a systematic approach for the design of fuzzy control charts of tip shear carpets.

There are certain steps for designing fuzzy control charts. All input, state and output variables of the carpet plant and partition of the universe of discourse were first determined. The interval spanned by each variable and the number of fuzzy subsets each assigned with a linguistic label were identified. Then, the adaptive capability of neural network was used to determine the membership functions for each fuzzy subset. The fuzzy relationship functions between the inputs and outputs were assigned to form the fuzzy rule base (controller) in order to normalize the variables and certain intervals. Fuzzification of input parameters and max‐min composition of rules for inferring crisp outputs was the next step. The aggregation of fuzzified outputs and defuzzification of the outputs were the last step of this study, which helped to produce crisp outputs for latex weight.

Fuzzy linguistic terms were employed for overall quality assessment and rating of the end product. The outcomes of neuro‐fuzzy system were good supplements to other statistical process control tools.

Lack of qualified domain experts, knowledge acquisition of process parameters and time limitation for training of neuro‐fuzzy model were primary limitations.

The approach is more flexible and meaningful to identify the quality distribution of a product. The qualitative aspect of human reasoning for decision making was employed in this approach.

The paper is original and the first such work for local industry.

Studies the necessity of controlling the variation of the skewness of the process distribution in order to reduce the product scrap. Proposes a γ control chart for detecting the skewness shift, also implements a simulation procedure to decide the control limits of the γ chart.

A product or service usually has multiple measurable characteristics, and its performance on different measures may vary and may change over time. Multi-criterion and multi-period performance benchmarking presents a challenge for management to determine performance gaps among comparable products or services. The purpose of this paper is to propose a new performance benchmarking method to address this challenge.

The author develop this method by formulating two benchmarking functions: a differentiation function based on Shewhart average and standard deviation charts to distinguish the performance of products or services on a single measure, and a categorization function to classify each product’s or service’s overall performance across all measures. By systematically removing the lowest-performing products or services from comparison, the author use these functions iteratively to detect performance gaps.

Using this method, the author find performance gaps in each of three benchmarking applications of airports, hotels, and minivans, although a number of performance gaps are not obvious from the raw data.

This benchmarking study focuses on the quantifiable outcome performance of products and services.

This benchmarking method is generic and applicable to most products and services. It is robust not only for discovering performance gaps, but also for providing useful insights for managers to prioritize improvement efforts on individual performance measures.

The novelty of this benchmarking method lies in that it can not only find the “best overall” products or services for all performance measures, but can also pinpoint the “best-in-class” products or services as well as performance gaps for each performance measure. In addition, this paper presents several original ideas for performance benchmarking, including: using the control limits of Shewhart control charts to categorize performance gaps, systematically removing the lowest-performing products or services from comparison for the purpose of detecting hidden performance gaps, and using symbolic expressions to integrate benchmarking results from all measures and to show all performance gaps intuitively.

Usually, statistical process control uses control charts for monitoring the evolution of a manufacturing process. Traditionally, control limits are computed without considering another aspect of statistical process control: the process capability (PC). This is essential information because it indicates the propensity of a manufacturing process of respecting the specifications (nominal and tolerances). The main problem when separating the PC from control charts is that the latter are not suitable for monitoring the manufacturing process evolution with respect to the specifications. Thus proposes a new approach for computing control limits: simultaneously accounts for the specifications and the PC, and leads to control charts useful not only for identifying a possible non‐random behaviour of the monitored process but also an erratic behaviour with respect to the specification limits. The main results are: (1) the control limits are no longer established using an interval of 3 standard deviation around a centre line (•); (2) the control limits are no longer restricted to be symmetric; and (3) there is no possibility for the control limits to stand outside the specification limits (which might be the case with the traditional approach).