bluuebunny/crossref_metadata_2025_split huggingface dataset:
Organizational Assessment Models
Authors: Conti Tito A. 
Published: September 2014

Abstract

The paper discusses the origin, development, and foreseeable trends in the area of organizational assessment models. The origin is traced back to the 1930s/1950s, when the problem of assuring quality in new product development became a competitive issue. “Quality assurance (QA) standards” emerged in the 1950s/1960s, together with methods to check compliance of the organization with such standards. Methods took the form of independent assessments, aimed at checking the capability of the product generating system (first‐party audits). The opportunity to assess supplier capabilities in advance soon led to the extension of the standards/assessment approach to B2B/B2A relations.
External
QA standards were then conceived, as the basis for assessing customer capabilities through “second‐party” audits. The evolution from proprietary external QA standards and second‐party audits to sector, national, and international standards and third‐party audits is described, with a particular emphasis on ISO 9000 standards and certification. Discontinuity in assessment models took place in the 1980s, with the outbreak of the new quality based competition and the advent of the so‐called total quality management (TQM) models. The paper examines the different types of TQM models in use today, from the quality award models to those focused on the optimization of performance measurement, and the “diagnostic models” for organizational improvement. Finally, trends in organizational models are discussed: first the evolution from organizational assessment to organizational improvement models; second the injection of more systems thinking into the models.
Policy Deployment for Performance Improvement
Authors: Watson Gregory H. 
Published: September 2014

Abstract

Policy deployment is a Japanese‐originated management process that applies the Deming cycle of plan‐do‐check‐act (PDCA) to the processes of planning and execution. Policy deployment has sometimes been called
management by objectives
(MBO)
on steroids
or
Turbo‐MBO
. A policy deployment process coordinates the definition and implementation of strategically important business‐process‐change projects to improve the routine operating processes of an organization. Policy deployment aligns and coordinates the activities of the organization through a number of mechanisms that open lines of communication across the organization in cross‐functional and cross‐layer dialogs. These communication mechanisms solicit ideas for making business improvements, define projects and objectives through an interactive “catchball process”, and review progress through cross‐layer improvement project assessments. This in‐depth involvement of the entire organization in both the planning and execution processes creates acceptance for change through the active participation of an engaged workforce. Synonyms for policy deployment include
hoshin kanri
(the original Japanese name) as well as policy management and management by policy (MBP).
Quality Control, Computing in
Authors: Noguera John 
Published: September 2014

AbstractThe article provides a brief survey of important advances in quality control tools that are (or should be) available to practitioners using current statistical quality software tools. The tools discussed include the use of the noncentral distribution function for power and sample size, confidence intervals in measurement systems analysis, dealing with nonnormality and autocorrelation in statistical process control, and the use of genetic algorithms in the design and analysis of experiments.
Neural Networks in Statistical Process Control
Authors: Hwarng H. Brian 
Published: September 2014

AbstractThe tremendous interest and wide applicability of neural computing theories and technology motivate researchers to explore how this class of modeling tools can be effectively applied to statistical process control (SPC). The basic principle of SPC is to signal an out‐of‐control state when the behavior of critical quality variables in a process is considered statistically abnormal, but also to leave the process intact when no evidence of statistical abnormality is present. Two primary categories of problems are the detection of shifts and the recognition of unnatural patterns from the process monitored. The article focuses on models appropriate in the SPC context and the general modeling approach employing various neural network algorithms. We illustrate the use of the approach with data generated from a bivariate autocorrelated process. While the neural network approach is flexible, adaptable, and capable of learning, it is limited by a lack of analytical robustness and the need for intensive data. Nevertheless, the approach enhances process‐troubleshooting capabilities and serves as a powerful alternative to the traditional SPC approaches in handling data collected from a dynamic and complex environment.
Monitoring of Safety in
EU
Railways
Authors: Pira Angelo , Piazza Roberto , Rajan Jane 
Published: September 2014

Abstract
According to the legislative framework of the European Union (EU) in the field of railway safety, information on safety performance is mainly collected to provide input to the development of an objective tool for setting common safety targets (CSTs) and assessing their achievement. CSTs will be developed in subsequent sets, on the basis of safety performances in member states (MSs) and their possible economic impact in terms of costs and benefits. European Railway Agency (ERA) is in charge of these projects, which are carried out in conjunction with industry and MSs in working groups of experts. The flow of information and data exchange on safety issues between the concerned parties is completed by the activity of accident investigation, carried out by National Investigation Bodies and communicated to ERA; this aims to better understand the causes of accidents, which, together with statistics on accidents, precursors to accidents, and economic aspects of safety, provides the necessary information to ERA for setting CSTs at EU level.
Statistical Quality Control
Authors: Parr William C. 
Published: September 2014

Abstract
This article discusses statistical quality control. Statistical quality control dates from the 1920s. Shewhart effectively launched the field of statistical quality control with a memorandum issued on May 16, 1924 featuring the first control chart. These ideas and others have been developed over many years to provide a reasonably complete and versatile set of statistical tools for studying, monitoring and improving processes using a wide variety of statistical methods.
Quality Function Deployment
Authors: El‐Haik Basem 
Published: September 2014

Abstract
Quality Function Deployment (QFD) is a structured approach to defining customer needs or requirements and translating them into specific plans to produce products to meet those needs. The “voice of the customer” is the term commonly used to describe these stated and unstated customer needs, delights and wants. This understanding of the customer needs is then summarized in a planning matrix called house of quality (HOQ). These matrices are used to translate higher level “whats” or needs into lower level “hows”—functional requirements, design parameters and process variables over four phases to satisfy the voice of the customer (VOC). QFD is methodology that broadens or extends quality responsibility throughout a product, service or process development.
Control Charts for the Standard Deviation
Authors: Mitra Amitava 
Published: September 2014

Abstract
Control charts are graphical tools to monitor the activity of a process. They are used to determine if the variation in a process is due to the presence of common causes (those that are inherent to the process) or special causes (those that are not part of the process as designed). A process is said to be in statistical control when it is under a system of common causes. A standard deviation control chart monitors process variation, as measured by the standard deviation of observations, through subgroups collected over a period of time. Typically, three lines are depicted on a control chart—the centerline (CL), the upper control limit (UCL), and the lower control limit (LCL). Rules for determining out‐of‐control conditions are defined, the most commonly used one corresponds to a point falling outside the control limits. Some parameters for the construction of the standard deviation control chart are the subgroup size, frequency of sampling, and placement of the control limits from the CL, based on the choice of an acceptable level of a false alarm rate.
Autocorrelated Data
Authors: Wardell Don G. 
Published: September 2014

Abstract
Traditional statistical process control methods assume that consecutive observations on a control chart are statistically independent. Often observations from actual processes are serially correlated. In such cases, the use of traditional control charts often leads to misleading conclusions as to whether or not the process is under control. We illustrate the problems of using traditional control charts and review several methods that have been proposed to handle the autocorrelation. Some of the methods are model based while others are model free. The majority of the methods are improvements over traditional control charts, but none is able to completely overcome the inherent difficulty of monitoring autocorrelated data. Hence there are some cases where traditional charts, which are simpler to implement, should be considered even when the process is autocorrelated.
Profile Monitoring
Authors: Woodall William H. 
Published: September 2014

Abstract
In many practical situations the quality of a process or product can be characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is used to understand and check the stability of this relationship over time. At each sampling stage one observes a collection of data points that can be represented by a curve (or profile). In some calibration applications, the profile can be represented adequately by a simple straight‐line model, while in other applications more complicated models are needed. In this article, we discuss some of the general issues involved in using control charts to monitor such process and product quality profiles.
Proficiency Test Data, Population Characteristics of
Authors: Wells David E. , Cofino Wim P. 
Published: September 2014

Abstract
Data from the measurement of silicate, acenaphthylene, and DDT in the marine environment are used to examine the Cofino model. This is a model for the inference of population characteristics from experimental data using uncertainties. It uses mathematical principles taken from quantum mechanics and provides an alternative to robust statistics for obtaining the population characteristics of proficiency test (PT) data. The Cofino model identifies clusters of values within a dataset that exhibit a high level of agreement and for each cluster calculates the mean, variance, and the percentage of the whole dataset. The model uses all data, including left‐censored values, and can cope with bi‐ or multimodal distributions, skewed, or tailing data. The model is based on a normal distribution when applied to PT data, but the principles it uses can be applied to other distributions and data sources.
Blocking
Authors: Gilmour Steven G. 
Published: September 2014

AbstractWhen an experiment is designed,blockinginvolves grouping the runs together in sets which are expected to give similar responses. It is used to improve the estimation of the treatment effects of interest. Complete blocks can be used if the number of runs in each block is equal to the number of treatments. Otherwise incomplete blocks are needed. Different methods of choosing incomplete blocks are used depending on whether the treatments are simple, discrete categories or combinations of levels of several factors. Extensions include resolved block designs and row–column designs, such as Latin squares.
Fractional Factorial Designs, issues in
Authors: Voelkel Joseph G. 
Published: September 2014

Abstract

If only a fraction of all the treatment combinations of a full factorial design are selected to be run, the resulting design is called a
fractional factorial design
. By carefully deciding which fraction of the treatment combinations to run, it is possible to construct fractional factorial designs with good properties. We examine these designs in detail for designs in which each factor is studied at two levels. We show how to construct these designs and how to find their properties. We discuss some uses of these designs as well as their advantages and disadvantages. An example is given of a design and its analysis. Extensions are considered to other situations, including three‐level designs, blocked designs, and split‐plot designs.
Half‐normal plot ‐ Some examples
Authors: Taam Winson 
Published: September 2014

Abstract
A half‐normal plot is a graphical tool to discern active contributors from inert ones in a factorial experiment. It is designed for two‐level factorial experiments but can be adapted for experiments with other levels. Plotting absolute effect estimates and their corresponding half‐normal scores provides a graphical display that compares the relative strength of various effects in the experiment. Its ease of use, visual appeal, and simplicity are reasons for its popularity.
Interactions with examples
Authors: Reese C. Shane 
Published: September 2014

Abstract
Statistical approaches for assessing the effect of a set of explanatory variables on a response variable often involve estimation of joint or interaction effects. This article provides a historical perspective on interaction effects from both an effects model and a cell means model perspective. Factorial designs are presented as an effective means of conducting studies where interaction effects are estimable. The assessment of presence or absence of interaction effects is demonstrated with both statistical hypothesis testing and graphical techniques. An example from the commercial automotive industry provides an illustration of interaction effects.
Main Effect Designs
Authors: Jacroux Mike 
Published: September 2014

Abstract
In this article we consider experimental situations where a screening experiment is to be run and primary interest is in the identification of variables which have an influential effect on the process being studied. In such cases, main effects designs are often used. Main effects designs typically allow for the efficient estimation of main effects parameters and are particularly useful when the initial number of experimental variables being studied is large compared to the number of runs in the screening experiment. Here we describe a number of the orthogonal and nonorthogonal main effects designs available to practitioners along with some of their individual advantages and disadvantages.
Projectivity in Experimental Designs
Authors: Tyssedal John 
Published: September 2014

Abstract
Projectivity concerns the properties of a factorial design when it is restricted to a subset of experimental factors. In this article we stress the importance of taking projective properties into account when a screening experiment is performed. We present results that can be used to decide on the projectivity of two‐level designs, show by an example how these properties can be taken advantage of in order to identify active subspaces of factors and give various techniques that can be used to construct designs with good projective properties for various experimental situations.
Box‐Behnken Designs
Authors: Robinson Timothy J. 
Published: September 2014

AbstractDesigns for second‐order response surface models play an important role in response surface methodologies. The central composite design and Box‐Behnken designs have served as popular choices for designs for second‐order models when design economy and precise prediction variance are desired. The Box‐Behnken design has been widely used in completely randomized experiments, split‐plot experiments, and within the robust parameter design setting. This article provides a historical background for the Box‐Behnken design, describes its construction, and reviews various contributions in the literature regarding the use of these designs.
Rotatable Designs and Rotatability
Authors: Draper Norman R. 
Published: September 2014

Abstract

Choosing a suitable experimental design is an important step in the investigations of the factors that may influence one or more response variables. There are several criteria to keep in mind when the choice is made, one of which is
rotatability
. When a design is
rotatable
, it will produce information that predicts ŷ with the same precision at all points equidistant from the coded origin of the design. Another way of saying this is that the contours of
V
(ŷ), the variance of the predicted value, will be spherical about the design origin. In experimental practice,
exact
rotatability is not essential, but the knowledge of how to obtain it is useful in producing approximate rotatability while perhaps attaining other desirable design characteristics.
Performance Measures for Robust Design
Authors: Murphy Terrence E. , Tsui Kwok‐Leung 
Published: September 2014

Abstract

Despite their widespread use as performance measures in robust design, Taguchi's signal‐to‐noise ratios (SNRs) do not always minimize quadratic loss. The relationship between minimization of expected quadratic loss and Taguchi's SNR was articulated by León
et al.
1. Their performance measure independent of adjustment (PerMIA) is a general performance measure which justifies the use of the Taguchi two‐step procedures as a method of minimizing quadratic loss under certain models. We review the basic conceptual framework of the PerMIA and the literature related to this performance measure.
Robust Design
Authors: Ankenman Bruce E. 
Published: September 2014

AbstractRobust design is a methodology that seeks to set the controllable factors of a system in order to achieve a consistent and desirable response from the system. The defining characteristic of robust design is the emphasis on achieving a consistently desirable response, not just a response that is desirable on average. The robust design procedure involves designing, conducting, and analyzing experiments. In this article, robust design methodology is explored for two typical cases. The first case is when the variability in the system is due to replication error. The second is when there are certain factors, callednoise factors, which can be controlled during an experiment, but will vary randomly when the system is put into its standard operating mode. Models for the response, data analysis techniques, and experimental design techniques will all be discussed and demonstrated through a simple manufacturing example.
Signal‐to‐Noise Ratios for Robust Design
Authors: Murphy Terrence E. , Tsui Kwok‐Leung , McShane‐Vaughn Mary 
Published: September 2014

Abstract

Taguchi introduced a collection of performance measures called
signal‐to‐noise ratios
(SNRs) as a core component of the robust design methods he introduced to US corporations in the 1980s. The SNR is the most controversial part of Taguchi's methods because it summarizes the quality information from the entire experiment in a single entity. While the use of a single performance measure provided a conceptual simplicity attractive to engineers, it also introduced certain statistical controversies. We review the strengths and limitations of this performance measure and the evolution of related research. Our discussion includes the use of the SNR for both static and dynamic robust design (
see
Robust Design
).
Disease and Clinical Trial Modeling
Authors: Abbas Ismail , Rovira Joan , Cobo Erik , Romeu Joan , Casanovas Josep 
Published: September 2014

Abstract

Introduction
The objective is to show how disease and clinical trial modeling can be applied to estimate the success probability, duration and cost that resulting from a certain trial design or from similar decisions.

Methods
Multidisplinar team has participated in the construction of the general simulation model of clinical trials. This model is constructed splitting the whole process into two main parts. The first part represents the recruitment sub‐model aiming at estimating the time and the number of included patients. The second part is based on the follow‐up sub‐model represented by disease progression.

Results
The model is applied to retrospective clinical trial.

Conclusion
Simulation modelling will optimise clinical trials whenever information from different disciplines, such as clinics, statistics, economics and informatics is integrated and used properly.
Process Capability Indices, Skewed
Authors: Chang Young Soon , Bai Do Sun 
Published: September 2014

Abstract
Conventional process capability indices (PCIs) are usually determined under the assumption that quality characteristics follow a normal distribution. If a process does not follow a normal distribution, however, conventional PCIs do not provide valid measures of process capability, especially in terms of the number of nonconforming parts. Methods to remedy the shortcoming of the conventional PCIs when the population is skewed can be divided into five categories; to use normalizing transformations like Box–Cox power transformation or Johnson transformation, to fit an empirical distribution or a known three‐ or four‐parameter distribution such as Burr or Pearson distribution to the original data and use the quantiles of the fitted distribution, to modify the standard definition of PCIs in order to increase their robustness to skewness, to construct PCIs with estimate of the process yield, and to develop heuristic methods adequate for skewed distributions. This article provides a compact survey and brief comments on the skewed PCIs.
Process Capability Indices,
B
ayesian Estimation of
Authors: Spiring Fred , Cheng Smiley 
Published: September 2014

Abstract
Practitioners often base inferences regarding the capability of a process on a point estimate of one of the common indices without examining the distributional properties of the estimator used. The practice is due, in part, to (a) the complexity of the indices developed and, at least initially, (b) the lack of stochastic development in the general area of process capability indices. Since the mid‐1980s the body of work addressing (a) estimation of and (b) drawing inference from process capability indices has grown steadily. Investigating process capability from a Bayesian perspective has mirrored this development. Much like the indices themselves, the Bayesian approach has seen two distinct patterns develop. One focus has been on examining parts nonconforming while a second examines the ability of a process to cluster around a target.
Process Capability Plots
Authors: Vännman Kerstin 
Published: September 2014

Abstract
A process is usually defined to be capable if a process capability index exceeds a stated threshold value. This definition can be expressed graphically as a region in the plane, defined by the process parameters, to obtain a process capability plot. Under the assumption of normality, safety regions can be plotted in the process capability plot to draw conclusions about process capability at a given significance level. Alternatively, an estimated process capability plot defined by the estimators of process parameters can be used. This article discusses these different kinds of plots and presents examples and interpretations.