bluuebunny/crossref_metadata_2025_split huggingface dataset:
Queues in Reliability
Authors: Ausín M. Concepción 
Published: February 2008

Abstract
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

Abstract
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: Designs Comparison within a
B
ayesian Framework
Authors: Van Dorp Johan René , Mazzuchi Thomas A. , Garcidueñas Jorge E. 
Published: February 2008

Abstract

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

Abstract
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

Abstract
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.
Masked Failure Data
Authors: Yashchin Emmanuel 
Published: February 2008

Abstract

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

Abstract

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

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

The article cites a number of factors that can cause BPR initiatives fail to reach their initial objectives, and discusses three known weaknesses of the methodology, including the
process paradox.
K
aizen
Authors: Coleman Shirley Y. 
Published: February 2008

Abstract

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

Abstract
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

Abstract
This article discusses statistical quality control. Statistical quality control dates from the 1920s. Shewhart effectively launched the field of statistical quality control with a memorandum issued on May 16, 1924 featuring the first control chart. These ideas and others have been developed over many years to provide a reasonably complete and versatile set of statistical tools for studying, monitoring and improving processes using a wide variety of statistical methods.
Project Management, Stage‐Gate Approach to
Authors: Trabert Thomas F. 
Published: February 2008

Abstract
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.
Factor Relationship Diagrams
Authors: Hild Cheryl , Sanders Doug 
Published: February 2008

Abstract
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.
Creativity Tools
Authors: Zlotin Boris , Zusman Alla 
Published: February 2008

Abstract
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

Abstract

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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract

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
k
‐within‐consecutive‐
m
‐out‐of‐
n
:
F
system of
n
components arranged in a linear or circular fashion. This system fails if
k
components fail within any segment of
m
consecutive components. In this article, we survey results for approximations and bounds for the system reliability of such systems. Two‐dimensional scan statistics have been used in the analysis and design of a
k
‐within‐consecutive‐(
m
1
,
m
2
)‐out‐of‐(
n
1
,
n
2
):
F
system of
n
1
n
2
components arranged in an
n
1
by
n
2
rectangular or cylindrical lattice. This system fails if
k
components fail within an
m
1
by
m
2
rectangular subregion in the
n
1
by
n
2
lattice. We survey results for the approximations and bounds for system reliability of these systems. In material science, reliability of certain materials can be compromised by the appearance of a large number of microcracks in a small area or volume. We present a survey of two‐ and three‐dimensional scan statistics that have been used in analyzing the occurrences of such microcracks.
Process Capability Indices, Alternatives to
Authors: Post Richard I. 
Published: February 2008

Abstract
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

Abstract
In many respects, statistical foundations and techniques that worked so well to help improve the quality of manufactured goods have proved extensible to data and information. But data and information differ from manufactured products in some important ways, presenting both challenges and opportunities. For example, unlike manufactured products, data and information have no measurable physical qualities such as length or viscosity. This article discusses several such differences and their implications for quality management.
Interlaboratory Comparisons
Authors: Guthrie William F. 
Published: February 2008

AbstractAn 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.