bluuebunny/crossref_metadata_2025_split huggingface dataset:
Theory of Constraints
Authors: Mordoch Avraham 
Published: September 2014

Abstract

The
theory of constraints
(
TOC
) is a holistic, overall management philosophy that aims to (a) resolve systems‐oriented problems and (b) continually achieve more of the goal of a defined system. If that system is, e.g., a for‐profit business, then the goal becomes one of making more money, in the present as well as in the future.
Statistics in Data and Information Quality
Authors: Redman Thomas C. 
Published: September 2014

Abstract
In many respects, statistical foundations and techniques that worked so well to help improve the quality of manufactured goods have proved extensible to data and information. But data and information differ from manufactured products in some important ways, presenting both challenges and opportunities. For example, unlike manufactured products, data and information have no measurable physical qualities such as length or viscosity. This article discusses several such differences and their implications for quality management.
Process Reengineering
Authors: Pall Gabriel A. 
Published: September 2014

Abstract

In this article, we first define business process reengineering (BPR) as the
fundamental
rethinking and
radical
redesign of
business processes
, with the purpose of achieving
drastic
improvement over current performance. We then examine the conditions that are conducive to its application. Among the key concepts and methods of reengineering, its nine basic principles are presented and the enabling role of information technology is discussed.

The discussion on the implementation of reengineering includes a description of the human infrastructure, and the recommended budgetary arrangements.
As one of its strengths, reengineering provides the solution to the two known dilemmas of process management: (1) variation and dispersion and (2) scale and complexity.

The article cites a number of factors that can cause BPR initiatives fail to reach their initial objectives, and discusses three known weaknesses of the methodology, including the
process paradox
.
Concept Selection Matrices
Authors: Maass Eric Charles 
Published: September 2014

Abstract

Concept selection matrices provide a way to evaluate and select among several alternative approaches, architectures, or solutions using a set of selection criteria. The selected alternative might be one of the alternatives considered or a new alternative generated or developed as a hybrid of several alternatives during the concept selection process (referred to as
controlled convergence
). The concept selection matrix and associated process of controlled convergence is illustrated with a nontechnical and a technical example. Weighted and categorized matrices are also briefly discussed.
Statistical Process Control,Bayesian
Authors: Tsiamyrtzis Panagiotis , Hawkins Douglas M. 
Published: September 2014

AbstractStatistical process control (SPC) is a collection of statistical methods which can be used to maintain quality of an outcome of a process. The SPC tools originally developed within a purely frequentist framework required independent identically distributed observations and known process parameters. Before actual process monitoring can begin (phase II) they require a phase I data gathering exercise for chart calibration. The Bayesian SPC approach avoids the need for a large phase I calibration exercise, and provides a consistent well defined framework for use of prior information which is almost always available but is hard to accommodate and so is ignored by the traditional frequentist SPC methods. Furthermore, in the Bayesian SPC we can transform the inferential procedure to an equivalent decision theory problem. The ability to provide (relatively easy) forecasting is another great advantage of the Bayesian perspective. Although Bayesian methods may require substantially more computing effort than classical control charts, their benefits increasingly outweigh this cost in the modern setting of accessible computer power.
Exponentially Weighted Moving Average (
EWMA
) Control Chart
Authors: Crowder Stephen V. , Wiel Scott A. 
Published: September 2014

Abstract
An exponentially weighted moving average (EWMA) is a filter of sequential process data, giving the most recent observation the most weight and older observations exponentially less weight. EWMA control charts are especially useful in detecting small shifts in the mean or variance of a process. For processes that tend to wander over time, EWMAs are also a basic technique for predicting future observations, for making automatic adjustments to a process and for monitoring a sequence of forecast or regulation errors.
Control Charts for Attributes
Authors: Jones‐Farmer L. Allison 
Published: September 2014

Abstract

Control charts can be used to monitor characteristics of a process that are measured using categorical or attribute data. The most common attribute control charts are the
p
‐chart and
c
‐chart. This section discusses the scenarios in which the
p
‐ and
c
‐charts are useful. A detailed example of the use of a
p
‐chart is given. Strengths and weaknesses of attribute charts are discussed.
H
otelling's

T

2
Chart
Authors: Sullivan Joe H. 
Published: September 2014

Abstract

Often referred to as a
Shewhart‐type control chart
, Hotelling's
T
2
control chart is based on a multivariate generalization of the Student's
t
statistic for univariate observations. As is the case of the univariate Shewhart chart, Hotelling's
T
2
chart is a good, general purpose tool that is sensitive to outliers and sustained shifts in the mean vector, although not effective in detecting quality improvements that might be reflected in decreased dispersion about the in‐control mean vector. Compared with the alternative of monitoring the measurement variables separately, this method takes into account the in‐control correlation among the measurements in detecting shifts in the mean vector resulting from special causes of variation, which is considered an advantage.
Control Charts, Nonparametric
Authors: Chakraborti Subhabrata , Graham Marien A. 
Published: September 2014

AbstractStandard control charts are often based on the assumption that the observations follow a specific parametric distribution, such as the normal. In many applications we do not have enough information to make this assumption and in such situations, development and application of control charts that do not depend on a particular distributional assumption is desirable. Nonparametric or distribution‐free control charts can serve this broader purpose. We discuss some ideas and recent developments in the area of univariate nonparametric control charts. Most of these charts are for monitoring the location; few charts are available for scale. A key advantage of nonparametric charts is that the in‐control run length distribution is the same for all continuous process distributions. Nonparametric charts are more robust and efficient than their parametric counterparts under heavy‐tailed and/or asymmetric distributions.
Control Charts for Short Production Runs
Authors: Elam Matthew E. 
Published: September 2014

AbstractThis article presents control charts for short production runs. The existing research can be placed into four categories. They are pooled data control charts, control charts with greater sensitivity, control charts for process inputs, and control charts with modified limits. This article also presents advantages and disadvantages of each. Control charts with greater sensitivity include exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and economically designed control charts. Control charts with modified limits include Q charts and two‐stage short‐run control charts. The particular focus of this article is the two‐stage short‐run control charting methodology. This article concludes with future research opportunities in two‐stage short‐run control charts.
Control Charts for Batch Processes
Authors: Lewis Donald K. 
Published: September 2014

Abstract

Applying control charts successfully to batch processes requires the recognition of some distinguishing features of batch processing
versus
single‐unit processing. Traditional Shewhart‐type charts can be modified for use in batch‐processing practitioners of statistical process control (SPC); however, entirely different control chart methods and practices will often be required. This article reviews the common features of batch processing and the data available to control chart practitioners from typical batch processes and suggests alternatives to standard Shewhart charts for effective process monitoring and control.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies
Authors: Vardeman Stephen B. 
Published: September 2014

Abstract
This article discusses basic design considerations for so‐called gauge repeatability and reproducibility studies. These common industrial measurement studies are used to investigate the precision of measurement systems that employ more than one operator to make measurements with a single gauge/measurement device. Data from them are typically used to estimate functions of variance components in random‐effects models that quantify both “within part‐by‐operator combination” and “between‐operator” variation in measurement.
Block Designs, Incomplete
Authors: Prescott Philip 
Published: September 2014

AbstractIn experimental situations, incomplete blocks occur when there is insufficient homogeneous material within a block for all treatments to be applied. Balanced incomplete block designs are such that all treatments are equally represented within the design and every pair of treatments occurs together within a block the same number of times. We give examples of unreduced and reduced balanced incomplete block designs, and briefly discuss several methods of constructing such designs. The intrablock analysis of variance for the general case is given in detail, and this is extended to the simplified analysis which results when the design is balanced.
Latin Squares and Related Experimental Designs
Authors: Morgan J. P. 
Published: September 2014

AbstractLatin squares and their generalizations are explored for their utility as experimental designs. The popularity of Latin square designs is due to their parsimonious use of experimental material while providing orthogonal estimation of treatment factors. As a design with twov‐level blocking factors, av × vLatin square can evaluate levels of onev‐level treatment factor orthogonally to both of the blocking factors while requiring onlyv2experimental units (one for each combination of the blocking factors). Generalizations include: designs with more rows and columns (F‐squares and rectangles, Youden designs), squares with more than one experimental unit per row‐column combination (semi‐Latin squares), and designs based on multiple squares. Mutually orthogonal Latin squares allow more treatment factors to be orthogonally accommodated with the same number of experimental units as a single Latin square. This concept is explored in the wider context of the generalizations listed above.
Optimal Design
Authors: Atkinson Anthony C. 
Published: September 2014

AbstractThe article focuses on the use of the methods of optimal experimental design to solve a number of nonstandard problems arising in quality and reliability. Examples include response‐surface designs for an arbitrary number of observations. Because some combinations of factors may produce unstable results, designs are found over irregular design regions. The method of dividing such designs into blocks is also shown. Mixture designs, again over constrained regions, are mentioned. The theory underlying the construction of the designs is integrated using a general equivalence theorem which leads to algorithms for the construction of designs and to the assessment of the efficiency of designs. References are given to recent work.
Orthogonal Arrays
Authors: Notz William I. 
Published: September 2014

AbstractIn this article we use a simple example to introduce the notion of orthogonality as a desirable characteristic of an experimental design. In particular, we show that a lack of orthogonality prevents us from distinguishing the effects of some of the factors. We then formally define a class of designs, known asorthogonal arrays, which possess attractive orthogonality properties. Some examples of orthogonal arrays are given. These are followed by comments about the construction of orthogonal arrays and sources where one can find tables of orthogonal arrays. Next, we discuss the use of orthogonal arrays in several applications. These include fractional factorial experiments for controlling aliasing, with special attention to designs in which no main effects are aliased; screening experiments in which orthogonal arrays have special projection properties; response surface modeling in which orthogonal arrays yield optimal designs; robust parameter designs in which orthogonal arrays are the basis for cross arrays; and in computer experiments where orthogonal arrays produce designs that space points evenly on lower‐dimensional subspaces. We conclude with an example.
Screening Designs
Authors: Jacroux Mike 
Published: September 2014

Abstract
In this article, we consider experimental situations where the initial number of predictor variables to be studied in an experimental process is large and it is desired to identify which variables actually have a significant influence on the process being investigated. Screening designs are used primarily for this purpose. Here we describe some of the two‐level screening designs that are commonly used along with some of their individual advantages and disadvantages.
Supersaturated Designs
Authors: Lin Dennis K.J. 
Published: September 2014

AbstractIn the early stages of industrial experimentation, one often has a large set of candidate factors believed to have a possibly significant influence on the response of interest, although it is reasonable to assume that only a small fraction are influential, a condition known aseffect sparsity. A design of an experiment with the property that the number of potential effects exceeds the number of experimental runs (observations) is called asupersaturated design. These designs are useful as screening tools. This article explains what these designs are, why you would want to use them, and what are their pros and cons relative to other designs. Both design and analysis issues will be discussed.
Central Composite Designs
Authors: Draper Norman R. 
Published: September 2014

AbstractChoosing a suitable experimental design is an important first step in investigations of the factors that may influence one or more response variables. Such investigations often lead to the representation of the response variable as a second‐order (quadratic) surface in the predictor variables. Central composite‐type designs are popular, classical, and excellent choices for this purpose. A portion of a design of this type can be performed initially to fit a first‐order model. A second‐order portion can then be added, if it becomes clear that the first‐order fit suggests the need for supplementary information. A three‐predictor variable data set is worked through to illustrate the decisions involved in these types of studies.
Mixture Experiments
Authors: Piepel Greg F. 
Published: September 2014

AbstractAmixture experimentinvolves combining two or more components in various proportions or amounts and then measuring one or more responses for the resulting end products. Other factors that affect the response(s), such as process variables and/or the total amount of the mixture, may also be studied in the experiment. Amixture experiment designspecifies the combinations of mixture components and other experimental factors (if any) to be studied and the response variable(s) to be measured.Mixture experiment data analysesare then used to achieve the desired goals, which may include (a) understanding the effects of components and other factors on the response(s), (b) identifying components and other factors with significant and nonsignificant effects on the response(s), (c) developing models for predicting the response(s) as functions of the mixture components and any other factors, and (d) developing end products with desired values and uncertainties of the response(s).Given a mixture experiment problem, a practitioner must consider the possible approaches for designing the experiment and analyzing the data, and then select the approach best suited to the problem. Eight possible approaches are (a) component proportions, (b) mathematically independent variables, (c) slack variable, (d) mixture amount, (e) component amounts, (f) mixture‐process variable, (g) mixture of mixtures, and (h) multifactor mixture. The article provides an overview of the mixture experiment designs, models, and data analyses for these approaches.
Dispersion Effects
Authors: Tort‐Martorell Xavier , Marco Lluis 
Published: September 2014

Abstract
Reduction of variation is a crucial ingredient for quality and productivity improvement. An economical and effective approach is to conduct statistically designed experiments, not only to identify controllable factors that affect the mean level (location) of the response, but also to identify and learn about the ones that affect the dispersion. This article explains how control factors can affect the response variability, how they can be identified and estimated, and gives two well‐documented examples that show how this knowledge can be used to improve products and processes.
Product Array Designs
Authors: Zhu Yu 
Published: September 2014

AbstractThis article is a review of product arrays proposed by Taguchi for robust design experiments. The motivation and advantage of using product arrays in robust design are discussed. Furthermore, alternative modeling approaches such as the location‐dispersion modeling approach and the response modeling approach are introduced. As an extension of product arrays, compound orthogonal arrays are defined. This article also discusses the optimal selection of product arrays.
Signal‐Response Systems
Authors: Miller Arden 
Published: September 2014

Abstract
The defining characteristic of a signal–response system is that the function of the system requires a response variable to take different values when the value of a signal factor changes. The performance of the system depends on the relationship between the signal and the response. Improving signal–response systems using designed experiments is usually approached as a special case of parameter design. This article describes several types of signal–response systems, discusses how their performance depends on the signal–response relationship and presents methodology for analyzing experiments aimed at improving their performance.
Box‐Meyer Method for Dispersion Effects
Authors: Meyer R. Daniel 
Published: September 2014

Abstract

One aspect of product quality is variability in finished product. Taguchi showed the world that variability is a consequence of product design and not just manufacturing performance. He also showed us that variability can be studied in engineering optimization experiments. Product variables that affect variability are said to have dispersion effects (
see
Dispersion Effects
). Using the Box‐Meyer method, dispersion effects can be estimated simply from the same unreplicated two‐level designs used to study effects on the mean performance.
Harvey's Method for Dispersion Effects
Authors: Brenneman William A. 
Published: September 2014

AbstractThere has been considerable interest in the use of unreplicated designed experiments to identify both location and dispersion effects. This article takes a look at the two‐step approach which incorporates a noniterative identification step followed by an iterative final estimation step. This two‐step approach eliminates some of the bias present in the noniterative methods.
Process Yield
Authors: Lin Pi‐Chuan , Pearn Wen Lea 
Published: September 2014

Abstract

Process capability indices (PCIs), establishing the relationship between the actual process performance and the manufacturing specifications, have been the focus of recent research in quality assurance and capability analysis. In this article, PCIs dedicated to the measurement of the yield of normally distributed processes with two‐sided specification limits and one‐sided specification limit are discussed. The yield measure formulas based on
C

pk

,
C

pm

,
C

pmk

,
S

pk

,
C

PU

, and
C

PL

are all displayed. The corresponding upper bounds of nonconforming parts per million (NCPPM) for various values of
C

pk

,
C

pm

, and
C

pmk

and the corresponding NCPPM for various
S

pk

values are tabulated.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Misclassification Rates
Authors: Burdick Richard K. 
Published: September 2014

Abstract
To properly monitor and improve a manufacturing process, it is necessary to measure attributes of the process output. Measurements of these attributes are taken by a gauge that has associated measurement variability. Gauge R&R studies are used to determine if the measurement system variability is harmfully large. The approach for evaluating the goodness of the measurement system based on a gauge R&R study is to determine the probability that a part is misclassified. The better a measurement system, the less likely it is to classify a “good” part as “bad”, and a “bad” part as “good”. References are provided for computing bounds on the probabilities of these misclassifications.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Confidence Intervals for
Authors: Leong Chiang Andy Kok 
Published: September 2014

Abstract
Gauge repeatability and reproducibility (R&R) studies are often conducted to determine the capability of a gauge for monitoring a manufacturing process. In this paper, we discuss common measures of gauge capability as well as established confidence intervals for them, based on the modified large sample and generalized confidence interval methods.
Nonparametric Methods for Analysis of Repair Data
Authors: Hollander Myles , Samaniego Francisco J. , Sethuraman Jayaram 
Published: September 2014

Abstract

Repair models specify the joint distribution of the failure times of a system subject to repair. Prominent models include the minimal repair model, the Brown–Proschan model, the Block–Borges–Savits model, the Kijima models, and the general Dorado–Hollander–Sethuraman model. For such models, failure‐time data and data on the types of repairs performed allow inference for the distribution
F
of the time to first failure of the system and other features of the system that can be defined in terms of
F
. This article surveys some nonparametric inference procedures for repair data, including frequentist and Bayesian estimation of
F
, simultaneous confidence bands for
F
, two‐sample tests, tests of the minimal repair assumption, and goodness‐of‐fit tests.
Analysis of Recurrent Events from Repairable Systems
Authors: N. Agustin Ma. Zenia , Agustin Marcus A. , Peña Edsel A. 
Published: September 2014

AbstractThis article provides a review of recently developed dynamic stochastic models for recurrent events, in particular, recurrent events arising from monitoring of repairable systems. Inference methods such as the estimation of model parameters and goodness‐of‐fit testing are also discussed. The procedures described are demonstrated using a reliability data from load–haul–dump machines.