L. Allison Jones-Farmer

Effects of parameter estimation on control chart properties : A literature review

Control charts are powerful tools used to monitor the quality of processes. In practice, control chart limits are often calculated using parameter estimates from an in-control Phase I reference sample. In Phase II of the monitoring scheme, statistics based on new samples are compared with the estimated control limits to monitor for departures from the in-control state. Many studies that evaluate control chart performance in Phase II rely on the assumption that the in-control parameters are known. Although the additional variability introduced into the monitoring scheme through parameter estimation is known to affect the chart performance, many studies do not consider the effect of estimation on the performance of the chart. This paper contains a review of the literature that explicitly considers the effect of parameter estimation on control chart properties. Some recommendations are made and future research ideas in this area are provided.

Adoption of cloud computing technologies in supply chains

Purpose – The purpose of this paper is to employ organizational information processing theory to assess how a firms information processing requirements and capabilities combine to affect the intention to adopt cloud computing as an enabler of electronic supply chain management systems. Specifically, the paper examines the extent to which task uncertainty, environmental uncertainty, and inter‐organizational uncertainty affect intention to adopt cloud computing technology and how information processing capability may moderate these relationships.Design/methodology/approach – The paper uses a multiple method approach, thus examining the hypothesized model with both quantitative and qualitative methods. To begin, the paper incorporates a Delphi study as a way in which to choose a practically relevant characterization of the moderating variable, information processing capability. The authors then use a survey method and hierarchical linear regression to quantitatively test their hypotheses. Finally, the autho...

An Overview of Phase I Analysis for Process Improvement and Monitoring

We provide an overview and perspective on the Phase I collection and analysis of data for use in process improvement and control charting. In Phase I, the focus is on understanding the process variability, assessing the stability of the process, investigating process-improvement ideas, selecting an appropriate in-control model, and providing estimates of the in-control model parameters. In our article, we review and synthesize many of the important developments that pertain to the analysis of process data in Phase I. We give our view of the major issues and developments in Phase I analysis. We identify the current best practices and some opportunities for future research in this area.

Properties of the T2 Control Chart When Parameters Are Estimated

Moments of the run length distribution are often used to design and study the performance of quality control charts. In this article the run length distribution of the T2 chart for monitoring a multivariate process mean is analyzed. It is assumed that the in-control process observations are iid random samples from a multivariate normal distribution with unknown mean vector and covariance matrix. It is shown that the in-control run length distribution of the chart does not depend on the unknown process parameters. Furthermore, it is shown that the out-of-control run length distribution of the chart depends only on the statistical distance between the in-control and out-of-control mean vectors. It follows that a performance analysis can be given without knowledge of the in-control values of the parameters or their estimates. The performance of charts constructed using traditional F-distribution–based control limits is studied. Recommendations are given for sample size requirements necessary to achieve desired performance. Corrected control limits are given for designing charts with estimated parameters when large sample sizes are not available.

Distribution-free Phase I Control Charts for Subgroup Location

Much of the work in statistical quality control is dependent on the proper completion of a Phase I study. Many Phase I control charts are based on an implicit assumption of normally distributed process observations. In the beginning stages of process control, little information is available about the process and the normality assumption may not be reasonable. Existing robust and distribution-free control charts are concerned with the establishment of Phase II control limits that are robust to nonnormality or outliers from the Phase I sample. Our literature review revealed no purely distribution-free Phase I control-chart methods. We propose a distribution-free method for defining the in-control state of a process and identifying an in-control reference sample. The resultant reference sample can be used to estimate the process parameters for the Phase II procedure of choice. The proposed rank-based method is compared with the traditional X chart using Monte Carlo simulation. The rank-based method compares favorably to the X chart when the process is normally distributed and performs better than the X chart in many situations when the process distribution is skewed or heavy tailed.

Properties of Multivariate Control Chart with Estimated Parameters

Abstract The Hotellings T 2, multivariate exponentially weighted moving average (MEWMA), and several multivariate cumulative sum (MCUSUM) charts are examined in this paper. Two descriptions are given of each chart with estimated parameters for monitoring the mean of a vector of quality measurements. For each chart, one description explains how the chart can be applied with estimated parameters in practice, and the other description is useful for analyzing the run length performance of the chart. It is shown that, if the covariance matrix is in control, the run length distribution of most of these charts depends only on the distributional parameters through the size of the process shift in terms of statistical distance. Simulation is used to provide performance analyses and comparisons of these charts. An example is given to illustrate the MCUSUM and MEWMA charts when parameters are estimated.

Applying Control Chart Methods to Enhance Data Quality

As the volume and variety of available data continue to proliferate, organizations increasingly turn to analytics in order to enhance business decision-making and ultimately, performance. However, the decisions made as a result of the analytics process are only as good as the data on which they are based. In this article, we examine the data quality problem and propose the use of control charting methods as viable tools for data quality monitoring and improvement. We motivate our discussion using an integrated case study example of a real aircraft maintenance database. We include discussions of the measures of multiple data quality dimensions in this online process. We highlight the lack of appropriate statistical methods for the analysis of this type of problem and suggest opportunities for research in control chart methods within the data quality environment. This article has supplementary material online.

A Proposed Framework for Educational Innovation Dissemination.

Although the need for new educational technologies is increasing, the process for disseminating these innovations remains a challenge. A literature review shows that few studies have thoroughly investigated this area. Furthermore, there is no comprehensive framework or coordinated research agenda that may be used to guide such investigation. This study draws on diffusion of innovation, technology acceptance, and related literatures as a basis to examine the process by which educational innovations are disseminated. In this article, we develop a framework for educational innovation dissemination and illustrate the process described by the framework using online education as an example. The stages of the dissemination process are discussed and it is shown how characteristics of the innovation, adopter, and environment may affect how well an innovation progresses through these stages. This leads to the development of a series of propositions that can be used as the basis for future investigation.

Performance expectancy and use of enterprise architecture: training as an intervention

Purpose – Enterprise architecture (EA) aligns information systems with business processes to enable firms to reach their strategic objectives and, when effectively employed by organizations, can lead to enhanced levels of performance. However, while many firms may adopt EA, it is often not used extensively. The purpose of this paper is to examine how performance expectancy (PE) and training affect the degree to which organizations use EA. Design/methodology/approach – The paper employed a survey method to gather data from IT professionals, senior managers, and consultants who work within organizations that have adopted EA. Covariance-based structural equation modeling was used to analyze the research model and test the hypotheses. Findings – The paper found PE to be a significant predictor of EA use. In addition, training is also shown to enhance use of EA while also playing a mediating role within the relationship between PE and use of EA. Research limitations/implications – The study is limited by the f...

A Distribution-Free Multivariate Phase I Location Control Chart for Subgrouped Data from Elliptical Distributions

In quality control, a proper Phase I analysis is essential to the success of Phase II monitoring. A literature review reveals no distribution-free Phase I multivariate techniques in existence. This research develops a Phase I location control chart for multivariate elliptical processes. The resulting in-control reference sample can then be used to estimate the parameters for Phase II monitoring. Using Monte Carlo simulation, the proposed method is compared with the Hotellings T2 Phase I chart. Although Hotellings T2 chart is preferred when the data are multivariate normal, the proposed method is shown to perform significantly better under nonnormality. This article has supplementary material online.