bluuebunny/crossref_metadata_2025 huggingface dataset:
Process Yield
Authors: Lin Pi‐Chuan , Pearn Wen Lea
Published: February 2008

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.
Measurement Systems Analysis, Attribute
Authors: Borror Connie M.
Published: February 2008

Methods for assessing the capability of a quantitative or continuous measurement system are well documented and include the tabular method and the analysis of variance approach. When the measurement system involves attribute data, the standard quantitative methods are not appropriate. An attribute gauge measurement system is one where the parts or objects under study are placed into one of two or more possible categories. The classification of the part is the measurement of interest in an attribute measurement system. Several commonly used statistics for “appraiser agreement” and attribute gauge repeatability and reproducibility are presented.
Measurement Systems Analysis, Overview
Authors: Michelson Diane K.
Published: February 2008

Measurement systems analysis is a way to quantify the variability of a metrology process. The fundamental technique is the gauge
repeatability and reproducibility
(GR&R) study, which yields an estimate of

. The estimate is then used to determine whether or not the metrology system is capable of measuring the process under study. The stability of the measurement system is also determined.
Risk Analysis, Extremes in
Authors: Teugels Jozef L.
Published: February 2008

Over the last 25 years, extreme value theory has become a substantial part of statistical analysis. Often, extremes extract from data essential information that can hardly be retrieved from central tendency measures. We first deal with the main ingredients of classical extreme value theory, indicating its potentials and generalizations.
Fisheries Stock Assessment
Authors: Maxwell David L. , Pilling Graham M.
Published: February 2008

Fishing is an economically and socially important activity with around 90 million tonnes of wild fish caught each year. Fisheries policy tries to balance potentially conflicting management drivers for sustainability and maintenance of ecosystem biodiversity with socioeconomic drivers of profit and employment at a variety of geographic scales. Stock assessment's role is to provide the best possible technical support to this process, estimating current and possible future fishery states and the effects of management decisions. This article summarizes the assessment process from the collection of biological and fisheries data, through the mathematical modeling approaches used, to assessing the uncertainty of the results and the robustness of advice. At each stage we highlight quality assurance issues and provide key references for more detailed study.
Sampling Sediments in the Marine Environment
Authors: Moffat Colin F. , Webster Lynda , Pollard Pat , Ahmed Abdulwaheed S. , Russell Marie , Davies Ian M.
Published: February 2008

A number of studies into sampling regimes for marine sediments are discussed. The use of random sampling, stratified random sampling, and the combining of samples are all investigated in coastal and offshore environments. The resulting data is analyzed using standard statistical methods. Carefully designed sampling regimes, with a sound statistical basis, will result in data which can be used to describe a defined area or else make comparisons of contaminant loadings between different areas, often at a reduced cost.
Healthcare Performance Reports
Authors: Bevan Richard G.
Published: February 2008

This article is about the impacts of reporting healthcare performance within a country and confidential reports to individual clinicians as part of an audit process. It considers three approaches to reporting information: the “soft” approach, which is about quality improvement, focuses on success, and is voluntary; the “hard” approach, which is compulsory and about quality assurance; and the market approach, which relies on corporate or consumers' choices of health plans, and corporate or patients' choices of providers. It considers the impacts of reporting healthcare performance on four different groups: third party payers (either corporately or by consumers), patients, physicians, and providers. The evidence is that although publication of results has an impact, this is on providers and not
via
actions of patients or third party payers. The article suggests criteria that influence this impact and outlines statistical implications.
Disease Mapping
Authors: Lawson Andrew B.
Published: February 2008

This work focuses on the analysis of geo‐referenced health outcome data. The main concern in this analysis is the use of appropriate models to allow the estimation of relative risk within small areas, detection of clustering of unusual risk areas, ecological analysis and disease surveillance. Different data types (case event and count data) and different modeling approaches are considered.
Disease Surveillance
Authors: Lawson Andrew B.
Published: February 2008

This work focuses on disease surveillance and issues related to prospective detection of changes or aberrations in health data. One feature of recent interest in this area is the use of ancillary variables to allow for early detection of outbreaks. For example, gastro‐intestinal disease outbreaks can be detected by examining over‐the‐counter sales of pharmaceutical products. This work is sometimes known as syndromic surveillance. The focus of the detection will define the correct methods used in detection. For example, if a cluster of risk is to be detected then different methods would be used that if a jump in risk over time were to be detected.
Self‐Controlled Case Series Analysis
Authors: Hocine Mounia N. , Whitaker Heather J. , Farrington C. Paddy
Published: February 2008

The case series method was developed by Farrington 1 to investigate the strength of association between a time‐varying exposure and a potentially recurrent adverse event, using cases only. The method has been widely used in pharmacoepidemiology, particularly in the study of vaccine safety. The method is derived from an age‐dependent Poisson model by conditioning on the number of events and on exposure histories. The conditioning leads to estimation within individuals, and thus the effects of fixed covariates cancel out. In some circumstances, the case series method has high efficiency, relative to the cohort method. A semiparametric version of the case series method, in which the underlying age effect is left unspecified, is also available.
Early Detection Programs in Medical Care
Authors: Davidov Ori , Hu Ping
Published: February 2008

Early detection programs are increasingly used to diagnose cancer and other diseases at an early stage. Evaluating their benefit is consequently of major interest. In this chapter we review the main ideas and methods used for assessing whether a screening program is beneficial.
Quality in Critical Care Medicine
Authors: Segal Eran
Published: February 2008

Critical care medicine is a growing element of modern hospital medicine. Critically ill patients consume about of 20% of hospital budgets and are therefore a very expensive part of hospital medicine. The occurrence of errors in ICU practice is determined by many factors including ICU design, workflow parameters, and the type of clinical procedures and patient population. Newer approaches to reducing error and improving expected outcome in the treatment of critically ill patients require monitoring and measurement of process, as well as of specific clinical outcomes such as the incidence of line related infections, ventilator associated pneumonia and also compliance with the tools needed to improve care. A great deal of research is directed at improving clinical care and outcome using measurements and improving treatment accordingly. Some information is available that ICUs which utilize these tools may provide better outcome.
Disease and Clinical Trial Modeling
Authors: Abbas Ismail , Rovira Joan , Cobo Erik , Romeu Joan , Casanovas Josep
Published: February 2008

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.
Statistical Process Control in Clinical Medicine
Authors: Winkel Per , Zhang Nien Fan
Published: February 2008

A control chart may be used to characterize a clinical process and subsequently to monitor it. The chart depicts the values of a measurable quantity characterizing the process, e.g., the fraction of patients dying per month during a specified surgical procedure.

When the results of the process are stable the quality of the process may be assessed, e.g., by estimating the fraction of results that do not have an acceptable quality.

As an example, it is shown how the daily average of patient waiting times at an outpatient clinic is adjusted and subsequently monitored. If the variable one wants to monitor is an outcome measure, risk adjustment is necessary in order to adjust for variation between the patients in terms of severity of disease, comorbidities, age, sex, etc.

The outcome results of several healthcare providers (e.g., hospital departments) may be compared if the patients are assigned at random to the providers. Using simulation it is illustrated how an unknown or unmeasured risk factor may bias the comparison of the quality of care between two healthcare providers if observational data are used. It is recommended that risk‐adjusted comparisons based on observational data are considered inconclusive and used as inspiration for further investigations.
Parallel, Series, and Series–Parallel Systems
Authors: Agustin Marcus A.
Published: February 2008

This article will consider the system reliability of three different systems: series, parallel, and series–parallel systems. Interest is in finding the system reliability when the failure of each component depends on the length of time that the component is observed. Exponentially distributed failure times as well as Weibull failure times will be considered.
k

‐out‐of‐

n

Systems
Authors: Shepherd Deborah K.
Published: February 2008

A
k
‐out‐of‐
n
system is a system with
n
components which functions or works if at least
k
out of the
n
components work. Parallel and series systems are special cases of a
k
‐out‐of‐
n
system. There are many different arrangements of the components in a
k
‐out‐of‐
n
system and many assumptions that can be made about the lifetimes of the components. In this chapter, the lifetimes of the components in the system are assumed to be independent and identically distributed. The reliability function of the system is found by calculating the probability that at least
k
components out of the
n
components work. The mean time to failure of the system is also found for the case of components with exponential distributions of lifetimes. In addition, the reliability function for a linear and circular consecutive
k‐out‐of‐n
system is given.
Component Reliability Importance
Authors: Rigdon Steven E.
Published: February 2008

The concept of reliability importance gives a measure of how much changes in the reliability of a particular component of a coherent system affects the reliability of the system. The definition of the importance of component
k
is
I
(
k
) = ∂
r
(

p

)/∂
p

k

, where

p

is the vector of component reliabilities and
r
(

p

) is the system reliability function. Examples of the reliability importance for common systems are given. The structural reliability importance is also given, along with its relationship to the reliability importance.
Reliability of Redundant Systems
Authors: Pham Hoang
Published: February 2008

This articles provides a brief concept of how to evaluate the reliability of redundant systems with various aspects including dynamic redundancy, imperfect coverage, fail‐safe redundancy, noncoherent systems, degradable redundancy, multiple failure modes,k‐out‐of‐nsystem redundancy, weighted systems, and load‐sharing redundancy.
Reliability Optimization
Authors: Rigdon Steven E.
Published: February 2008

The goal of reliability engineering is to produce systems that have the highest reliability possible. In this article, we consider a number of ways that this optimization can be done, including design for reliability, reliability allocation, redundancy allocation, and system maintenance.
Reliability Databases
Authors: Bedford Tim , Hardman Gavin , Walls Lesley , Quigley John
Published: February 2008

We present an overview of the development of reliability databases and their use in the analysis of failure data. We consider through examples the construction of a reliability database and the implementation of standard methods of analysis. We also discuss common criticisms of the use of reliability databases.
Fault Trees
Authors: Bartlett Lisa M.
Published: February 2008

The ability to analyze how failures occur and determine their likelihood of occurrence is fundamental in maximizing the functionality of a system. To achieve this, a technique such as fault tree analysis is required. The technique can be used to carry out a qualitative analysis, namely, identifying the failure events that contribute to the system failure, and a quantitative analysis, calculating numerical statistics relating to the system, for example, the probability of failure. This article provides an introduction to the technique and its analysis potential.
System Reliability:
M
onte
C
arlo Estimation
Authors: Rigdon Christopher J. , Rigdon Steven E.
Published: February 2008

Monte Carlo methods are useful for a number of situations in reliability. First, for very complex systems, it is difficult to determine the system reliability even if the component reliabilities are known. This can be done by simulating the performance (operating/failed) of each component, and determining the status of the system given the performances of these components. Second, the system reliability can be estimated when component reliabilities are estimated from pass/fail data. Uncertainties in the resulting estimate of system reliability can be obtained
via
Monte Carlo simulation. Third, many questions of availability (or other measures of system performance) can be treated using queuing theory. Since queuing models can become intractable, especially when times between events or repair times are not exponential random variables, Monte Carlo methods can be used.
System Reliability: Computational Algebra Methods
Authors: Leemis Lawrence M.
Published: February 2008

Computer algebra systems are useful in calculating system reliability. We use several diverse examples to illustrate how a computer algebra system can be helpful in determining system reliability. The examples presented here are (a) analyzing a
k
‐out‐of‐
n
system; (b) determining the mean time to failure of a parallel system with symbolic parameters; (c) determining the conditional survivor function for a system with a piecewise survival distribution; (d) determining the probability of mission success for a unit and two cold standby units; (e) determining a lower confidence bound on the system reliability from binary failure data collected on each of the independent components in a coherent system
via
bootstrapping; and (f) determining the exact distribution of a popular goodness‐of‐fit test statistic.
Genetic Algorithms in Reliability
Authors: Coit David W. , Taboada Heidi A.
Published: February 2008

Genetic algorithms (GAs) have been effectively used to solve many difficult engineering problems and are particularly effective for solving combinatorial optimization problems with large, complex search spaces. The redundancy allocation problem (RAP) is perhaps one of the most common problems in design for reliability and it is known to be NP hard. Redundancy is used as a strategy to meet high reliability specifications in many system design engineering projects. In this material, we present an overview of some of the most relevant research that has been done on the use of GAs in the reliability optimization of redundant systems.
Reliability, Safety, and Risk Management
Authors: Cox Sue
Published: February 2008

The aim of this paper is to present a brief exposition on the connection between safety and reliability. The author adopts an approach that is described in full in the bookSafety, Reliability and Risk Management: An Integrated Approach. This book was designed to support students and practitioners in the development of their understanding of safety and its links to reliability. The text is structured around a systems approach to safety, reliability, and risk management and highlights a number of practical tools that practitioners have used to identify and manage risk. This approach is compatible with the current development of risk management theory, both in relation to public decision making and loss prevention strategies.
System Downtime Distributions
Authors: Pievatolo Antonio
Published: February 2008

System downtime distributions are useful for determining economic or safety performance measures of a system. We consider system downtime in an interval and after a failure. We try to give expressions that can be readily programmed using only information on failure and repair time distributions of every single component of the system. For this reason, all such expressions are of asymptotic nature. We deal with systems of independent binary components in detail, whereas we mention only briefly (also indicating references) the tools that can be used for systems with multistate components.
Intensity Functions for Nonhomogeneous Poisson Processes
Authors: McCollin Christopher
Published: February 2008

When analysing reliability data from a repairable system e.g. a car, a fridge, etc, reliability specialists are interested in the full life history of a system so that they may identify problems that have affected its life and apply solutions, if possible, to other new systems so that these new systems do not suffer from the same problems. The basis of reliability analysis is to understand how systems are failing by modeling their life history so that prediction of the likely future reliability of systems is possible. This is the basis of reliability growth (development) testing where systems under development are tested to find latent design and manufacturing defects so that they can be eliminated from the full‐scale production system.

The simplest way of identifying structure in the system's life history is to plot the data. Three plots are commonly used: plotting failures on a time line; plotting the failure number against cumulative time, and plotting an estimate of the intensity, the rate of occurrence of failures (ROCOF) against cumulative time. These plots appear in Ascher and Feingold [31] and Crowderet al. [61] among others. These plots will help to identify structure such as an increase or decrease in the interfailure times of a system over time (more commonly known as reliability growth and reliability decay); evidence of cycles in the data—these may be short or long cycles; periods of high or low reliability (again related to usage); gaps in the time stream (due to change in the reporting mechanism, management, etc) and outliers (possible abnormal operating conditions).

This review lists the intensity functions and their associated expected number of failures, which is the mean value parameter of the nonhomogeneous Poisson process (NHPP). The simplest and best known intensities, those based on mortality rates, those based on hazards of well‐known probability distributions, the software related intensities, and those based on differential equations and extensions are discussed under their appropriate classification headings.
Repairable Systems:
B
ayesian Analysis
Authors: Rigdon Steven E.
Published: February 2008

The Bayesian approach to analyzing data from repairable systems is presented in this chapter. Specifically, the homogeneous Poisson process and the power law process, a nonhomogeneous Poisson process with intensity equal to λ(
t
) = (β/θ)(
t
/θ)
β−1
,
t
 > 0, are studied. Other models, such as the renewal process, follow a similar pattern. Also, measures of system performance, such as availability, are discussed, where the Bayesian approach provides a convenient way to combine uncertainties. For multiple copies of a repairable system, we discuss empirical Bayes and hierarchical Bayes methods.
Flowgraph Models
Authors: Huzurbazar Aparna V.
Published: February 2008

Flowgraph models are used to analyze complex multistate systems. The focus is on modeling data from semi‐Markov processes. Semi‐Markov models play an important role in the analysis of time‐to‐event data. However, in practice, data analysis for semi‐Markov processes can be quite difficult and many simplifying assumptions are made. Flowgraph models are multistate models that provide a data analytic method for semi‐Markov processes. The system data can be both censored and incomplete. Flowgraphs are useful for estimating Bayes predictive densities, predictive reliability functions, and predictive hazard functions for waiting times of interest in the presence of censored and incomplete data.
Queues in Reliability
Authors: Ausín M. Concepción
Published: February 2008

Queuing models can be useful in solving many complex reliability problems. Component failures are usually interpreted as the arrival of customers and the repair or replacement of failed components is typically associated with the service facility. A distinctive characteristic of queues in reliability is that requests for service are usually generated by a finite customer population because, in general, there are a limited number of units, e.g., machines which can fail, and when they are all in the system, being repaired or waiting for repair, no more can arrive. Thus the arrivals do not form a renewal process as they may depend on the number of units in the system. This is an essential difference from typical queuing systems, where the population of potential arrivals can be considered to be effectively limitless. This article gives an overview of the main queuing models used in reliability which are illustrated using the classical machine repairmen model. Some statistical methods to estimate the main quantities of interest in a queue are also discussed.
Reliability Allocation
Authors: Malaiya Yashwant K.
Published: February 2008

A system is generally designed as an assembly of subsystems, each with its own reliability attributes. The overall system reliability is a function of the subsystem reliability metrics. The cost of the system is the sum of the costs for all the subsystems. This article examines possible approaches to allocate the reliability values such that the total cost is minimized. The problem is very general and is applicable for mechanical systems, electrical systems, and computer hardware and software. The problem involves expressing the system reliability in terms of the subsystem reliability values and specifying the cost function for each subsystem, which allows setting up an optimization problem. Software reliability allocation is examined as a detailed example. The article also examines some preliminary apportionment approaches that have been proposed. In some cases, it is possible to use exact optimization methods. In general, a complex case will require use of iterative approaches.
Accelerated Life Tests: Stress Step (Classical Methods)
Authors: Nikulin Mikhail
Published: February 2008

In this chapter we consider the accelerated models describing dependence of the lifetime distribution on the step stresses. Such models are often used in reliability and survival analysis to study the reliability of highly reliable, complex technical and biotechnical systems in dynamic environments.
Accelerated Life Tests: Designs Comparison within a
B
ayesian Framework
Authors: Van Dorp Johan René , Mazzuchi Thomas A. , Garcidueñas Jorge E.
Published: February 2008

Accelerated life testing (ALT) is the set of procedures used to reduce the time needed to obtain information related to life characteristics of an item, material or part of interest. The importance of the design of these ALTs is paramount to obtain inferential life estimators with, for example, minimum variance. Typically, for a set ALT, step‐regimen optimal stress levels and duration are determined in the ALT design phase. Herein we focus on the comparison of different ALT designs (fixed stress, profile ALT, progressive step‐stress ALT, and regressive ALT) within a single Bayesian inference framework. We analyze the preposterior variance of the use stress reliability based on a single failure over the course of the ALT for these different ALT designs. To the best of our knowledge Miller and Nelson in their 1983 article titled “Optimum simple step‐stress plans for accelerated life testing” in
IEEE Transactions on Reliability
R–32
, 59–65, were among the earliest to claim similarity of asymptotic estimator variance when comparing step‐stress testing ALT with fixed stress ALT. The results in this article shed some light on this claim within a Bayesian context.
Accelerated Life Tests: Nonparametric Approach
Authors: Ebrahimi Nader
Published: February 2008

Many of today's products are capable of operating under extremes of environmental stress and for thousands of hours without failures. For a variety of reasons, manufacturers may wish to obtain reliability results for their products more quickly than they can with data obtained under normal operating conditions. Consequently, traditional life data analyses are no longer sufficient to identify design weaknesses or validate life prediction.

Accelerated life testing (ALT) is an approach for obtaining more information from a given test time than would normally be possible. It does this by subjecting items to test environment that is more severe than those experienced during normal use. ALT uses a model relating reliability (a life) measured under high‐stress conditions to that which is expected under normal operation to determine the reliability (or length of life). Various parametric and nonparametric models have been introduced to describe the relationship between the distribution functions of lifetimes for different stress levels. The parametric models assume a particular form of the distribution functions for different stress levels and the relationship between them. The approach in this article is nonparametric, where only a relationship between the distribution functions corresponding to any two different stress levels is assumed.
Prediction of Expected Fatigue Lives of Fiber Reinforced Plastic Joints
Authors: Momenkhani Kourosh , Sarkani Shahram
Published: February 2008

This paper scrutinizes the accuracy of the application of the probability distribution functions for predicting the fatigue lives of different fiber reinforced plastic (FRP) joints under variable loading. Three different joint configurations (bonded, bolted, and bonded‐bolted) were tested under constant‐ and variable‐amplitude fatigue loading. The constant‐amplitude fatigue test results were analyzed in both log‐log and semi‐log scales. From there, Rayleigh approximation and Gaussian cumulative functions were used to predict expected fatigue lives. This study shows that even though Rayleigh approximation provides better fatigue lives than Gaussian cumulative function for higher root‐mean‐square (RMS) levels, it falls behind in predicting fatigue lives in lower RMS levels. However, both methods are weak in approximating the experimental fatigue lives.
Design for Reliability
Authors: Pecht Michael
Published: February 2008

Design for reliability (DfR) requires that certain practices be followed to ensure the appropriate level of product reliability is obtained. This chapter discusses some of the key DfR practices and their impact on life cycle reliability.
Masked Failure Data
Authors: Yashchin Emmanuel
Published: February 2008

We discuss life‐testing situations in which some of the failures are not identified as being due to a single cause, but are rather explained by one of the causes in a group. Failures of this type are called
masked
. Within this framework, we discuss the problems of modeling, estimation, and diagnostics. Such problems involve, for example, identification of survival curves for individual causes, or estimation of parameters related to conditions under which masking occurs. Another class of problems is related to the impact of masking on root‐cause analysis of individual failures. We also discuss the impact of masking on quality and reliability management issues.
Process Reengineering
Authors: Pall Gabriel A.
Published: February 2008

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.
K
aizen
Authors: Coleman Shirley Y.
Published: February 2008

Kaizen
is a methodology used to improve some aspect of a business.
Kaizen
projects are well defined but limited in scope so that there is a reasonable chance of some improvement within a few days. A small team of people are taken out of their usual work environment and allowed to spend undisturbed time on the issue; they are encouraged to think deeply and laterally. Suitable projects include streamlining process flow or reducing waiting time. Better results are obtained when data are available and accessible at the start and the team has authority to make changes. The success of a
Kaizen
event is judged by measurable improvements.
Work‐Out
Authors: Hoerl Roger W.
Published: February 2008

The GE Work‐Out method for continuous improvement started in the late 1980s, as the brainchild of then‐CEO Jack Welch. Welch wanted to create an environment, modeled after the New England town meeting, in which people would feel free to share their frustrations about things hindering them in their daily work. He wanted frontline people to have the opportunity to air their opinions and ideas, and get an immediate answer from management on their suggestions to reduce bureaucracy and to address workplace issues. So Welch initiated a formal process, originally using facilitators outside of GE—which made people feel comfortable sharing things they would not normally share with their management. When using this approach, teams discuss specific work‐related issues about which they have firsthand knowledge. They share opinions, debate, argue, and eventually agree on suggestions to improve the situation. That is, they “work out” solutions to their issues. Management retains the right to approve or disapprove the suggestions, but they are expected to give immediate answers, rather than sending the suggestions to a “committee” for further discussion and review. This approach still works well today for appropriate problems, and should form an important place in the process improvement systems of organizations.
Statistical Quality Control
Authors: Parr William C.
Published: February 2008

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.
Process Maps, Construction of
Authors: Daniels Lorraine
Published: February 2008

Process mapping is an essential part of the quality efforts within any organization. This article addresses the basics of mapping and the reasons for mapping. Included are the generic steps for conducting a mapping session. Different formats of process maps are illustrated with the generic purpose of the map described. Best practices and difficulties of process mapping are also addressed.
Project Management, Stage‐Gate Approach to
Authors: Trabert Thomas F.
Published: February 2008

The project process uses a predetermined set of steps, or stages, in a sequence to plan and implement a variety of projects. Each step has a stage‐gate, which is the criterion or question that must be appropriately addressed prior to proceeding to the next stage in the project. Each project an organization implements must be considered an investment of resources to produce a benefit or add value to the organization. The stage‐gate approach is used to ensure that the project has the necessary support and approved from the senior leadership prior to proceeding to the next stage and to continue investing resources. The project process model is composed of four main phases and nine stages within these phases. The planning phase includes Stage 1, Inception and Stage 2, Feasibility. The solution design phase contains Stage 3, Concept design, Stage 4, Scheme design, and Stage 5, Detailed design. The implementation phase includes Stage 6, Performance and Stage 7, Commissioning and Handover. And the final operational startup phase consists of Stage 8, Process integration, and Stage 9, Closeout and benefits realization. By applying the same disciplined approach to each project, your organization establishes ways of working that sets the organization up to reliably produce projects and have the greatest likelihood of gaining the return on investment promised at the outset of the project.
Total Productive Maintenance
Authors: Nakamura Tsutomu
Published: February 2008

We define the concept of total productive maintenance (TPM). TPM was developed by the Japan Institute of Plant Maintenance (JIPM). We also perform a technical study to evaluate TPM implementation results in global companies as well as Japanese companies.
Cause‐and‐Effect Diagrams
Authors: Kenett Ron S.
Published: February 2008

Cause and effect is a basic knowledge driven by theoretical and empirical considerations. Several tools have been proposed to map cause and effect relationships, with some more heuristics some highly quantitative. In this section we cover the Ishikawa fishbone diagram, structural equation models, and Bayesian networks.
Factor Relationship Diagrams
Authors: Hild Cheryl , Sanders Doug
Published: February 2008

Design of Experiments consists of methodologies and data acquisition plans where specific product or process factors (X's) are manipulated across certain levels in order to understand their impact on critical performance parameters (Y's). However, experimental results are impacted by the choice of the design factors (X's), the design structure, and by decisions on how background and noise variables are managed during the experiment. The Factor Relationship Diagram (FRD) is presented as a visual tool effective in assisting in the planning of DOEs, the evaluation of alternative design plans, and in the interpretation of DOE results. The FRD also provides a visual means of describing the error structure and understanding the implications of restrictions on randomization in a DOE.
Value Stream Analysis in Quality Management
Authors: Grayson Kevin
Published: February 2008

Value stream analysis as a quality management tool. Let value stream analysis determine where to direct you improvement efforts.
Creativity Tools
Authors: Zlotin Boris , Zusman Alla
Published: February 2008

Over the centuries, the trial‐and‐error‐method (T&EM) was the main creativity tool. While resulting in an enormous amount of incredibly valuable inventions enabling human survival, T&EM became less and less acceptable as a method for design and systematic innovation with the acceleration of technological evolution. During the second part of the twentieth century, hundreds of creativity techniques were developed; however, most of them were psychologically based, were lacking a systematic approach and structure and could not provide smooth integration with normal engineering and new product development. This paper provides a brief description and comparative analysis of the best known creativity techniques including a brand new knowledge based approach to creativity implementation which is part of the theory of inventive problem solving (TRIZ) in its classical and advanced forms.
Directed Evolution
®
Authors: Zlotin Boris , Zusman Alla
Published: February 2008

The ability to understand, predict, and control evolution of technology is becoming the centerpiece need of the informational era. A proactive approach to all aspects of innovation requires the development of an integrated multistep process for “visualizing the future”, which entails anticipating possible evolutionary scenarios (both positive and negative), selecting the preferable variant(s), and realization methods, providing creative support for the realization process, and forming the vision for the next step. This paper describes the Directed Evolution
®
(DE) methodology developed at Ideation International over the last decade. DE is the result of synthesizing the most powerful approaches to creativity and innovation developed during the last century, making it the most suitable technology for the twenty‐first century and the informational era.
Quality Management, Overview
Authors: Watson Gregory H.
Published: February 2008

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).
Six Sigma
Authors: Godfrey A. Blanton
Published: February 2008

Over the past two decades quality management has evolved into a leading‐edge practice called Six Sigma. Although not radically different from total quality management and total quality control, there are at least seven major differences. Sophisticated tools have been added to quality management practices in a structured way with intense application to achieve breakthrough results. Training methods for practitioners, often called Green Belts and Black Belts, are intense. Bottom‐line results are stressed.
Scorecards
Authors: Jordan Michael E. , McCarty Thomas
Published: February 2008

This article covers the essential tasks and requirements for building performance scorecards, a tool commonly used by management teams to gauge functional performance and to identify improvement opportunities. We cover scorecard development starting with the elaboration of customer expectations, the mapping of programs and processes to those expectations, and suggestions for how to assign useful units of measurement. We include practical advice for managing the dashboard in an action‐oriented manner.
Lean Accounting
Authors: Grayeski Frank , Stavish Len
Published: February 2008

Lean accounting calls upon the quality‐derived tools and methods used in both Lean and Six Sigma initiatives over the past several decades. This article discusses the application of the DMAIC (pronounced Da‐May‐Ick) model, which incorporates tools from Lean and Six Sigma bodies of knowledge, as an analytical framework for implementing lean accounting.
Assessment of Experimental Designs
Authors: Anderson‐Cook Christine
Published: February 2008

Collecting data as part of a designed experiment involves choices between different candidate designs. The criteria for choosing between these designs involves many competing objectives which frequently force us to make trade‐offs based on which of these ideal characteristics of the design we think are most important. Optimality criteria are single‐number summaries for the “goodness” of the design, which typically focus on the quality of estimation or prediction. In addition to these measures, the robustness of the design to model misspecification, problems with data collection during the experiment, or erroneous data are also important. A number of the characteristics of good designs are outlined and compared, and some graphical methods for better understanding the performance of a design are shown. While there is no single answer for how to choose a good design, an experimenter can make a better choice of which objectives are most important for a particular application by considering a variety of diverse criteria.
Scan Statistics
Authors: Glaz Joseph
Published: February 2008

Scan statistics have been used extensively in several areas of science and technology to test for the occurrence of clusters of rare events. In this article we present a survey of results in the area of scan statistics that are applicable to quality control and reliability theory. In quality control, scan statistics have been used in the design and analysis of acceptance sampling schemes. We survey results that are useful in evaluating the operating characteristics of such sampling schemes. In reliability theory, scan statistics have been used in the analysis and design of a
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Process Capability Indices, Alternatives to
Authors: Post Richard I.
Published: February 2008

Since we (the statistical community) are to blame for the introduction of process capability indices (PCIs) and their ensuing use and abuse in the industrial world, it is now up to us to provide an alternative to PCIs that is both statistically sound and relatively simple to understand. The problems with the present capability indices have been well documented elsewhere (e.g., Gunter 1, Kotz 2, Journal of Quality Technology (JQT) 24 3) and these will not be addressed here. Instead, the dual roles of specifications and PCIs in process and product development and in the manufacturing environment will be discussed. A basic approach to meet most of the goals of PCIs without some of their problems will then be introduced. Incorporated into the measures will be the concepts of confidence level and sample size. An example of this procedure is included. The relatively simple assumptions of this approach can then be modified to meet requirements where PCIs are presently being applied. These measures are intended to be a foundation rather than a total solution to all the needs of PCI users. However, these techniques will provide a direction for future work.
Statistics in Data and Information Quality
Authors: Redman Thomas C.
Published: February 2008

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.
Interlaboratory Comparisons
Authors: Guthrie William F.
Published: February 2008

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