bluuebunny/crossref_metadata_2025 huggingface dataset:
Project Management, Stage‐Gate Approach to
Authors: Trabert Thomas F.
Published: September 2014

The project process uses a predetermined set of steps, or stages, in a sequence to plan and implement a variety of projects. Each step has a stage‐gate, which is the criterion or question that must be appropriately addressed prior to proceeding to the next stage in the project. Each project an organization implements must be considered an investment of resources to produce a benefit or add value to the organization. The stage‐gate approach is used to ensure that the project has the necessary support and approved from the senior leadership prior to proceeding to the next stage and to continue investing resources. The project process model is composed of four main phases and nine stages within these phases. The planning phase includes Stage 1, Inception and Stage 2, Feasibility. The solution design phase contains Stage 3, Concept design, Stage 4, Scheme design, and Stage 5, Detailed design. The implementation phase includes Stage 6, Performance and Stage 7, Commissioning and Handover. And the final operational startup phase consists of Stage 8, Process integration, and Stage 9, Closeout and benefits realization. By applying the same disciplined approach to each project, your organization establishes ways of working that sets the organization up to reliably produce projects and have the greatest likelihood of gaining the return on investment promised at the outset of the project.
Multi‐Vari Charts
Authors: Shainin Richard D.
Published: September 2014

This article introduces multi‐vari charts. It shows how they can be used to break process variation into several components of variation each associated with a source of variation. Breaking process variation into these components enables one, using the Pareto principle, to identify the biggest component of variation. Eliminating the source of this component is usually the best way to reduce process variation. This paper also presents several interesting examples of the application of multi‐vari charts.
Lean Methods, Creating Flow with
Authors: Srinivasan Mandyam M.
Published: September 2014

Lean thinking and lean manufacturing are terms associated with effective delivery of goods and services to the customer. These are management tools that are typically used in conjunction with Six Sigma. This article presents a brief discussion on the real purpose of lean, which is to create a smooth flow of goods or services across the entire supply chain. It describes how lead‐time reduction is vital to achieve this purpose. Lean is an ongoing effort, and this article also briefly discusses how organizations can use the tools and techniques of lean to undertake the lean journey.
Value Stream Analysis in Quality Management
Authors: Grayson Kevin
Published: September 2014

Value stream analysis as a quality management tool. Let value stream analysis determine where to direct you improvement efforts.
Six Sigma
Authors: Godfrey A. Blanton
Published: September 2014

Over the past two decades quality management has evolved into a leading‐edge practice called Six Sigma. Although not radically different from total quality management and total quality control, there are at least seven major differences. Sophisticated tools have been added to quality management practices in a structured way with intense application to achieve breakthrough results. Training methods for practitioners, often called Green Belts and Black Belts, are intense. Bottom‐line results are stressed.
Lean Accounting
Authors: Grayeski Frank , Stavish Len
Published: September 2014

Lean accounting calls upon the quality‐derived tools and methods used in both Lean and Six Sigma initiatives over the past several decades. This article discusses the application of the DMAIC (pronounced Da‐May‐Ick) model, which incorporates tools from Lean and Six Sigma bodies of knowledge, as an analytical framework for implementing lean accounting.
Change Management, Stakeholder Assessment in
Authors: Trabert Thomas F.
Published: September 2014

In managing organizational changes, a stakeholder assessment tool is used to increase the chances of a successful outcome. The benefit of a stakeholder assessment is to understand a stakeholder's level of support for a particular change initiative by first, determining his level of involvement in and commitment to the change and then assessing his ability to impact that change through his influence, authority, and control in the organization. The results of these two assessments of each stakeholder are summarized in a matrix showing the relationship of each stakeholder with the others. For stakeholders who support the change, a plan is created to increase their level of influence throughout. For those who are not supportive of the change, a plan is created to either gain their support or decrease their influence.
Sigma Metric (Sigma Level)
Authors: Does Ronald J.M.M. , Mast Jeroen
Published: September 2014

The Greek symbol σ is used in statistics as a measure of variation. Since, especially in industry, many quality problems are related to excessive variation, the concept σ has become a basis for the definition of a measure for conformance quality: the
sigma metric
or
sigma level
. The sigma level indicates the performance of a process, namely: what percentage of products or service deliveries conforms to the specifications.
Work‐Out
Authors: Hoerl Roger W.
Published: September 2014

The GE Work‐Out method for continuous improvement started in the late 1980s, as the brainchild of then‐CEO Jack Welch. Welch wanted to create an environment, modeled after the New England town meeting, in which people would feel free to share their frustrations about things hindering them in their daily work. He wanted frontline people to have the opportunity to air their opinions and ideas, and get an immediate answer from management on their suggestions to reduce bureaucracy and to address workplace issues. So Welch initiated a formal process, originally using facilitators outside of GE—which made people feel comfortable sharing things they would not normally share with their management. When using this approach, teams discuss specific work‐related issues about which they have firsthand knowledge. They share opinions, debate, argue, and eventually agree on suggestions to improve the situation. That is, they “work out” solutions to their issues. Management retains the right to approve or disapprove the suggestions, but they are expected to give immediate answers, rather than sending the suggestions to a “committee” for further discussion and review. This approach still works well today for appropriate problems, and should form an important place in the process improvement systems of organizations.
Critical‐to‐Quality Matrices
Authors: Hoerl Roger W.
Published: September 2014

In many applications, improvement teams need to take fuzzy, intangible customer needs, and translate them into measurable metrics. For example, the customer of an energy company producing electric generators may state that they want to deal with knowledgeable sales representatives from the company. This is a reasonable request, but how is the energy company to know if its sales representatives are “knowledgeable”? What does knowledgeable really mean, and how could the company measure it? Critical‐to‐quality (CTQ) matrices are one method used to take such fuzzy, intangible customer needs, and translate them into hard metrics that can be quantified. These metrics are often referred to as
critical‐to‐quality measures
. In addition to producing tangible metrics, these matrices also provide a prioritization of the metrics that enables focus on those metrics most critical to satisfying customers' needs. Such matrices can also be used in subsequent analysis, beyond identifying and prioritizing CTQs. For example, subsequent matrices can begin with CTQs and prioritize the most critical steps in the process that need improvement in order to impact the CTQs.
Process Maps, Construction of
Authors: Daniels Lorraine
Published: September 2014

Process mapping is an essential part of the quality efforts within any organization. This article addresses the basics of mapping and the reasons for mapping. Included are the generic steps for conducting a mapping session. Different formats of process maps are illustrated with the generic purpose of the map described. Best practices and difficulties of process mapping are also addressed.
Team Building
Authors: O'Brien Margaret G.
Published: September 2014

Teams are not new to our workplace; they have been around for a very long time and have been used effectively for short‐ and long‐term implementation strategies in organizations, as evidenced by all the articles and books written on teams and team work over time. Interestingly enough, when we began to examine critical performance factors on technical projects, we discovered the following.

Improving: technical skills were not enough to sustain gains in an organization. One of the missing factors was a systematic application to team building to complement the scientific technical model used in Six Sigma at NC State University.

The perception of how to operate in and on a team was disparate across an organization. Everyone had a different concept.

Including modules on teams and team building as well as others on project management, and leadership and change management, in teaching a technical topic such as Six Sigma for technical professionals would probably enhance the positive outcomes for the participants.

The Six Sigma curriculum committee determined that we would provide multiple modules in every technical class taught at our university. The modules focused on basic skills including team roles, conflict and self‐assessment, and reward/recognition.

This article shares some of the results and findings from the implementation of team modules in the technical non credit Six Sigma curriculum with our business and industry noncredit classes at NC State University in Raleigh, North Carolina.
Total Productive Maintenance
Authors: Nakamura Tsutomu
Published: September 2014

We define the concept of total productive maintenance (TPM). TPM was developed by the Japan Institute of Plant Maintenance (JIPM). We also perform a technical study to evaluate TPM implementation results in global companies as well as Japanese companies.
B
ayesian Control Charts
Authors: Colosimo Bianca M.
Published: September 2014

Control charts are widely used in industrial practice to check for process stability. The basic procedure of control charting is due to Shewhart and consists in periodically inspecting a sample of quality characteristics to decide whether the process is “out of control”. The out‐of‐control state is characterized by deteriorated performances and should be detected as soon as possible to bring the process back to its nominal or “in‐control” state. With reference to this objective, different Bayesian control charts have been proposed in the literature; the term “Bayesian” is used here to denote any approach that adopts Bayes' theorem within the monitoring procedure. In particular, the literature on Bayesian control charting is characterized by two streams of research. The first assumes that all the information required to detect the degraded state is enclosed in the posterior probability that the process is out of control. The second stream of research adopts a more classical perspective, which bases the out‐of‐control rule on detecting changes of the process parameters. In this case, Bayes' theorem allows one to compute the posterior distribution of the parameter of interest. The article reviews approaches proposed in both these streams of research, focusing mainly on the first type of Bayesian procedure.
Control Charts, Overview
Authors: Faltin Frederick W.
Published: September 2014

Control charts originated in the work of Dr. Walter Shewhart, the father of statistical process control, in the 1920s. Shewhart's groundbreaking insight was to apply statistical concepts to model the characteristic variability of a manufacturing process. His tool for doing so was the control chart, which was traditionally implemented graphically by having line workers plot production data over time. Corrective action would be taken when the plotted values fell outside of limits which were specified based on statistical properties of the process. Over the intervening decades, control charting techniques have been developed for monitoring and improving nearly all commonly encountered business processes, including nonmanufacturing processes such as supply chain/order fulfillment, human resource management, financial and accounting processes, call center management, and many others.
Control Charts for the Mean
Authors: Ryan Thomas P.
Published: September 2014

A Shewhart control chart for the mean is almost certainly the most frequently used control chart, due in part to its simplicity and ease of understanding. As with any statistical procedure, however, it must be used properly to be most effective, and this entails recognizing the assumptions upon which the chart is based and the importance of checking those assumptions in each application.
Cumulative Sum (CUSUM) Chart
Authors: Gan Fah Fatt
Published: September 2014

The use of the cumulative sum (CUSUM) charting procedure in process monitoring is first described. The strengths and weaknesses of the CUSUM charting procedure, with respect to the Shewhart charting procedure, are then illustrated with simulated data sets. Various other aspects of the CUSUM chart are also discussed. These include derivation of run‐length distribution, estimation of process mean using the CUSUM, weighting of samples for signaling, fast initial response for a newly start‐up process, simultaneous CUSUM schemes, design of the CUSUM chart, and multivariate CUSUM schemes.
Moving Range and

R

Charts
Authors: Champ Charles W. , Shepherd Deborah K.
Published: September 2014

Control charts for controlling the process mean are affected by changes in the process standard deviation. Therefore it is important to also control the process standard deviation. Various control charting methods have been developed for this purpose. Two commonly recommended statistical methods for controlling the process standard deviation are the moving range (
MR
) and the range (
R
) charts. The designs of
MR
and
R
charts are discussed in this article and each is illustrated with an example.
Multivariate Control Charts Overview
Authors: Runger George C.
Published: September 2014

Multivariate statistical process control is used to simultaneously monitor multiple measurements from a process. Similar to the case for a single variable, the objective is to detect assignable causes of variation. Multiple measurements are quite common in modern processes. This overview presents a brief summary of the advantages of an integrated solution for multiple measurements, types of applications, structure of the data, assignable causes, characteristics of charts, and signal interpretation.
Multivariate Exponentially Weighted Moving Average (
MEWMA
) Control Chart
Authors: Conerly Michael D.
Published: September 2014

The multivariate exponentially weighted moving average (MEWMA) control chart is used to simultaneously monitor several correlated process variables. The primary focus of this article is monitoring the process mean by constructing an MEWMA control chart. An overview of the application and use of MEWMA control charts is discussed. The plotting statistic is clearly defined and an illustrative example is presented.
Multivariate Cumulative Sum (
CUSUM
) Chart
Authors: Testik Murat C. , Runger George C.
Published: September 2014

Cumulative sum (CUSUM) control charts are important tools for the real‐time, on‐line monitoring of production processes. There are several multivariate extensions to the univariate CUSUM control chart. This work discusses the multivariate extensions to the CUSUM control chart and their uses. Basic formulas and notation are provided and the advantages and disadvantages of each are compared. References are given to allow the interested reader to investigate further.
Multivariate Charts for Variability
Authors: Lin Dennis K.J. , Yeh Arthur B.
Published: September 2014

In this article, we discuss two multivariate control charts for monitoring changes in a covariance matrix. The |
S
| chart is applicable when the subgroup size
n
is larger than the dimensionality
p
. The multivariate exponentially weighted moving squared‐deviation (MEWMS) chart is specifically designed for the case when
n
 = 1. Examples are given and discussed to demonstrate how each chart can be used in practice. Related issues and potential future research are also discussed.
Multivariate Control Charts, Interpretation of
Authors: Mason Robert L. , Young John C.
Published: September 2014

Signal interpretation is straightforward when only one variable is being monitored in a process. This is because separate charts can be constructed to monitor changes in the location and/or changes in the variation of the variable of interest. For a multivariate process consisting of
p
variables, signal interpretation is more complex because the signaling statistic can potentially be a function of all
p
variables. Not only do we have to detect location and variation changes in the
p
variables, but we also have to detect changes in the relationships (i.e., correlations) among the many variables. Further, procedures that identify the particular variable or group of variables that cause or contribute to the signal are limited. In this presentation, we discuss two procedures that are useful in signal interpretation when using Hotelling's
T
2
statistic in multivariate process control and briefly describe several alternative methods.
Regression Control Charts
Authors: Shu Lianjie , Tsui Kwok‐Leung , Tsung Fugee
Published: September 2014

The regression control chart, which integrates regression analysis and control chart theory, has proved useful in various fields of application in the last few years. The basic idea of the regression control chart is to monitor the process output after it has been adjusted for the effect of the external covariate(s). This paper provides an overall review of regression control techniques. The formulation of the regression control chart is presented, and its computation is illustrated. Some remarks on future issues are also made.
Variable Sampling Rate Control Charts
Authors: R. Reynolds Marion , Stoumbos Zachary G.
Published: September 2014

When control charts are used to monitor a process, the traditional practice is to take samples from the process using a fixed sampling rate (FSR). Variable sampling rate (VSR) control charts vary the sampling rate as a function of the data obtained from the process. With a VSR control chart the sampling rate is increased when there is some indication of a problem with the process, and decreased when there is no indication of a problem with the process. A VSR control chart signals in the same way as a standard FSR control chart when the indication of a problem with process is strong enough. There are several approaches to implementing the VSR concept. One approach is to allow the sampling interval to vary as a function of the data. Another approach is to allow the sample size for the current sample to vary as a function of the data in previous samples. A third approach is to allow the sample size for the current sample to depend on the data in this sample. Compared to traditional FSR control charts, all of the approaches to implementing the VSR concept will produce much faster detection of most process changes.
Precontrol
Authors: Ledolter Johannes
Published: September 2014

Precontrol monitors the acceptability of a process by comparing individual process values against their specification limits. Its goal is the prevention of nonconforming units. This article reviews precontrol and discusses its strengths and weaknesses.
Economic Design of Control Charts
Authors: Saniga Erwin M.
Published: September 2014

Control charts are designed by using a heuristic, or meeting statistical constraints or minimizing cost. The latter two design methods are calledstatistical designandeconomic design. A combination of them, calledeconomic statistical design, is a related criterion. In this chapter I discuss these methods and their relative strengths and weaknesses. I then review some literature that addresses the choice of an optimal economic process control policy and subsequently discuss how one can design the optimal economic control chart. I also review and address some of the criticism of economic design.
Average Run Lengths and Operating Characteristic Curves
Authors: Human Schalk W. , Graham Marien A.
Published: September 2014

Control charts are typically used in establishing whether a process is in a state of statistical control or not. The performance of any control chart is an important consideration and characteristics of the run‐length distribution are generally used to assess its performance. Two popular measures of a control chart's performance are the expected value of the run‐length distribution, called the
average run length
(
ARL
), and the operating characteristic (
OC
). Both are measures of how quickly a chart will detect or react to a change in the process and can be used to compare control charting plans. Both these measures provide useful information about the operational performance of a control chart. We discuss the
ARL
and the
OC
of the well‐known Shewhart

X

chart when both the process mean and variance are known. The application of
OC
curves in acceptance sampling is also considered.
Change‐point Methods in Statistical Process Control
Authors: Hawkins Douglas M. , Qiu Peihua , Zamba Kokou D.
Published: September 2014

Change‐point methodologies applied to statistical process control are predicated on the possibility that a special cause induces a shift from an in‐control statistical model to an out‐of‐control statistical model, and so are particularly attractive for persistent special causes. Along with indications of a loss of control, they provide estimates of when the shift occurred, and (if needed) of the before‐ and after‐shift process parameters. A less obvious advantage is that some change‐point proposals allow for the near‐universal situation that the in‐control distribution of process readings is not known exactly. This feature largely removes the need for extensive phase I calibration studies, and allows phase II production use to start early. In addition to the stand‐alone use of change‐point methodologies for both signaling and diagnosing the effects of special causes, they have been proposed as tools for following up signals given by other charting methods, when their likelihood properties lead to good estimators of the time of occurrence and effect of the special cause.
Computer Experiments
Authors: Morris Max D.
Published: September 2014

Computer models are often used as substitutes for physical systems in engineering studies. When these studies are organized in advance with particular goals in mind, they are sometimes called
computer experiments
. We briefly describe a few general types of computer experiments and how Gaussian stochastic processes are used as statistical
metamodels
or
surrogates
in some of these settings.
Factorial Experiments
Authors: Kenett Ron S. , Steinberg David M.
Published: September 2014

Proactive experiments are an essential element of the learning process, in all application areas. Statistically designed experiments have been used to accelerate learning since their introduction by R.A. Fisher in the first half of the twentieth century. In this article we review factorial and fractional experiments and provide some examples from the chemical and semiconductors industries marketing and computer intensive simulations, and and briefly discuss the role of factorial experiments in statistical education.
Foldover Designs
Authors: Li William
Published: September 2014

Foldover is a commonly used follow‐up technique. A foldover design is constructed by reversing the signs of several columns of the initial design. We demonstrate how a foldover design can be used to untangle aliased effects, especially main effects and two‐factor interactions, found from the analysis of the initial design. We then discuss several methods for constructing foldover designs and introduce optimal foldover plans for both regular and nonregular factorial designs. The main advantage and disadvantage of the foldover method are also discussed.
L
atin Hypercube Designs
Authors: Tang Boxin
Published: September 2014

Latin hypercubes are a rich class of designs that are suitable for computer experiments and numerical integration. They are easy to generate and achieve maximum stratification in each of the univariate margins of the design region. This article introduces Latin hypercubes, explains how they can be used in computer experiments and numerical integration, and discusses their strengths and weaknesses. A design may not perform well in terms of other criteria such as those of space filling and orthogonality, simply because it is a Latin hypercube. The article concludes with a discussion on some of the methods for constructing Latin hypercubes that have better space‐filling or orthogonality properties.
L
enth's Method for the Analysis of Unreplicated Experiments
Authors: Lenth Russell V.
Published: September 2014

Lenth's method is an objective method for deciding which effects are active in the analysis of unreplicated experiments, when the model is saturated and hence there are no degrees of freedom for estimating the error variance. The article reviews its formulation, computation, and examples, and discusses the choice of critical values and the presentation and interpretation of the results.
Minimum Aberration
Authors: Deng Lih‐Yuan
Published: September 2014

The minimum aberration criterion is useful to select good regular two‐level fractional factorial designs. To select good nonregular designs, the generalized minimum aberration criterion can be used. It is a natural extension of the minimum aberration criterion. Other related criteria are also discussed.
Plackett‐Burman Designs
Authors: Tyssedal John
Published: September 2014

Plackett–Burman designs are important screening designs. In this article we explain how they can be constructed and how they can be classified according to projectivity of two‐level designs. We give a reasonably exhaustive list of existing methods for analyzing them and illustrate one of these methods by an example. Some techniques useful for constructing designs with good projective properties for various experimental situations are described at the end.
Factorial Designs, Resolution of
Authors: Romano Daniele
Published: September 2014

After providing the definition of resolution for 2k−pdesigns we present two rationales that justify the practical importance of resolution as a criterion for selecting good fractional designs. Insight is given into designs of resolution III, IV, and V, i.e., the most widespread in practice. Then we describe how to determine the resolution of a design and a simple rule for finding half‐fractions of maximum resolution. Smaller fractions of maximum resolution can be found in specialized books referenced in the text. We also describe some circumstances when the resolution criterion needs to be supplemented or replaced by other user‐relevant criteria.
Sample‐Size Determination in Experimental Designs
Authors: Lenth Russell V.
Published: September 2014

Sample‐size determination is a procedure by which we decide how much data are required in order to achieve stated goals for a statistical study. The article focuses primarily on the two most popular paradigms (confidence interval width and power), and on the use of software to solve sample‐size problems. It discusses what situations are most pertinent with regard to sample‐size planning, the importance of eliciting a relevant effect size, and the use of pilot data. An illustration is given for a complex experiment. There is also information on the related issue of sample‐size allocation, and on sample‐size requirements for pilot studies.
Split Plot Designs in Industry
Authors: Goos Peter
Published: September 2014

Split‐plot designs are
nested designs
that are frequently used in industrial experimentation and result in correlated observations. A correct analysis of the data from split‐plot designs takes account of the correlation. However, as split‐plot designs are often used inadvertently, the typical split‐plot correlation structure is not recognized in many applications. The resulting statistical analysis is then inappropriate. In this contribution, the agricultural origin of split‐plot experimentation is discussed first. Design aspects are discussed as well as the classical analysis of variance (ANOVA) (
see
Analysis of Variance
) and industrial applications of split‐plot designs. It is explained that a generalized least‐squares analysis can be used for estimation and inference and different approaches to constructing split‐plot designs are discussed.
Uniform Experimental Designs
Authors: Fang Kai‐Tai , Hickernell Fred J.
Published: September 2014

The uniform experimental design is one important kind of space filling design and has been applied in computer experiments, laboratory experiments under model uncertainty, and experiments with mixtures. The uniform design is robust against model misspecification.
Bergman‐Hynén Method for Dispersion Effects
Authors: Arvidsson Martin , Bergman Bo
Published: September 2014

This paper presents the Bergman‐Hynén method for dispersion effect identification in unreplicated fractional factorials. In contrast with other methods the Bergman‐Hynén test statistic has a known reference distribution which allows use of formal hypothesis testing for the dispersion effect identification.
Ridge Analysis in Experimental Design
Authors: Draper Norman R.
Published: September 2014

Ridge analysis is a valuable tool for exploring second‐order surfaces that have been fitted to experimental data. Paths of absolute maxima (ridge of maxima), absolute minima (ridge of minima), or of intermediate turning values (ridges that are neither overall maxima nor minima but are best locally) can be located and followed. The paths can start from
any
selected location in the predictor space; often the coded origin of the experimental space would be chosen.
Attributes Sampling Schemes in International Standards
Authors: Balamurali Saminathan , Göb Rainer , Jun Chi‐Hyuck
Published: September 2014

Sampling tables for the inspection of attribute quality characteristics were developed during World War II by the United States Department of Defense. In 1950, these tables led to the first military standard MIL‐STD‐105A which evolved into the latest version MIL‐STD‐105E issued in 1989. Further national and international standards emerged as derivatives of MIL‐STD. On international level, the International Standards Organization (ISO) introduced the attributes sampling standard ISO 2859 which has been adopted as a national standard in many countries. The article analyzes the purpose, structure, and operation of MIL‐STD‐105E and of ISO 2859.
Engineering Process Control
Authors: He Bin , Adams Benjamin M.
Published: September 2014

This article addresses the similarities and distinctions between traditional statistical process control (SPC) methods (
see
Control Charts, Overview
) and engineering process control (EPC) methods (
see
Feedback Control
). Both techniques share a primary goal of reducing process variation while maintaining target values. The strategic and mathematical foundations for the techniques are, however, quite different. These differences have led to confusion surrounding the appropriate choice of control method when both SPC and EPC appear to be viable quality tools. This article provides a brief overview of SPC and EPC methods and seeks to clarify the relationship between these two complementary methods.
Assessment of Experimental Designs
Authors: Anderson‐Cook Christine
Published: September 2014

Collecting data as part of a designed experiment involves choices between different candidate designs. The criteria for choosing between these designs involves many competing objectives which frequently force us to make trade‐offs based on which of these ideal characteristics of the design we think are most important. Optimality criteria are single‐number summaries for the “goodness” of the design, which typically focus on the quality of estimation or prediction. In addition to these measures, the robustness of the design to model misspecification, problems with data collection during the experiment, or erroneous data are also important. A number of the characteristics of good designs are outlined and compared, and some graphical methods for better understanding the performance of a design are shown. While there is no single answer for how to choose a good design, an experimenter can make a better choice of which objectives are most important for a particular application by considering a variety of diverse criteria.
Integrating Computer and Physical Experiment Data
Authors: Williams Brian J. , Higdon David M.
Published: September 2014

The investigation of complex physical systems utilizing sophisticated computer models has become commonplace with the advent of modern computational facilities. In many applications, experimental data on the physical systems of interest is extremely expensive to obtain and hence is available in limited quantities. The mathematical systems implemented by the computer models often include parameters having uncertain values. This article provides an overview of statistical methodology for calibrating uncertain parameters to experimental data. This approach assumes that prior knowledge about such parameters is represented as a probability distribution, and the experimental data is used to refine our knowledge about these parameters, expressed as a posterior distribution. Uncertainty quantification for computer model predictions of the physical system are based fundamentally on this posterior distribution. Computer models are generally not perfect representations of reality for a variety of reasons, such as inadequacies in the physical modeling of some processes in the dynamic system. The statistical model includes components that identify and adjust for such discrepancies. A standard approach to statistical modeling of computer model output for unsampled inputs is introduced for the common situation where limited computer model runs are available. Extensions of the statistical methods to functional outputs are available and discussed briefly.
Process Capability, Error in Estimation of
Authors: Pearn Wen Lea , Wu Chien‐Wei
Published: September 2014

Process capability indices (PCIs) are widely used to provide numerical measures of process performance, which have received considerable research attention and increased usage in process assessments and purchasing decisions during the last decade. A substantial majority of capability research works that appeared in the literature do not take into account gauge measurement errors. However, the gauge measurement errors have a significant effect on estimating and assessing process capability since measurement systems are not 100% precise. An inaccurate measurement system can thwart all the benefits of improvement endeavors resulting in poor quality. Analyzing process capability without considering gauge capability may often lead to unreliable decisions. It could result in a serious loss to producers if gauge capability is not being considered in process capability estimation and testing. Since measurement errors unfortunately cannot be avoided, using appropriate confidence coefficients and power becomes an essential task. In this article, we summarize the issue on estimating and testing the process capability is the presence of measurement errors, provide a numerical example discussing the influence of measurement errors, and cite additional references for more discussions on gauge repeatability and reproducibility (GR&R) studies.
Process Capability Indices, Nonnormal
Authors: Chou Youn‐Min , Polansky Alan M.
Published: September 2014

Data from manufacturing processes may often come from nonnormal distributions. Process capability indices for nonnormal distributions have attracted much research in the last 20 years. This paper presents process capability indices for nonnormal distributions and reviews various approaches to deal with the problem of nonnormality in evaluating process capability.
Process Capability Indices, Nonparametric
Authors: Polansky Alan M.
Published: September 2014

The capability of a manufacturing process is a measure of the ability of the process to consistently produce items within engineering specifications. Most early attempts to assess the capability of a process were based on the assumption that the quality characteristic of interest follows a parametric distribution, usually normal. Several studies of process data indicate that many quality characteristics follow nonnormal distributions.Further, theoretical and empirical studies of measures of process capability indicate that violations of the assumption relating to the distribution of the quality characteristic can produce serious errors in conclusions about the capability of the process. This article reviews methods that have been developed to assess process capability within a nonparametric framework. Such methods require few specific assumptions about the distribution of the quality characteristic. The methods reviewed in this article include those based on nonparametric tolerance limits, nonparametric estimates of the process yield, and indices based on the percentiles of the process distribution.
Process Capability Indices, Multivariate
Authors: Fuchs Camil , Kenett Ron S.
Published: September 2014

Process capability is determined by comparing the actual performance of a process with required specifications. Several indices have been proposed to report the capability of a process in the univariate case. When performance is tracked in several dimensions, an extension of these indices is required. We present several multivariate capability indices, and extend the idea of multivariate tolerance regions for assessing the capability of a process.
Multivariate Process Capability Indices, Comparison of
Authors: Wang Fu‐Kwun
Published: September 2014

In this article, some methods of evaluating the process capability of products with multiple quality characteristics are reviewed. Multivariate process capability indices are introduced and a summary is made systematically. An example is used to demonstrate the performance of different multivariate process capability indices.
Process Capability Indices, Comparison of
Authors: Spiring Fred
Published: September 2014

The most common process capability indices are examined from several perspectives. A brief historical review outlines recent developments from simple variability measures to today's more sophisticated indices. The behavior of the indices
C

pk

,
C

pm

, and
C

pmk

is compared with
C

p

for processes that are not necessarily centered at target, illustrating the relationships among the various indices. A weight function is presented that allows the functional form of a variety of indices to be examined. A discussion of process yield and the resulting interpretation from a quality loss perspective is presented and the differing philosophies represented by the competing indices are highlighted.
Yield Surface Modeling
Authors: Maass Eric Charles
Published: September 2014

Yield Surface Modeling™ (YSM) has been used to perform multiple‐response optimization for over 60 new products with high yields and first‐pass successes. The approach expands on response surface modeling (RSM), using the Generation of System Moments method to forecast the mean and variance, and then the process capability index (
C

pk

), and yield for each response—effectively replacing the expected value of the response with the expected value of the
C

pk

and yield for each combination of settings of factor levels. Composite
C

pk

and composite yield surfaces are generated for those combinations of settings of factor levels. The method is illustrated with a case study for an integrated alternator regulator (IAR) (integrated circuit). The strengths and limitations of the approach are discussed, including the value of the sensitivity analyses that come from the use of the Generation of System Moments method.
Control Charts and Process Capability
Authors: Lovelace Cynthia R.
Published: September 2014

Statistical process control (SPC) charts play a very important role in the determination of process capability. It is important that SPC be achieved
via
the use of appropriate control charts before process capability indices are calculated, so that the indices reflect the long‐term capability of a process to produce product within specification. A significant portion of the theoretical research dedicated to process capability indices has focused on those index estimators that utilize a pooled estimator of process variance, but data collected in subsamples from an in‐control SPC chart, most likely an

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chart, is preferred in practice for index estimation to insure process stability. This article is focused on the significant work published to date on process capability indices estimated using subsamples, including confidence bounds and hypothesis testing procedures for the primary indices
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Measurement Systems Analysis, Overview
Authors: Michelson Diane K.
Published: September 2014

Measurement systems analysis is a way to quantify the variability of a metrology process. The fundamental technique is the gauge
repeatability and reproducibility
(GR&R) study, which yields an estimate of . The estimate is then used to determine whether or not the metrology system is capable of measuring the process under study. The stability of the measurement system is also determined.
Gauge Repeatability and Reproducibility (
R
&
R
), Variance Components in
Authors: Borror Connie M. , Burdick Richard K. , Montgomery Douglas C.
Published: September 2014

Traditionally, a standard gauge repeatability and reproducibility (R&R) study consisted of a two‐factor crossed design with operators and parts; with each operator measuring each part say
r
times. There are numerous situations where there are more than two factors, the design is not crossed but possibly nested, or the appropriate design is crossed and nested. In this article, variance components for gauge R&R studies are presented for these more complex experimental situations.
Measurement Error and Uncertainty
Authors: Borror Connie M.
Published: September 2014

In this article, some very basic definitions and concepts concerning measurement error and uncertainty are provided. The information provided in this article is an introduction to the differences between uncertainty and error. Measurement error and uncertainty are defined and several resources are provided for discussions and critiques of the uses of these terms and how they are applied in various situations.
Measurement Systems Analysis, Capability Measures for
Authors: Larsen Greg A.
Published: September 2014

There are two “families” of measurement capability metrics in common use. There are those that compare measurement variation to total or part variation, and there are those that compare to the tolerance or specification width. These capability metrics are all functions of the variance components in the statistical model used to describe the response of interest. In addition to these capability metrics, it is also often useful to estimate the misclassification rates that arise from using the measurement system to discriminate between “good” and “bad” items being tested. An additional capability metric is obtained from the ratio of the estimated misclassification rates to the rates that would occur purely due to random chance. This section will define several capability measures, how they are related to one another, and give some common criteria for capability. Additional topics discuss the effect of the unit of measure and present a situation where none of the aforementioned metrics are particularly useful in quantifying the capability of a measurement process.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Destructive Testing
Authors: Trip Albert , Mast Jeroen
Published: September 2014

The fundamental problem of gauge capability studies is that an object can be measured only once. After this measurement the object is different, sometimes emphatically so because the measurement is destructive. A standard gauge repeatability and reproducibility (R&R) study, to determine the precision of the measurement, is designed for nondestructive measurements, and assuming temporal stability of the objects. If temporal stability is beyond assumption, or if measurements can only be obtained by damaging the object—both types are called
destructive measurements
—then additional assumptions are needed to estimate the measurement precision. Several possibilities are given and reviewed. The assumptions are essentially attempts to model object‐to‐object variation, aiming at separating this from measurement precision. The failure of these attempts will show up in larger than necessary estimates of the measurement variation.
Process Capability Indices, Alternatives to
Authors: Post Richard I.
Published: September 2014

Since we (the statistical community) are to blame for the introduction of process capability indices (PCIs) and their ensuing use and abuse in the industrial world, it is now up to us to provide an alternative to PCIs that is both statistically sound and relatively simple to understand. The problems with the present capability indices have been well documented elsewhere (e.g., Gunter 1, Kotz 2, Journal of Quality Technology (JQT) 24 3) and these will not be addressed here. Instead, the dual roles of specifications and PCIs in process and product development and in the manufacturing environment will be discussed. A basic approach to meet most of the goals of PCIs without some of their problems will then be introduced. Incorporated into the measures will be the concepts of confidence level and sample size. An example of this procedure is included. The relatively simple assumptions of this approach can then be modified to meet requirements where PCIs are presently being applied. These measures are intended to be a foundation rather than a total solution to all the needs of PCI users. However, these techniques will provide a direction for future work.
Interlaboratory Comparisons
Authors: Guthrie William F.
Published: September 2014

An interlaboratory comparison for a measurement procedure is an exercise carried out by a group of laboratories to compare their performance or assess a measurement standard. Interlaboratory comparisons are typically used for one of three main purposes, to assess the random variation in measurement results across a population of laboratories, to determine the systematic differences in results among a fixed set of laboratories, or to determine the value of a physical property of an artifact or a population of artifacts. There are several design issues to consider when planning an interlaboratory study. These include selection of participating laboratories, number and stability of artifacts, time frame for the comparison, and numbers of replicate measurements made in each laboratory. The analysis of interlaboratory data ranges from simple, but informative, graphical analyses of the differences between laboratories to methods for constructing approximate confidence intervals for linear combinations of variance components. Probabilistic intervals for the physical property of an artifact or population of artifacts can also be obtained using maximum likelihood, generalized pivot quantities, or regression methods that handle random effects between laboratories and nonconstant measurement variation within each laboratory.