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This paper seeks to propose a univariate robust control chart for location and the necessary table of factors for computing the control limits and the central line as an alternative to the Shewhart X¯ control chart.

The proposed method is based on two robust estimators, namely, the sample median, MD, to estimate the process mean, μ, and the median absolute deviation from the sample median, MAD, to estimate the process standard deviation, σ. A numerical example was given and a simulation study was conducted in order to illustrate the performance of the proposed method and compare it with that of the traditional Shewhart X¯ control chart.

The proposed robust X¯_MDMAD control chart gives better performance than the traditional Shewhart X¯ control chart if the underlying distribution of chance causes is non‐normal. It has good properties for heavy‐tailed distribution functions and moderate sample sizes and it compares favorably with the traditional Shewhart X¯ control chart.

The most common statistical process control (SPC) tool is the traditional Shewhart X¯ control chart. The chart is used to monitor the process mean based on the assumption that the underlying distribution of the quality characteristic is normal and there is no major contamination due to outliers. The sample mean, X¯, and the sample standard deviation, S, are the most efficient location and scale estimators for the normal distribution often used to construct the X¯ control chart, but the sample mean, X¯, and the sample standard deviation, S, might not be the best choices when one or both assumptions are not met. Therefore, the need for alternatives to the X¯ control chart comes into play. The literature shows that the sample median, MD, and the median absolute deviation from the sample median, MAD, are indeed more resistant to departures from normality and the presence of outliers.

The purpose of this paper is to explore the scope of robust dispersion control charts in a distribution-free environment, which is a specific case of non-normal control charts. These control charts are skewness-based structures designed to monitor skewed-type processes whilst equally performing under symmetric processes. Moreover, the choice of a suitable control chart for a particular non-normal situation is also suggested.

The probability control limits approach is considered as an alternative way to determine the skewness-based structure of dispersion control charts. The proposals of five robust and two conventional Shewhart-type dispersion control charts are suggested as efficient competitors of skewness correction (SC) dispersion control charts. The evaluation of robust proposals and competing dispersion control charts is done through false alarm rate (FAR) and probability to signal (PTS) measures.

The proposed dispersion control charts are found robust and efficient alternatives of SC dispersion control charts in both normal and non-normal distributions. The FARs and PTS properties of proposed control charts are impressive in all studied cases, and a real-data example also verifies the dominance of proposed control charts.

Conventional dispersion control charts quickly lose their efficiency as underlying process distribution deviates from normality; however, robust control charts emerge as most suitable candidates in such situations. This paper proposes the idea of robust dispersion control charts under a distribution-free structure for the skewed-type process, which is not yet explored.

The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T² control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II.

In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T² control chart.

The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles.

In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II.

This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

This study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.

The PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.

The variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.

A new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

The purpose of this paper is to find the configuration of the number (m) and size (n) of the sample in Phase I that would make it possible to detect the out-of-control (OOC) state of the process with the smallest number of samples and ensure a capability index (C_pk) that would meet the customer’s requirements.

The suggested approach addresses this problem using simulation techniques and design of experiments (DOE). The simulation techniques made it possible to reproduce the normal operating conditions of the process. The DOE was used to construct a predictive model for control chart performance and thus to determine combinations of m and n in Phase I that would meet the capability objectives of the process. A numerical example and a simulation study were conducted to illustrate the proposed method.

Using simulation techniques and DOE, the authors can find the number (m) and size (n) of the sample in Phase I that would make it possible to detect the OOC state of the process with the smallest number of samples and ensure a C_pk that would meet the customer’s requirements.

In the real situations of many companies, choosing the numbers and sizes of samples (m and n) in Phases I and II is a crucial decision in relation to implementing a control chart. The paper shows that the simulation method and use of linear regression are effective alternatives because they are better known and more easily applied in industrial settings. Therefore, the need for alternatives to the X control chart comes into play.

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.

Explores the relationships between statistical process control (SPC) and forecasting procedures. While both procedures are often applied and used in different contexts, a careful analysis shows that they go through the same stages that culminate in process or forecast monitoring. This apparent similarity of SPC and forecasting enables a general framework to be established for model‐based SPC. Discusses some forecasting procedures applicable to SPC and underlines the importance of SPC concepts in forecasting.

As an alternative to the standard p and np charts along with their various modifications, beta control charts are used in the literature for monitoring proportion data. These charts in general use average of proportions to set up the control limits assuming in-control parameters known. The purpose of the paper is to propose a control chart for detecting shift(s) in the percentiles of a beta distributed process monitoring scheme when in-control parameters are unknown. Such situations arise when specific percentile of proportion of conforming or non-conforming units is the quality parameter of interest.

Parametric bootstrap method is used to develop the control chart for monitoring percentiles of a beta distributed process when in-control parameters are unknown. Extensive Monte Carlo simulations are conducted for various combinations of percentiles, false-alarm rates and sample sizes to evaluate the in-control performance of the proposed bootstrap control charts in terms of average run lengths (ARL). The out-of-control behavior and performance of the proposed bootstrap percentile chart is thoroughly investigated for several choices of shifts in the parameters of beta distribution. The proposed chart is finally applied to two skewed data sets for illustration.

The simulated values of in-control ARL are found to be closer to the theoretical results implying that the proposed chart for percentiles performs well with both positively and negatively skewed data. Also, the out-of-control ARL values for the percentiles decrease sharply with both downward and upward small, medium and large shifts in the parameters. The phenomenon indicates that the chart is effective in detecting shifts in the parameters. However, the speed of detection of shifts varies depending on the type of shift, the parameters and the percentile being considered. The proposed chart is found to be effective in comparison to the Shewhart-type chart and bootstrap-based unit gamma chart.

It is worthwhile to mention that the beta control charts proposed in the literature use average of proportion to set up the control limits. However, in practice, specific percentile of proportion of conforming or non-conforming items should be more useful as the quality parameter of interest than average. To the best of our knowledge, no research addresses beta control chart for percentiles of proportion in the literature. Moreover, the proposed control chart assumes in-control parameters to be unknown, and hence captures additional variability introduced into the monitoring scheme through parameter estimation. In this sense, the proposed chart is original and unique.

The aim of the article is to ascertain the challenges, lessons learned and managerial implications in the deployment of Lean Six Sigma (LSS) competitiveness to micro, small and medium Enterprises (MSME) in India and to establish doctrines to strengthen the initiatives of the government.

The research adopts the Action Research methodology to develop a case study, which is carried out in the printing industry in a Tier III city using the LSS DMAIC (Define-Measure-Analyze-Improve-Control) approach. It utilizes LSS tools to deploy the strategy and to unearth the challenges and success factors in improving the printing process of a specific batch of a product.

The root cause for the critical to quality (CTQ) characteristic, turn-around-time (TAT) is determined and the solutions are deployed through the scientifically proven data-based approach. As a result of this study, the TAT reduced from an average of 1541.2–1303.36 min, which in turn, improved the sigma level from 0.55 to 2.96, a noteworthy triumph for this MSME. The company realizes an annual savings of USD 12,000 per year due to the success of this project. Top Management Leadership, Data-Based Validation, Technical Know-how and Industrial Engineering Knowledge Base are identified as critical success factors (CSFs), while profitability and on-time delivery are the key performance indicators (KPIs) for the MSME. Eventually, the lessons learned and implications indicate that LSS competitiveness can be treated as quality management standards (QMS) and quality tools and techniques (QTT) to ensure competitive advantage, sustainable green practices and growth.

Even though the findings and recommendations of this research are based on a single case study, it is worth noting that the case study is executed in a Tier III city along with novice users of LSS tools and techniques. This indicates the applicability of LSS in MSME and thus, the modality adopted can be further refined to suit the socio-cultural aspects of India.

This article illustrates the deployment of LSS from the perspective of novice users, to assist MSME and policymakers to reinforce competitiveness through LSS. Moreover, the government can initiate a scheme in line with LSS competitiveness to complement the existing schemes based on the findings of the case study.

The purpose of this paper is to understand the post-COVID-19 fluctuations in the building construction demand from various angles at the national, regional, and sectoral levels. Despite the significant impact of COVID-19 on the building construction industry, a detailed quantitative analysis of the COVID-19 impact on the building construction demand is still lacking. The current study aims to (1) establish a statistical approach to quantify the COVID-19 impact on the building construction demand; (2) investigate the post-COVID-19 fluctuations in the construction demand of different building services, regional markets, and building sectors using the historical time series of the architecture billings index (ABI); and (3) identify vulnerable market and sector and discuss the post-COVID-19 recovery strategies.

The research methodology follows four steps: (1) collecting national, regional, and sectoral ABIs; (2) creating seasonal autoregressive integrated moving average models; (3) illustrating cumulative sum control charts to identify significant ABI deviations; and (4) quantifying the post-COVID-19 ABI fluctuations.

The results show that all the ABIs experienced a statistically significant decrease after COVID-19. The project inquiries index reduced more but recovered faster than billings and design contracts indices. The midwest billings index decreased the most among the regional ABIs and the commercial/industrial billing index dropped the most among the sectoral ABIs.

This study is unique in the way that it utilized the ABI data and the approach using SARIMA models and CUSUM control charts to assess the post-COVID-19 building construction demand represented by ABI fluctuations.