Economic Control of Quality of Manufactured Product.

Disturbance detection and isolation by dynamic principal component analysis

The positive core construct of psychological capital (or simply PsyCap), consisting of the psychological resources of hope, efficacy, resilience, and optimism, has recently been demonstrated to be open to human resource development (HRD) and performance management. The research stream on PsyCap has now grown to the point that a quantitative summary analysis of its impact on employee attitudes, behaviors, and especially performance is needed. The present meta-analysis included 51 independent samples (representing a total of N � 12,567 employees) that met the inclusion criteria. The results indicated the expected significant positive relationships between PsyCap and desirable employee attitudes (job satisfaction, organizational commitment, psychological well-being), desirable employee behaviors (citizenship), and multiple measures of performance (self, supervisor evaluations, and objective). There was also a significant negative relationship between PsyCap and undesirable employee attitudes (cynicism, turnover intentions, job stress, and anxiety) and undesirable employee behaviors (deviance). A sub-analysis found no major differences between the types of performance measures used (i.e., between self, subjective, and objective). Finally, the analysis of moderators revealed the relationship between PsyCap and employee outcomes were strongest in studies conducted in the United States and in the service sector. These results provide a strong evidence-based recommendation for the use of PsyCap in HRD and performance programs. Theoretical contributions, future research directions, and practical guidelines for HRD conclude the article.

/pdf/meta-analysis-of-the-impact-of-positive-psychological-2o2sb72243.pdf

Meta-Analysis of the Impact of Positive Psychological Capital on Employee Attitudes, Behaviors, and Performance.

Statistical Process Control of Multivariate Processes

Multiscale principal-component analysis (MSPCA) combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelate autocorrelated measurements. MSPCA computes the PCA of wavelet coefficients at each scale and then combines the results at relevant scales. Due to its multiscale nature, MSPCA is appropriate for the modeling of data containing contributions from events whose behavior changes over time and frequency. Process monitoring by MSPCA involves combining only those scales where significant events are detected, and is equivalent to adaptively filtering the scores and residuals, and adjusting the detection limits for easiest detection of deterministic changes in the measurements. Approximate decorrelation of wavelet coefficients also makes MSPCA effective for monitoring autocorrelated measurements without matrix augmentation or time-series modeling. In addition to improving the ability to detect deterministic changes, monitoring by MSPCA also simultaneously extracts those features that represent abnormal operation. The superior performance of MSPCA for process monitoring is illustrated by several examples.

Multiscale PCA with application to multivariate statistical process monitoring

A multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure. A comparison shows that the average run length (ARL) performance of this chart is similar to that of multivariate cumulative sum (CUSUM) control charts in detecting a shift in the mean vector of a multivariate normal distribution. As with the Hotelling's χ2 and multivariate CUSUM charts, the ARL performance of the multivariate EWMA chart depends on the underlying mean vector and covariance matrix only through the value of the noncentrality parameter. Worst-case scenarios show that Hotelling's χ2 charts should always be used in conjunction with multivariate CUSUM and EWMA charts to avoid potential inertia problems. Examples are given to illustrate the use of the proposed procedure.

A multivariate exponentially weighted moving average control chart

Control chart limits are often calculated using parameter estimates from an in-control Phase I reference sample. In Phase II monitoring, statistics based on new samples are compared with the estimated control limits to monitor for departures from the in..

Effects of Parameter Estimation on Control Chart Properties; A Literature Review

This article gives a simple and efficient method, using Markov chains, to obtain the exact run-length properties of Shewhart control charts with supplementary runs rules. Average run-length comparisons are made among the Shewhart chart with supplementary runs rules, the basic Shewhart chart, and the cumulative sum (CUSUM) chart.

Exact results for shewhart control charts with supplementary runs rules

The exponentially weighted moving average (EWMA) control chart is typically designed assuming that standards are given for the process parameters. In practice, the parameters are rarely known, and control charts are constructed using estimates in place of the parameters. This practice can affect the control chart's run-length performance in both in- and out-of-control situations. Specifically, estimation can lead to substantially more frequent false alarms and yet reduce the sensitivity of the chart to detecting process changes. In this article, the run-length distribution of the EWMA chart with estimated parameters is derived. The effect of estimation on the performance of the chart is discussed in a variety of practical scenarios.

The Performance of Exponentially Weighted Moving Average Charts With Estimated Parameters

Two methods that are often used to evaluate the run length distribution of quality control charts are the Markov chain and integral equation approaches. Both methods have been used to evaluate the cumulative sum (CUSUM) charts and the exponentially weighted moving average (EWMA) control charts. The Markov chain approach involves "discretiz-ing" the possible values which can be plotted. Using properties of finite Markov chains, expressions for the distribution of the run length, and for the average run length (ARL), can be obtained. For the CUSUM and EWMA charts there exist integral equations whose solution gives the ARL. Approximate methods can then be used to solve the integral equation. In this article we show that if the product midpoint rule is used to approximate the integral in the integral equation, then both approaches yield the same approximations for the ARL. In addition we show that the recursive expressions for the probability functions are the same for the two approaches. These results establ...

Charles W. Champ

Papers

A multivariate exponentially weighted moving average control chart

Effects of Parameter Estimation on Control Chart Properties; A Literature Review

Exact results for shewhart control charts with supplementary runs rules

The Performance of Exponentially Weighted Moving Average Charts With Estimated Parameters

A a comparison of the markov chain and the integral equation approaches for evaluating the run length distribution of quality control charts