Transition probabilities and ARL performance of Six Sigma zone control charts

In this article, Six Sigma zone control charts (SSZCCs) are proposed for world class organizations. The transition probabilities are obtained using the Markov chain approach. The Average Run Length (ARL) values are then presented. The ARL performance of the proposed SSZCCs and the standard Six Sigma control chart (SSCC) without zones or run rules is studied. The ARL performance of these charts is then compared with those of the other standard zone control charts (ZCCs), the modified ZCC and the traditional Shewhart control chart (SCC) with common run rules. As expected, it is shown that the proposed SSZCC outperforms the standard SSCC without zones or run rules for process shifts of any magnitude. When compared to the other standard ZCCs and the Shewhart chart with common run rules, it is observed that the proposed SSZCCs have much higher false alarm rates for smaller shifts and hence they prevent unwanted process disturbances. The application of the proposed SSZCC is illustrated using a real time example.     

Assessing dust exposure in an integrated iron and steel manufacturing plant in South India

A study to monitor and estimate respirable particulate matter (RPM), toxic trace metal concentrations in the work environment was carried out in different sections of an integrated steel manufacturing industry. The average RPM concentration observed varied according to the section blast furnace was 2.41 mg/m;{3}; energy optimization furnace, 1.87 mg/m;{3}; sintering plant, 0.98 mg/m;{3}; continuous casting machine, 1.93 mg/m;{3}. The average trace metal concentration estimated from the RPM samples like iron, manganese, lead and chromium did not exceed ACGIH prescribed levels.     

Inference based on conditional speclfication

The first bibliography in the area of inference based on conditional specification was published in 1977, A second bibliography is compiled, and a combined subject index is given.     

Exploring Contrasting Formulations of Expectancy Theory*

The explanatory potential of four forms of expectancy theory with additive and multiplicative expectancy terms and linear and nonlinear valence functions were contrasted. A behavioral decision-making theory approach was used when 101 subjects were asked to make 128 hypothetical job-choice decisions. More than 25,800 decisions under a within-subjects framework were analyzed. Results indicate that the majority (83 percent) of subjects employed additive or multiplicative expectancy models with linear valence functions. However, the predictive efficacy of the expectancy theory model was improved for 17 percent of the subjects when nonlinear valence terms were introduced. The findings imply that different functional forms of expectancy theory may be needed to model individuals' decision-making processes.     

Estimation of Variance Function in Heteroscedastic Regression Models by Generalized Coiflets

A wavelet approach is presented to estimate the variance function in heteroscedastic nonparametric regression model. The initial variance estimates are obtained as squared weighted sums of neighboring observations. The initial estimator of a smooth variance function is improved by means of wavelet smoothers under the situation that the samples at the dyadic points are not available. Since the traditional wavelet system for the variance function estimation is not appropriate in this situation, we demonstrate that the choice of the wavelet system is significant to have better performance. This is accomplished by choosing a suitable wavelet system known as the generalized coiflets. We conduct extensive simulations to evaluate finite sample performance of our method. We also illustrate our method using a real dataset.     

Environmental Factors,Managerial Processes,and Information Technology Investment Strategies

Although significant research attention has been directed at understanding the value of information technology (IT) investments for firms, very little attention has been paid to understand the IT investment behavior of firms. This article seeks to fill this void. We introduce the concept of IT investment strategy, defined by dimensions of intensity and proactiveness, to characterize the IT investment behavior of firms. Synthesizing the environmental deterministic and strategic choice perspectives of a firm's strategic decision making, we examine the effects of environmental factors, managerial processes, and the interplay between them on IT investment strategy. Specifically, we examine the impact of environmental factors such as industry clockspeed and information intensity on IT investment strategy. We also incorporate the strategic choice perspective to argue that managerial processes such as frequent chief executive officer—chief information officer communications and collective information systems planning play a critical role in shaping the firm's IT investment strategy. The empirical results show that the environmental variables are related to IT investment strategy. Besides, managerial processes serve as a means to understand the environment and thus moderate the relationships between the environmental variables and the various facets of IT investment strategy. The conceptualization of IT investment strategy and the focus on both environmental determinism and strategic choice should enrich our understanding of firms’ IT investing behavior.     

Self-starting X-bar control chart based on Six Sigma quality and sometimes pooling procedure

Self-starting control charts have been proposed in the literature to allow process monitoring when only a small amount of relevant data is available. In fact, self-starting charts are useful in monitoring a process quickly, without having to collect a sizable Phase I sample for estimating the in-control process parameters. In this paper, a new self-starting control charting procedure is proposed in which first an effective initial sample is chosen from the perspective of Six Sigma quality, then the successive sample means are either pooled or not pooled (sometimes pooling procedure) for computing next Q-statistics depending upon its signal. It is observed that the sample statistics obtained so from this in-control Phase I situation can serve as more efficient estimators of unknown parameters for Phase II monitoring. An example is considered to illustrate the construction of the proposed chart and to compare its performance with the existing ones.     

Variance Estimation in Heteroscedastic Models by Undecimated Haar Transform

We propose a method in order to maximize the accuracy in the estimation of piecewise constant and piecewise smooth variance functions in a nonparametric heteroscedastic fixed design regression model. The difference-based initial estimates are obtained from the given observations. Then an estimator is constructed by using iterative regularization method with the analysis-prior undecimated three-level Haar transform as regularizer term. We notice that this method shows better results in the mean square sense over an existing adaptive estimation procedure considering all the standard test functions used in addition to the functions that we target. Some simulations and comparisons with other methods are conducted to assess the performance of the proposed method.     

A review of preliminary test-based statistical methods for the benefit of Six Sigma quality practitioners

Ever since Professor Bancroft developed inference procedures using preliminary tests there has been a lot of research in this area by various authors across the world. This could be evidenced from two papers that widely reviewed the publications on preliminary test-based statistical methods. The use of preliminary tests in solving doubts arising over the model parameters has gained momentum as it has proven to be effective and powerful over to that of classical methods. Unfortunately, there has been a downward trend in research related to preliminary tests as it could be seen from only few recent publications. Obviously, the benefits of preliminary test-based statistical methods did not reach Six Sigma practitioners as the concept of Six Sigma just took off and it was in a premature state. In this paper, efforts have been made to present a review of the publications on the preliminary test-based statistical methods. Though studies on preliminary test-based methods have been done in various areas of statistics such as theory of estimation, hypothesis testing, analysis of variance, regression analysis, reliability, to mention a few, only few important methods are presented here for the benefit of readers, particularly Six Sigma quality practitioners, to understand the concept. In this regard, the define, measure, analyze, improve and control methodology of six sigma is presented with a link of analyze phase to preliminary test-based statistical methods. Examples are also given to illustrate the procedures.