bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural Reliability
Authors: Stewart Mark G.
Published: September 2014

Structural systems such as buildings, bridges, water tanks, containment vessels, and communication towers are often represented as load‐resistance systems. In all cases, these structures represent substantial and expensive assets that are unique or “one‐off”. Even apparently, similar buildings have different occupancy loads, foundations, connection details, etc. Clearly, their reliability cannot be directly inferred from observation of failures or other experimental studies. In these circumstances, reliabilities need to be predicted from predictive models and probabilistic methods. As such, there is a clear recognition that uncertainty and variability are associated with many variables describing a structure's performance and that this can be accounted for explicitly by the use of probability distributions and structural reliability theory. Structural reliability theory allows the probability of failure to be predicted where failure is defined to occur when load exceeds a resistance. Not surprisingly, structural reliability theory underpins many recent advances in structural and safety engineering, including development of design and assessment codes, predicting service‐life performance, and assessment and maintenance of aging and deteriorating structures.
Failure Modes and Effects Analysis, applications
Authors: Price Chris
Published: September 2014

Failure mode and effects analysis (FMEA) is a safety analysis method that evaluates the effects of lower level failures on an overall system. The output of performing an FMEA is often ranked by significance, enabling engineers to focus on the failures with the most important effects. FMEA has its origins in the automotive and aerospace industries, but is now being applied in a much broader range of domains, from medical systems to semiconductor manufacturing.
Intensity Modeling: TheCox Process
Authors: Lando David
Published: September 2014

We define the Cox process as a randomly time‐changed Poisson process and outline the most important specifications and some simple properties. An example from credit risk modeling is given.
Data Fusion
Authors: Braverman Amy J.
Published: September 2014

We define data fusion as the process of combining statistically heterogeneous samples from marginal distributions to construct a new sample that can be regarded as having come from the unobserved joint distribution of interest. This problem has been addressed in the literature on statistical matching, which also appeals to the missing data paradigm of Little and Rubin. Though less obvious, spatial statistics also offers techniques that facilitate rigorous data fusion by inferring unobserved values in multiple data sets at specific locations. We touch just briefly on the topic of copulas. Copulas have yet to be exploited in this context, but the link is clear given our definition of data fusion. Finally, we consider some challenges posed by data fusion, and maybe more importantly by the absence of data fusion.
Sampling and Inspection for Monitoring Threats to Homeland Security
Authors: Thompson Colin , Fox David R.
Published: September 2014

In this paper, we describe a simple mathematical model for identifying an appropriate sample size for surveillance programs for monitoring threats to homeland security associated with “objects” passing an inspection point—typically a port of entry.
Randomized Controlled Trials
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana
Published: September 2014

In the medical sciences, the ideal experiment is the randomized controlled clinical trial (RCT) in which the subject or patient is randomized to treatment with an experimental agent or control (either placebo or other active treatment), in a double‐blind manner. The RCT is the “gold standard” in medical research to establish the efficacy of a new treatment or intervention. The benefits of such an experiment are that any observed differences between the two groups (or more groups) with respect to the outcome can be attributed to the treatment and are without bias. That is, confounding is controlled and selection bias is removed since neither the subject nor the investigator chooses the intervention.

In this entry, we review the issues in the design, conduct, analysis, and interpretation of a randomized clinical trial.
Clinical Dose‐Response Assessment
Authors: Burman Carl‐Fredrik
Published: September 2014

Essentially all pharmacologically active drugs give adverse effects at high enough doses. On the other hand, the intended effect is generally increasing with dose. A critical question is, therefore, which dose should be chosen, that is, which dose minimizes the total risk associated with both insufficient therapeutic effect and side effects. This article briefly describes the estimation and weighting of different risk components, as well as the design of dose‐finding trials.
Kriging
Authors: Switzer Paul
Published: September 2014

Kriging is a method for spatial interpolation based on correlation modeling between observations. This article describes how correlation is used to assign weights to control data for interpolations, and how correlation is used to assess the uncertainty of interpolation. Additionally, the article describes how auxiliary and prior information are used in kriging interpolation and how such information constrains the weights assigned to the control data. Finally, the article describes the impact of measurement error and short‐scale variability on interpolated values and on the interpolation uncertainty. An illustration indicates how control data are weighted differently according to their relation to the interpolation location and how interpolation uncertainty would change with increasing density of the control data.
Public Health Surveillance
Authors: Stoto Michael A.
Published: September 2014

Public health surveillance seeks to assess and track the health of the public, define public health priorities, evaluate programs, and stimulate research. It includes case surveillance focuses on individuals, or sometimes groups of individuals, to identify individuals with certain diseases and take action to stop spread of disease. Statistical surveillance, on the other hand, focuses on populations to identify differentials and trends that can inform public health policymaking, including the allocation of resources. Surveillance systems rely on a diversity of data sources. Surveillance of infectious disease relies primarily on the system of notifiable diseases put in place in the nineteenth century. Statistical surveillance systems use data from vital statistics, population‐based sample surveys, registries, and health care records.

The introduction and growth of syndromic surveillance following 9/11 has focused on a statistical approach to detecting bioterrorist attacks. Despite many accomplishments, the value of syndromic surveillance for detecting bioterrorist attacks has not yet been demonstrated. Ultimately, the most important contribution of syndromic surveillance systems to public health may be for natural disease outbreaks, such as seasonal and pandemic influenza, and for other public health purposes beyond simple detection.
Case‐Control Studies: Overview
Authors: Rothman Kenneth J. , Greenland Sander , Lash Timothy L.
Published: September 2014

In a cohort study, the numerator and denominator of each disease frequency (incidence proportion, incidence rate, or incidence odds) are measured, which requires enumerating the entire population and keeping it under surveillance. A case‐control study observes the population more efficiently by using a sample of the population, which becomes the control series, in place of complete assessment of the denominators of the disease frequencies. This extra sampling step can make a case‐control study much more efficient than a cohort study of the same population, but it introduces a number of subtleties and avenues for bias that are absent in typical cohort studies. For diseases that are sufficiently rare, cohort studies become impractical, and case‐control studies become the only useful alternative.

The cases in a case‐control study should be the same people who would be considered cases in a cohort study of the same population. The definition of the source population for these cases determines the population from which controls should be sampled. Ideally, control selection would involve direct sampling from the source population, with equal probability of selection, although in many circumstances, variations in and substitutes for this ideal sampling design may be considered.
Lévy Processes in Asset Pricing
Authors: Kou Steven
Published: September 2014

After mentioning empirical motivation of Lévy processes in asset pricing, an overview of properties related to Lévy processes is given. Certain difficulties in applying Lévy processes, such as the volatility clustering effect and those in distinguishing the tail behavior of asset returns, are also discussed.
Model Risk
Authors: Crouhy Michel , Galai Dan , Mark Robert
Published: September 2014

Models are an inevitable feature of modern finance, and model risk is inherent in the use of models. In this article we stress the technical elements of model risk. Models are susceptible to errors: from incorrect assumptions about price dynamics and market interactions, from implementing a model wrongly, from inaccurate estimation of volatilities and correlations and other inputs that are not directly observable and must be forecasted. We also discuss how to mitigate model risk.
Volatility Modeling
Authors: Andersen Torben G.
Published: September 2014

This chapter discusses the main approaches to return volatility modeling based on historical time series observations on the underlying price. It starts from simple filtering estimates including historical volatility measures and exponential smoothers used by Risk Metrics, then progresses through ARCH and more general stochastic volatility modeling procedures and ends with the recent development of realized volatility measurement, modeling and forecasting based on the increasing availability of high‐frequency intraday transaction and quote data from financial markets.
Volatility Smile
Authors: Duan Jin‐Chuan , Li Yun
Published: September 2014

Although the Black–Scholes model lays the foundation to the modern option pricing theory, its empirical performance is rather unsatisfactory as manifested in the phenomenon known as
volatility smile
. This article explains what volatility smile is, and describes some nonparametric techniques and parametric models that may be used to deal with volatility smile for pricing and/or hedging purposes. Kernel regression and the GARCH option pricing model are chosen to be the nonparametric technique and parametric model, respectively, in our demonstration involving a sample of S&P 500 index options. Kernel regression is found to perform better than the GARCH model, but its applicability is limited to pricing the same type of options. Without providing a dynamic for the underlying asset price, kernel regression is also ill‐suited for hedging purposes. In contrast, the GARCH model is not subject to the same application limitations even though its performance on the same type of options is poorer. Thus, both nonparametric techniques and parametric models serve useful purposes in dealing with derivative contracts.
Weather Derivatives
Authors: Chan Ngai H. , Wong Chun Y.
Published: September 2014

The weather derivatives market has now become one of the fastest growing markets. This article provides some historical background of weather derivatives and presents the difference between weather derivatives and “standard” derivatives. We give a brief review of existing pricing methods.
Structural Models of Corporate Credit Risk
Authors: Wong Hoi Y.
Published: September 2014

Credit risk is related to the ability of a corporation or an individual to honor a financial obligation. A typical example is the default risk embedded in corporate bond markets. Structural models of credit risk are specifically designed to describe corporate credit risk using the capital structure of a firm. The capital structure comprises the firm's underlying assets, equities, and debts. A comprehensive approach even takes into account the optimal allocation of the firm's assets, the dividend policy of the management, and protective covenants of bond holders. The Moody's KMV, a corporation owned by the Moody's Investor Services, is the first rating agency to put a structural model into commercial use since the early 1990s. Popular structural models include, but are not limited to, the Merton model, the Black–Cox model, the Leland–Toft model, and the KMV approach. In this article, a brief overview of the formulation of a structural approach, specifications within the aforementioned models, and the implementation issues is given.
Statistical Arbitrage
Authors: Lehoczky John P. , Schervish Mark J.
Published: September 2014

This article gives historical background on the emergence of statistical arbitrage, and then describes some of the advances and tools in the area. These include pairs trading, cointegration, value strategies, contrarian strategies, momentum strategies, volatility trading, evaluation of strategies, and technical analysis.
Dynamic Financial Analysis
Authors: D'Arcy Stephen P.
Published: September 2014

Dynamic financial analysis (DFA) is an approach used by organizations to model financial results of the entity under a variety of conditions. The modeling techniques embedded in DFA developed as advances in the computational power of business computers made running large numbers of complex simulations feasible. The models have gradually become more complex, more realistic, and more widely applicable. Companies, rating agencies, and regulators now routinely use DFA type models to evaluate the financial condition of an organization and assess operational changes. This article describes this approach, compares simulation with other financial planning methods, and provides the details of a publicly accessible DFA model that has been extensively used in research and education. DFA is most widely used by insurance companies and other financial institutions, but many nonfinancial firms utilize similar techniques.
Environmental Health Risk Assessment, Statistics in
Authors: Stern Alan H.
Published: September 2014

Quantitative health risk assessment is a tool for making predictions about the impact of substances in the environment on health. Despite the use of scientific principles and a reliance on the most appropriate data from the scientific literature, risk assessment, in the strict sense of the word, is not, by itself, a science. Two different forms of quantitative risk assessment are practiced. One form can be referred to assafety assessment, which seeks to identify the largest level of exposure or dose that can be reasonably expected to be without adverse effect. The other form seeks to quantify the risk associated with any exposure or dose of an environmental agent. In the United States, this form of risk assessment is generally applied to carcinogens. The classical structure of formal environmental health risk assessments comprises of four interdependent steps: hazard identification; dose–response assessment; exposure assessment; and risk characterization. Recent advances in conducting environmental health risk assessments include the use of the benchmark dose approach in lieu of the no observed adverse effect level (NOAEL)/lowest observed adverse effect level (LOAEL) determination, the use of probabilistic analysis in place of single value estimates for risk assessment variables, and progress toward harmonization of cancer and noncancer endpoints.
Role of Risk Communication in a Comprehensive Risk Management Approach
Authors: Jardine Cynthia G.
Published: September 2014

Risk communication is now seen as a reciprocal exchange of information between all interested and affected parties that serves to inform them about the risk issue. It is also generally acknowledged that successful risk management requires the incorporation of risk communication as an integral component of the risk process, from the critical stage of problem formulation through to the final selection of risk‐management options. However, while this knowledge is becoming more generally accepted, it is not being consistently employed in practice.
Evaluation of Risk Communication Efforts
Authors: Jardine Cynthia G.
Published: September 2014

Evaluation is a critical component of risk communication. However, it is frequently overlooked when planning and implementing risk communication efforts. The many compelling reasons for conducting evaluation and the various types of evaluation are explored. Some key factors required for successful evaluation are also discussed. Finally, some recognized barriers to conducting evaluation are presented. Evaluation needs to be considered as a required component of any risk communication program, or we doom ourselves to forever repeating the same mistakes instead of building on our successes.
Laplace Approximations inBayesian Lifetime Analysis
Authors: Onar Arzu
Published: September 2014

We provide some background on Laplace approximations based on the work by Tierney and Kadane and illustrate the method's use in obtaining posterior distributions as well as the associated parameter estimates in Bayesian lifetime analysis. The advantages and the disadvantages of using this approach compared to other numerical alternatives are also discussed. Finally, a few examples from the relevant literature where the approximation has been successfully employed are provided.
M
onte
C
arlo Methods, Univariate and Multivariate
Authors: Walsh Cathal D.
Published: September 2014

With the prevalence of high‐powered computing, Monte Carlo methods are increasingly used in a wide range of disciplines. In this article we review some of the principles behind these methods, note the relationship with other techniques used in systems reliability, and demonstrate some methods in practice. In particular, the accept–reject algorithm, the importance sampler, the method of compositions and the specific case of Box–Muller transformation are highlighted here.
Life Distributions, Simulation of
Authors: Marseguerra Marzio , Zio Enrico
Published: September 2014

This chapter introduces the principles underlying the simulation of the probability distributions that typically characterize the life or failure processes of engineered components and systems. The basic procedures for sampling random numbers are reviewed and examples are provided with reference to the uniform, exponential, and Weibull distributions, which are among the most commonly used probability distributions in reliability, availability, and safety analysis.
Normality Tests: Comparisons
Authors: Koning Alex J.
Published: September 2014

For testing normality with unknown parameters μ and σ
2
, extensive simulation studies have shown the superiority of the Shapiro–Wilk test and variants. The Shapiro–Francia test is a simplified version of the Shapiro–Wilk test, which uses the squared normal probability plot correlation as a test statistic. The Cramér–von Mises and Anderson–Darling tests with plug‐in estimators for μ and σ
2
perform slightly worse than the Shapiro–Wilk test.
Sum of Squares
Authors: Bucchianico Alessandro Di
Published: September 2014

We describe the notion of sums of squares, which it the leading idea behind analysis of variance. We special attention to the case of unbalanced designs, where different types of sums of squares can be used. We outline the appropriate use of these sums of squares.
Nonparametric Tests
Authors: Koning Alex J.
Published: September 2014

Nonparametric tests do not rely on restrictive distributional assumptions, and hence are also known as
distribution‐free tests
. Examples are permutation methods, rank tests, and bootstrap tests.
Probability Plots: Introduction and Examples
Authors: Howell David C.
Published: September 2014

Probability plots are most often used to assess the shape of a distribution of data. Probability–probability plots display the cumulative percentage of the distribution against the cumulative percentages of a reference distribution (often the normal distribution). Quantile–quantile plots display the obtained and expected quantiles of the distribution.
Failure Modes and Effects Analysis, Implementation of
Authors: Bowles John B.
Published: September 2014

Failure modes and effects analysis (FMEA) is potentially one of the most beneficial and productive tasks in a well structured reliability program. FMEA consists of examining the modes and causes of potential item failures and determining the product response to the failure. Steps can then be taken to change the design to eliminate the failure, mitigate its effects, or develop compensating provisions if the failure should occur. FMEA can be applied to hardware, software, material, and process related causes of failure. Functional, interface, and detailed FMEAs can be usefully employed throughout the design process from the concept stage to production and deployment.
Lifetime Distributions, Optimization Methods for
Authors: Hayes Kevin , Barry Don
Published: September 2014

This article introduces the likelihood function as the main tool for parameter estimation and statistical inference with lifetime distributions. When the likelihood function cannot be maximized directly, numerical optimization is required. Approaches based on the Newton–Raphson algorithm are discussed and modifications used to ensure properties such as convergence to maxima rather than minima or saddle points, calculation of nonnegative parameter estimates, and invertibility of the Fisher information matrix are considered. The variability of parameter estimates, generalized linear models, and stochastic search routines are also briefly mentioned.
Availability Models
Authors: Aven Terje
Published: September 2014

Consider a system which is repaired or replaced at failures. The problem is to analyze the availability of the system at a fixed point in time or for an interval [u,v]. This article presents models and methods for analyzing the availability, both for one‐unit systems and for more complex systems comprising several components (monotone systems). Exact formulae as well as approximation approaches based on asymptotic results are covered.
Subjective Life Models
Authors: Mendel Max
Published: September 2014

This chapter deals with several subjective life models that are derived from first principles. First, the ingredients of these models including the hazard and its differential, maintenance policies, aging rates, and failure rates are rigorously defined from a decision‐theoretic perspective. Then the concepts of no‐aging and aging are defined and the corresponding statistical models are derived. Also, the circumstances under which these models converge to form commonly used statistical models are shown.
Characterization in Reliability
Authors: Klebanov Lev B.
Published: September 2014

We describe the problems of characterization of probability distributions arising in reliability theory. These characterizations are mostly based on the property of aging of technical devices or systems. Among them are the classical lack of memory property and its generalizations, functioning properties of redundant systems, relations between the reliabilities of two systems, and reliability of hierarchical structures. Typical results on the characterizations and their stability inside some popular classes of distributions with the special character of the hazard function are also given.
Precedence Tests
Authors: Balakrishnan Narayanaswamy , Tony Ng Hon Keung
Published: September 2014

A nonparametric two‐sample life test based on the order of early Failures–precedence test–is discussed. First, the basic idea of precedence‐type life‐testing procedures and some related works in the literature are reviewed. Then, some efficient alternatives for the precedence test are discussed which include the maximal precedence test, Wilcoxon‐type rank‐sum precedence tests, and the weighted precedence and maximal precedence tests with an extension to progressively censored data. Exact powers of these test procedures under the Lehmann alternative are discussed and evaluated numerically. Finally, applications of precedence‐type statistics to multisample selection problems are briefly mentioned.
Optimal Reliability Design‐Algorithms and Comparisons
Authors: Kuo Way , Wan Rui
Published: September 2014

New developments in optimization techniques are emphasized in this paper. Specially, the methods of ant colony optimization and hybrid optimization deserve more attention in the literature. We suggest that those interested in the field read this paper in conjunction with
Optimal Reliability Design‐Modeling
.
Missing Data and Imputation
Authors: Fuchs Camil , Kenett Ron S.
Published: September 2014

Handling missing data is a core requirement of data analysis. The assessment of the process that caused missing data and the approach for handling missing data can have significant impact on the results of the analysis. In this section, we address the issue of missing data and imputation, covering a wide variety of applications and solutions proposed in the recent literature.
Design and Testing of Questionnaires
Authors: Ograjenšek Irena
Published: September 2014

The questionnaire is the most frequently used data collection tool of survey research. It can be applied in personal, telephone, mail, and on‐line surveys. Two factors which determine the questionnaire design requirements to the largest extent are: the form of the questionnaire (paper
versus
electronic), and the mode of questionnaire administration (interviewer‐
versus
self‐administered questionnaire).

In order to test and evaluate a questionnaire, a wide range of methods can be used. Some (e.g., an expert panel or peer evaluation) are more applicable in the early stages of questionnaire design, other (e.g., a pilot study) should be used prior to the broad questionnaire distribution.
SERVQUAL
Surveys
Authors: Ograjenšek Irena
Published: September 2014

Service quality is often analyzed using rating scales to assess individual service attributes. The most widely used and most carefully scrutinized rating scale is SERVQUAL. The scale is a result of a systematic ongoing study of service quality that begun in 1983. The model defines quality as the difference between customer perceptions and expectations with regard to quality of delivered service. The respondents are asked to answer two sets of questions dealing with the same subject, one set at a general level, and one for a company in question. Respondents choose from a modified Likert scale which measures intensity of agreement or disagreement with any given statement. Response options range from “strongly agree” and lesser levels of agreement to “neutral” (neither agree nor disagree) and lesser levels of disagreement to “strongly disagree”. Although SERVQUAL has been widely used in business‐to‐business and business‐to‐customer settings, this does not mean the scale has not been subjected to reexamination and criticism. There are several well researched and documented objections to SERVQUAL. As a consequence of these critique, various alternative formulations of the service quality model have been suggested. The only one that, to date, seems to outperfom SERVQUAL is SERVPERF, the performance‐only measure of service quality.
Clinical Behavior Change
Authors: der Weijden Trudy van , Wensing Michel
Published: September 2014

Many patients do not receive optimal care. Clinical behavior change is directed at improvements in patient care. A systematic approach to clinical behavior change is promoted. Research on clinical behavior change is the scientific study of methods to promote the systematic uptake of clinical research findings into routine practice. Both quantitative and qualitative approaches in data collection can contribute to the development and evaluation of interventions targeted at clinical behavior change. This chapter focuses on the collection and analysis of quantitative data to answer questions such as “What is current practice?” and “What is the effect of an implementation strategy?” There are several specific statistical issues in this research area, such as the validity of measurements of clinical behavior, interdoctor variation, and clustering effects affecting power calculation and level of analysis. These issues are discussed in this chapter, and illustrated with examples from a study describing the changing test‐ordering behavior of general practitioners.
Quality Management, Overview
Authors: Watson Gregory H.
Published: September 2014

Quality management involves assessing, planning, improving, and controlling work that people accomplish to achieve their shared objective of service to their customers. Quality management requires attention to detail at each level of the organization: leaders must focus on concerns of shareholders and the community; managers must focus on commercial objectives for their targeted markets; and operational teams must focus on the daily work routine of the organization. Each level is responsible for contributing a unique element of the overall quality program. Quality management delivers a consistent system of working that establishes criteria for challenging aspirations for the desirable behavior of the organization, plans improvement objectives based on gaps in performance between the desired state of performance and the current observations of performance, improves work by executing projects that change the way that the routine work processes perform, and defines standards of performance for continuing control of these routine work processes. The components of a quality management system are usually derived from the Malcolm Baldrige National Quality Award criteria for business excellence, external benchmarking studies, total quality management, Six Sigma, and lean production methods, and then these components are packaged into a structure that adheres to the ISO 9000 standard for a quality management system (or other appropriate quality system structure).
Benchmarking in Project Definition
Authors: Watson Gregory H.
Published: September 2014

Benchmarking is a process of comparing performance between two or more organizations about a product, process or service that they have in common. The benchmarking objective is to identify a process that has superior performance and to determine the reasons behind that exceptional performance. Benchmarking studies may be conducted at either the strategic, or operational organizational level and they may compare competitive organizations, internal divisions or processes, industry‐wide practices, or generic business practices in cross‐industry studies. Benchmarking follows a four‐step process: plan‐collect‐analyze‐improve. In the planning step the objective of the study is defined and a literature search is made to determine what information is available from the public domain. Companies are targeted as partners in the study and a preliminary questionnaire is developed in order to solicit their participation. In the second step of the study surveys are conducted, site visits are accomplished, and interviews are used as part of the data collection process. In the analyze step, statistical and practical comparisons are made and best practices are identified among the participating organizations. In the final step, potential enablers of improvement are culturally adapted to each organization and they are implemented into the daily management system.
Total Quality Management: Overview
Authors: Stephens Ken
Published: September 2014

The philosophies of the quality discipline have been and are being established over a large period of our recent history by many contributors. How the profession defines “quality” has much to contribute to its fundamental philosophies. A comprehensive definition, suitable to the broad range of quality's involvement, is used in this paper to expose many of its philosophies.
Theory of Constraints
Authors: Mordoch Avraham
Published: September 2014

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

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.
Multi‐Vari Chart
Authors: Mast Jeroen
Published: September 2014

The multi‐vari chart is used to identify and display sources of variation in processes. It is a cousin of the control chart.
Process Reengineering
Authors: Pall Gabriel A.
Published: September 2014

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

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

Statistical 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

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

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

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

Standard 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

This 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

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

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

In 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

Latin 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

The 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

In 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.
Randomization in Experimental Designs
Authors: Kunert Joachim
Published: September 2014

The article describes the principal ideas of randomization. It points out that randomization can be used to validate a model for analysis.
Screening Designs
Authors: Jacroux Mike
Published: September 2014

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

In 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

Choosing 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.