bluuebunny/crossref_metadata_2025_split huggingface dataset:
Team Building
Authors: O'Brien Margaret G. 
Published: February 2008

Abstract

Teams are not new to our workplace; they have been around for a very long time and have been used effectively for short‐ and long‐term implementation strategies in organizations, as evidenced by all the articles and books written on teams and team work over time. Interestingly enough, when we began to examine critical performance factors on technical projects, we discovered the following.

Improving: technical skills were not enough to sustain gains in an organization. One of the missing factors was a systematic application to team building to complement the scientific technical model used in Six Sigma at NC State University.

The perception of how to operate in and on a team was disparate across an organization. Everyone had a different concept.

Including modules on teams and team building as well as others on project management, and leadership and change management, in teaching a technical topic such as Six Sigma for technical professionals would probably enhance the positive outcomes for the participants.

The Six Sigma curriculum committee determined that we would provide multiple modules in every technical class taught at our university. The modules focused on basic skills including team roles, conflict and self‐assessment, and reward/recognition.

This article shares some of the results and findings from the implementation of team modules in the technical non credit Six Sigma curriculum with our business and industry noncredit classes at NC State University in Raleigh, North Carolina.
Multi‐Vari Charts
Authors: Shainin Richard D. 
Published: February 2008

Abstract
This article introduces multi‐vari charts. It shows how they can be used to break process variation into several components of variation each associated with a source of variation. Breaking process variation into these components enables one, using the Pareto principle, to identify the biggest component of variation. Eliminating the source of this component is usually the best way to reduce process variation. This paper also presents several interesting examples of the application of multi‐vari charts.
Lean Methods, Creating Flow with
Authors: Srinivasan Mandyam M. 
Published: February 2008

Abstract
Lean thinking and lean manufacturing are terms associated with effective delivery of goods and services to the customer. These are management tools that are typically used in conjunction with Six Sigma. This article presents a brief discussion on the real purpose of lean, which is to create a smooth flow of goods or services across the entire supply chain. It describes how lead‐time reduction is vital to achieve this purpose. Lean is an ongoing effort, and this article also briefly discusses how organizations can use the tools and techniques of lean to undertake the lean journey.
Error Proofing, Healthcare
Authors: Nakajo Takeshi , Clapp Timothy G. , Godfrey A. Blanton 
Published: February 2008

Abstract
Many error proofing solutions are implemented successfully in healthcare. Most of them, however, are generated individually without the benefit of the knowledge of the wider body of error‐proofing solutions in related healthcare areas. Almost no healthcare organization has the benefit of the extensive experience in error proofing in manufacturing industries or other service industries. This article provides a simple methodology for applying error proofing principles and proven healthcare solutions for systematically generating workable solutions to reduce human error.
Cost of Quality
Authors: Godfrey A. Blanton , Moore Joseph D. 
Published: February 2008

Abstract
The understanding of costs of quality has evolved considerably over the past few decades. Traditionally, quality costs were thought of as prevention, appraisal, internal failures, and external failures. We also thought there was an optimal level of quality where costs were minimized. Newer thinking of quality costs has questioned the concept of optimum level, added the concept of quadratic loss, and explored many other costs associated with the costs of poor quality. Often, these new costs are far, far greater than the traditional costs.
Failure Modes and Effects Analysis
Authors: Snee Ronald D. , Rodebaugh William F. 
Published: February 2008

Abstract
This chapter gives an overview of failure modes and effects analysis (FMEA) and shows how it is allied in practice by discussing its use in improving manufacturing equipment performance. FMEA is an effective tool for the design of new products and processes and the improvement of existing processes. Using FMEA one can determine how a product, process, or system might fail and the likely effects of particular modes of failure. Having originated in the U.S. military in the 1940s, FMEA today is an analytical tool widely employed in approaches to quality and process improvement such as ISO 9000, ISO/TS 16949, Lean Six Sigma, and Design for Six Sigma.
Quality Function Deployment
Authors: El‐Haik Basem 
Published: February 2008

Abstract
Quality Function Deployment (QFD) is a structured approach to defining customer needs or requirements and translating them into specific plans to produce products to meet those needs. The “voice of the customer” is the term commonly used to describe these stated and unstated customer needs, delights and wants. This understanding of the customer needs is then summarized in a planning matrix called house of quality (HOQ). These matrices are used to translate higher level “whats” or needs into lower level “hows”—functional requirements, design parameters and process variables over four phases to satisfy the voice of the customer (VOC). QFD is methodology that broadens or extends quality responsibility throughout a product, service or process development.
Benchmarking in Project Definition
Authors: Watson Gregory H. 
Published: February 2008

Abstract
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.
Theory of Constraints
Authors: Mordoch Avraham 
Published: February 2008

Abstract

The
theory of constraints
(
TOC
) is a holistic, overall management philosophy that aims to (a) resolve systems‐oriented problems and (b) continually achieve more of the goal of a defined system. If that system is, e.g., a for‐profit business, then the goal becomes one of making more money, in the present as well as in the future.
Target Costing
Authors: Faltin Donna M. 
Published: February 2008

Abstract
Target costing is a proven, market‐driven strategy for product and service design. First conceived in the 1960s in Japan, target costing expanded the concept of value engineering to a customer‐focused, profit‐driven cost system that focuses on price.
Traditional “cost plus” reasoning sets the price of a product or service by starting with anticipated or realized costs, and adding the desired profit margin. This approach risks setting prices at a level the market will not support, with consequent risk of losing part or all of the capital invested in the introduction of a new product or service. Target costing, on the other hand, starts with the price that market conditions warrant, and factors in desired profitability to yield a “target cost” that must be met in the design and delivery of a product or service. By starting from market realities and explicitly accounting for business needs, target costing makes business success a design requirement, and helps avoid costly financial disasters in new product or service introduction.
Organizational Assessment Models
Authors: Conti Tito A. 
Published: February 2008

Abstract

The paper discusses the origin, development, and foreseeable trends in the area of organizational assessment models. The origin is traced back to the 1930s/1950s, when the problem of assuring quality in new product development became a competitive issue. “Quality assurance (QA) standards” emerged in the 1950s/1960s, together with methods to check compliance of the organization with such standards. Methods took the form of independent assessments, aimed at checking the capability of the product generating system (first‐party audits). The opportunity to assess supplier capabilities in advance soon led to the extension of the standards/assessment approach to B2B/B2A relations.
External
QA standards were then conceived, as the basis for assessing customer capabilities through “second‐party” audits. The evolution from proprietary external QA standards and second‐party audits to sector, national, and international standards and third‐party audits is described, with a particular emphasis on ISO 9000 standards and certification. Discontinuity in assessment models took place in the 1980s, with the outbreak of the new quality based competition and the advent of the so‐called total quality management (TQM) models. The paper examines the different types of TQM models in use today, from the quality award models to those focused on the optimization of performance measurement, and the “diagnostic models” for organizational improvement. Finally, trends in organizational models are discussed: first the evolution from organizational assessment to organizational improvement models; second the injection of more systems thinking into the models.
Multivariate Stochastic Orders and Aging
Authors: Belzunce Félix , Shaked Moshe 
Published: February 2008

Abstract
Many aging properties of univariate random lifetimes can be characterized by various stochastic orders. As a result, several efforts have been made in the literature to define or characterize aging notions of random vectors of lifetimes by means of multivariate stochastic orders. Such definitions enable researchers to apply the intuition that comes with the univariate aging notions to complex systems. The characterizations are useful for the identification of the corresponding aging properties and for a better understanding of the meaning of these properties. In this article we describe such definitions and characterizations.
Causality
Authors: Evandt Øystein 
Published: February 2008

AbstractDifferent aspects of the causality concept are discussed, inclusive of the fact that no widely accepted definition of causality exists. Still the concept has been essential to the development of science. Further, aspects of how to establish causal relationships are discussed, including causality and statistics, empiricalversusmechanistic causal models, agreement of causal relationships within acceptable margins of error when statistical data are part of establishing such relationships, and Karl Popper's concept of falsifiability. A few concepts and tools related to causality in quality and reliability are briefly mentioned. Newer developments in causality and statistics are mentioned and references to important literature on these developments are given, in addition to references to more established knowledge.
Quadrature and Numerical Integration
Authors: Ausín M. Concepción 
Published: February 2008

Abstract
The objective in numerical integration is the approximation of a definite integral using numerical techniques. There is a large number of numerical integration methods in the literature and this article overviews some of the most common ones, namely, the Newton–Cotes formulas, including the trapezoidal and Simpson's rules, and the Gaussian quadrature. Different procedures are compared and illustrated with examples. Discussions about more advanced numerical integration procedures are also included.
Computational Issues in Network Reliability
Authors: Colbourn Charles J. , Shier Douglas R. 
Published: February 2008

AbstractSystems of interconnected components can often be usefully represented by networks. It is practically important to calculate the reliability of such network systems when the components are subject to (statistically independent) failures. We define several commonly occurring reliability models and summarize the computational complexity of calculating their associated reliability measures. Some general techniques for exact and approximate calculation of network reliability are surveyed.
Data Mining in Quality and Reliability
Authors: Allen Elaine , Seaman Chris 
Published: February 2008

Abstract
Data mining techniques can be separated into two primary areas: those using supervised algorithms and those using unsupervised algorithms. Supervised data mining techniques most closely resemble standard statistical modeling because they are predictive, while unsupervised data mining techniques are associative and are used to examine relationships between variables and may or may not have an implied direction in the relationship. This article uses a large healthcare trial in which data mining techniques provided the only way to analyze the dataset for data quality.
Discrete‐Event Simulation for Reliability Prediction
Authors: Jones Jeff 
Published: February 2008

Abstract
Discrete‐event simulation is an empirical approach to system modeling that can allow us to build real‐world models and attempt to use them to predict what is likely to happen to the reliability of a system in the future. The underlying techniques used in this process involve random sampling from failure distributions, and representation of system structure using such techniques as Petri nets, Markov diagrams, and fault trees. These techniques allow us to build fairly realistic models of complex systems that can be used to understand their dependability behavior under various operating conditions.
B
ayesian Robustness: Theory and Computation
Authors: Betrò Bruno 
Published: February 2008

Abstract
The problem of analyzing the robustness of Bayesian procedures has been considered to some extent in the last few years. In spite of this, robustness analysis has not yet become part of routine Bayesian analysis mainly because of the inadequate development of numerical algorithms and related software. When robustness with respect to the prior is considered and the prior belongs to a class defined in terms of the so‐called generalized moment conditions, then the problem reduces to one of linear semi‐infinite programming (LSIP). The first part of this article is devoted to a review of the general theory of optimization of linear functions over generalized moment classes of measures. In the second part, its application to robust Bayesian analysis is considered, giving sufficient conditions for the existence and the identification of the required extremum values. Algorithms which have been proposed for actual computations are finally reviewed in the third part.
Survival Analysis, Nonparametric
Authors: Kottas Athanasios 
Published: February 2008

Abstract
Survival analysis was one of the first areas where Bayesian nonparametric priors were utilized to enhance the flexibility of standard parametric models. Bayesian nonparametric methods are very well suited for survival data analysis, enabling flexible modeling and rich inference for the survival function or hazard function, providing techniques to handle censoring, and allowing incorporation of prior information. In this article, we provide a review of Bayesian nonparametric and semiparametric approaches, based on mixture models, to the analysis of univariate survival data. We discuss mixture models for survival distributions in a fully nonparametric setting as well as for error distributions in semiparametric accelerated failure time regression settings.
Asymptotic Reliability Analysis of Very Large Structural Systems
Authors: Adhikari Sondipon 
Published: February 2008

Abstract

In the reliability analysis of safety‐critical complex engineering structures, a very large number of the system parameters can be considered to be random variables. The difficulty in computing the failure probability using the classical first and second‐order reliability methods (FORM and SORM) increases rapidly with the number of variables or “dimension”. On the basis of the asymptotic distribution of quadratic form in Gaussian random variables, two formulations, for the case when the number of random variables
n
→ ∞, are provided. The first is called
strict asymptotic formulation
and the second is called
weak asymptotic formulation
. Both approximations result in simple closed‐form expressions for the probability of failure of an engineering structure. The proposed asymptotic approximations are compared with existing approximations and Monte Carlo simulations using numerical examples.
Smoothed Function Estimation for Censored Data
Authors: Chaubey Yogendra P. , Sen Arusharka , Sen Pranab K. 
Published: February 2008

AbstractWe review the well‐known smoothing techniques for density and hazard function estimation under random censoring, developed in the recent past. We then outline how the recently proposed smoothing technique by Chaubey and Sen for uncensored data can be adapted to the case of random censoring.
Dynamic Programming Methods in Repair and Replacement
Authors: Bhattacharjee Manish C. 
Published: February 2008

Abstract
Applications of dynamic programming to problems in reliability, with a specific emphasis on repair and replacement are surveyed. A brief summary of early classic work on such uses of dynamic programming is followed by a review of later approaches adopted by different researchers toward the problem of modeling plausible and pragmatic notions of imperfect repairs. We then provide a synthesis of how such different approaches for the study of repairable systems can be subsumed within a unified setting of a stochastic dynamic programming framework with some illustrative applications of such a formulation.
Splines and other Metamodels in Reliability Analysis
Authors: Schueremans Luc , Van Gemert Dionys 
Published: February 2008

Abstract
Simulation‐based concepts are very attractive to be used in structural reliability because of their inherent simplicity. The major drawback, however, remains the large number of simulations required to obtain sufficiently accurate results. In case of large and complex structural systems with low failure probabilities, the processing time might become unrealistic. To cover this disadvantage, reliability analyses are based on simulation methods (Monte Carlo or directional sampling) in combination with an adaptive metamodel. Often use is made of a low‐order polynomial response surface. The use of splines, neural networks, or ordinary kriging may increase the overall performance.
Calibration
Authors: Bzik Thomas J. 
Published: February 2008

Abstract
Calibration establishes and standardizes common units of measurement for commerce and scientific study. Through calibration the measured item or sample is related to items or samples with known properties, the standards. Statistical methods are typically used to identify and quantify the form of the relationship between sample measurements and one or more standards. They are also used to evaluate the accuracy and precision of the current as well as alternative measurement methods. Statistical tools are also used to determine the optimal frequency of recalibration as well as the frequency with which calibration should be checked. Calibration models can take the form of linear relationships or a nonlinear relationship. Two‐variable calibration models are often monotonic in their form. While many calibration models often involve only two variables, they can be multivariate in nature. Keeping bias out of calibration models requires not only selecting the correct model form but also ensuring that the model is fit, using an appropriate optimization criteria. Calibration models typically provide very high explanatory powers as compared to other typical scientific modeling applications such as plant or process optimization studies. Calibration models also typically yield extremely good levels of statistical significance. Current common statistical toolsets used in calibration studies are often not well structured for handling the actual, and sometimes complex, error structures that occur in routine application of calibration.
Policy Deployment for Performance Improvement
Authors: Watson Gregory H. 
Published: February 2008

Abstract

Policy deployment is a Japanese‐originated management process that applies the Deming cycle of plan‐do‐check‐act (PDCA) to the processes of planning and execution. Policy deployment has sometimes been called
management by objectives
(MBO)
on steroids
or
Turbo‐MBO
. A policy deployment process coordinates the definition and implementation of strategically important business‐process‐change projects to improve the routine operating processes of an organization. Policy deployment aligns and coordinates the activities of the organization through a number of mechanisms that open lines of communication across the organization in cross‐functional and cross‐layer dialogs. These communication mechanisms solicit ideas for making business improvements, define projects and objectives through an interactive “catchball process”, and review progress through cross‐layer improvement project assessments. This in‐depth involvement of the entire organization in both the planning and execution processes creates acceptance for change through the active participation of an engaged workforce. Synonyms for policy deployment include
hoshin kanri
(the original Japanese name) as well as policy management and management by policy (MBP).
Monitoring of Safety in
EU
Railways
Authors: Pira Angelo , Piazza Roberto , Rajan Jane 
Published: February 2008

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