bluuebunny/crossref_metadata_2025_split huggingface dataset:
Project Management, Stage‐Gate Approach to
Authors: Trabert Thomas F. 
Published: September 2014

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.
Multi‐Vari Charts
Authors: Shainin Richard D. 
Published: September 2014

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: September 2014

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.
Six Sigma
Authors: Godfrey A. Blanton 
Published: September 2014

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.
Lean Accounting
Authors: Grayeski Frank , Stavish Len 
Published: September 2014

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.
Change Management, Stakeholder Assessment in
Authors: Trabert Thomas F. 
Published: September 2014

Abstract
In managing organizational changes, a stakeholder assessment tool is used to increase the chances of a successful outcome. The benefit of a stakeholder assessment is to understand a stakeholder's level of support for a particular change initiative by first, determining his level of involvement in and commitment to the change and then assessing his ability to impact that change through his influence, authority, and control in the organization. The results of these two assessments of each stakeholder are summarized in a matrix showing the relationship of each stakeholder with the others. For stakeholders who support the change, a plan is created to increase their level of influence throughout. For those who are not supportive of the change, a plan is created to either gain their support or decrease their influence.
Sigma Metric (Sigma Level)
Authors: Does Ronald J.M.M. , Mast Jeroen 
Published: September 2014

Abstract

The Greek symbol σ is used in statistics as a measure of variation. Since, especially in industry, many quality problems are related to excessive variation, the concept σ has become a basis for the definition of a measure for conformance quality: the
sigma metric
or
sigma level
. The sigma level indicates the performance of a process, namely: what percentage of products or service deliveries conforms to the specifications.
Work‐Out
Authors: Hoerl Roger W. 
Published: September 2014

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.
Critical‐to‐Quality Matrices
Authors: Hoerl Roger W. 
Published: September 2014

Abstract

In many applications, improvement teams need to take fuzzy, intangible customer needs, and translate them into measurable metrics. For example, the customer of an energy company producing electric generators may state that they want to deal with knowledgeable sales representatives from the company. This is a reasonable request, but how is the energy company to know if its sales representatives are “knowledgeable”? What does knowledgeable really mean, and how could the company measure it? Critical‐to‐quality (CTQ) matrices are one method used to take such fuzzy, intangible customer needs, and translate them into hard metrics that can be quantified. These metrics are often referred to as
critical‐to‐quality measures
. In addition to producing tangible metrics, these matrices also provide a prioritization of the metrics that enables focus on those metrics most critical to satisfying customers' needs. Such matrices can also be used in subsequent analysis, beyond identifying and prioritizing CTQs. For example, subsequent matrices can begin with CTQs and prioritize the most critical steps in the process that need improvement in order to impact the CTQs.
Team Building
Authors: O'Brien Margaret G. 
Published: September 2014

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.
B
ayesian Control Charts
Authors: Colosimo Bianca M. 
Published: September 2014

Abstract
Control charts are widely used in industrial practice to check for process stability. The basic procedure of control charting is due to Shewhart and consists in periodically inspecting a sample of quality characteristics to decide whether the process is “out of control”. The out‐of‐control state is characterized by deteriorated performances and should be detected as soon as possible to bring the process back to its nominal or “in‐control” state. With reference to this objective, different Bayesian control charts have been proposed in the literature; the term “Bayesian” is used here to denote any approach that adopts Bayes' theorem within the monitoring procedure. In particular, the literature on Bayesian control charting is characterized by two streams of research. The first assumes that all the information required to detect the degraded state is enclosed in the posterior probability that the process is out of control. The second stream of research adopts a more classical perspective, which bases the out‐of‐control rule on detecting changes of the process parameters. In this case, Bayes' theorem allows one to compute the posterior distribution of the parameter of interest. The article reviews approaches proposed in both these streams of research, focusing mainly on the first type of Bayesian procedure.
Control Charts, Overview
Authors: Faltin Frederick W. 
Published: September 2014

Abstract
Control charts originated in the work of Dr. Walter Shewhart, the father of statistical process control, in the 1920s. Shewhart's groundbreaking insight was to apply statistical concepts to model the characteristic variability of a manufacturing process. His tool for doing so was the control chart, which was traditionally implemented graphically by having line workers plot production data over time. Corrective action would be taken when the plotted values fell outside of limits which were specified based on statistical properties of the process. Over the intervening decades, control charting techniques have been developed for monitoring and improving nearly all commonly encountered business processes, including nonmanufacturing processes such as supply chain/order fulfillment, human resource management, financial and accounting processes, call center management, and many others.
Cumulative Sum (CUSUM) Chart
Authors: Gan Fah Fatt 
Published: September 2014

AbstractThe use of the cumulative sum (CUSUM) charting procedure in process monitoring is first described. The strengths and weaknesses of the CUSUM charting procedure, with respect to the Shewhart charting procedure, are then illustrated with simulated data sets. Various other aspects of the CUSUM chart are also discussed. These include derivation of run‐length distribution, estimation of process mean using the CUSUM, weighting of samples for signaling, fast initial response for a newly start‐up process, simultaneous CUSUM schemes, design of the CUSUM chart, and multivariate CUSUM schemes.
Moving Range and

R

Charts
Authors: Champ Charles W. , Shepherd Deborah K. 
Published: September 2014

Abstract

Control charts for controlling the process mean are affected by changes in the process standard deviation. Therefore it is important to also control the process standard deviation. Various control charting methods have been developed for this purpose. Two commonly recommended statistical methods for controlling the process standard deviation are the moving range (
MR
) and the range (
R
) charts. The designs of
MR
and
R
charts are discussed in this article and each is illustrated with an example.
Multivariate Control Charts Overview
Authors: Runger George C. 
Published: September 2014

Abstract
Multivariate statistical process control is used to simultaneously monitor multiple measurements from a process. Similar to the case for a single variable, the objective is to detect assignable causes of variation. Multiple measurements are quite common in modern processes. This overview presents a brief summary of the advantages of an integrated solution for multiple measurements, types of applications, structure of the data, assignable causes, characteristics of charts, and signal interpretation.
Multivariate Exponentially Weighted Moving Average (
MEWMA
) Control Chart
Authors: Conerly Michael D. 
Published: September 2014

Abstract
The multivariate exponentially weighted moving average (MEWMA) control chart is used to simultaneously monitor several correlated process variables. The primary focus of this article is monitoring the process mean by constructing an MEWMA control chart. An overview of the application and use of MEWMA control charts is discussed. The plotting statistic is clearly defined and an illustrative example is presented.
Multivariate Cumulative Sum (
CUSUM
) Chart
Authors: Testik Murat C. , Runger George C. 
Published: September 2014

Abstract
Cumulative sum (CUSUM) control charts are important tools for the real‐time, on‐line monitoring of production processes. There are several multivariate extensions to the univariate CUSUM control chart. This work discusses the multivariate extensions to the CUSUM control chart and their uses. Basic formulas and notation are provided and the advantages and disadvantages of each are compared. References are given to allow the interested reader to investigate further.
Multivariate Charts for Variability
Authors: Lin Dennis K.J. , Yeh Arthur B. 
Published: September 2014

Abstract

In this article, we discuss two multivariate control charts for monitoring changes in a covariance matrix. The |
S
| chart is applicable when the subgroup size
n
is larger than the dimensionality
p
. The multivariate exponentially weighted moving squared‐deviation (MEWMS) chart is specifically designed for the case when
n
= 1. Examples are given and discussed to demonstrate how each chart can be used in practice. Related issues and potential future research are also discussed.
Multivariate Control Charts, Interpretation of
Authors: Mason Robert L. , Young John C. 
Published: September 2014

Abstract

Signal interpretation is straightforward when only one variable is being monitored in a process. This is because separate charts can be constructed to monitor changes in the location and/or changes in the variation of the variable of interest. For a multivariate process consisting of
p
variables, signal interpretation is more complex because the signaling statistic can potentially be a function of all
p
variables. Not only do we have to detect location and variation changes in the
p
variables, but we also have to detect changes in the relationships (i.e., correlations) among the many variables. Further, procedures that identify the particular variable or group of variables that cause or contribute to the signal are limited. In this presentation, we discuss two procedures that are useful in signal interpretation when using Hotelling's
T
2
statistic in multivariate process control and briefly describe several alternative methods.
Regression Control Charts
Authors: Shu Lianjie , Tsui Kwok‐Leung , Tsung Fugee 
Published: September 2014

Abstract
The regression control chart, which integrates regression analysis and control chart theory, has proved useful in various fields of application in the last few years. The basic idea of the regression control chart is to monitor the process output after it has been adjusted for the effect of the external covariate(s). This paper provides an overall review of regression control techniques. The formulation of the regression control chart is presented, and its computation is illustrated. Some remarks on future issues are also made.
Variable Sampling Rate Control Charts
Authors: R. Reynolds Marion , Stoumbos Zachary G. 
Published: September 2014

AbstractWhen control charts are used to monitor a process, the traditional practice is to take samples from the process using a fixed sampling rate (FSR). Variable sampling rate (VSR) control charts vary the sampling rate as a function of the data obtained from the process. With a VSR control chart the sampling rate is increased when there is some indication of a problem with the process, and decreased when there is no indication of a problem with the process. A VSR control chart signals in the same way as a standard FSR control chart when the indication of a problem with process is strong enough. There are several approaches to implementing the VSR concept. One approach is to allow the sampling interval to vary as a function of the data. Another approach is to allow the sample size for the current sample to vary as a function of the data in previous samples. A third approach is to allow the sample size for the current sample to depend on the data in this sample. Compared to traditional FSR control charts, all of the approaches to implementing the VSR concept will produce much faster detection of most process changes.
Economic Design of Control Charts
Authors: Saniga Erwin M. 
Published: September 2014

AbstractControl charts are designed by using a heuristic, or meeting statistical constraints or minimizing cost. The latter two design methods are calledstatistical designandeconomic design. A combination of them, calledeconomic statistical design, is a related criterion. In this chapter I discuss these methods and their relative strengths and weaknesses. I then review some literature that addresses the choice of an optimal economic process control policy and subsequently discuss how one can design the optimal economic control chart. I also review and address some of the criticism of economic design.
Average Run Lengths and Operating Characteristic Curves
Authors: Human Schalk W. , Graham Marien A. 
Published: September 2014

Abstract

Control charts are typically used in establishing whether a process is in a state of statistical control or not. The performance of any control chart is an important consideration and characteristics of the run‐length distribution are generally used to assess its performance. Two popular measures of a control chart's performance are the expected value of the run‐length distribution, called the
average run length
(
ARL
), and the operating characteristic (
OC
). Both are measures of how quickly a chart will detect or react to a change in the process and can be used to compare control charting plans. Both these measures provide useful information about the operational performance of a control chart. We discuss the
ARL
and the
OC
of the well‐known Shewhart

X

chart when both the process mean and variance are known. The application of
OC
curves in acceptance sampling is also considered.
Change‐point Methods in Statistical Process Control
Authors: Hawkins Douglas M. , Qiu Peihua , Zamba Kokou D. 
Published: September 2014

AbstractChange‐point methodologies applied to statistical process control are predicated on the possibility that a special cause induces a shift from an in‐control statistical model to an out‐of‐control statistical model, and so are particularly attractive for persistent special causes. Along with indications of a loss of control, they provide estimates of when the shift occurred, and (if needed) of the before‐ and after‐shift process parameters. A less obvious advantage is that some change‐point proposals allow for the near‐universal situation that the in‐control distribution of process readings is not known exactly. This feature largely removes the need for extensive phase I calibration studies, and allows phase II production use to start early. In addition to the stand‐alone use of change‐point methodologies for both signaling and diagnosing the effects of special causes, they have been proposed as tools for following up signals given by other charting methods, when their likelihood properties lead to good estimators of the time of occurrence and effect of the special cause.
Factorial Experiments
Authors: Kenett Ron S. , Steinberg David M. 
Published: September 2014

Abstract
Proactive experiments are an essential element of the learning process, in all application areas. Statistically designed experiments have been used to accelerate learning since their introduction by R.A. Fisher in the first half of the twentieth century. In this article we review factorial and fractional experiments and provide some examples from the chemical and semiconductors industries marketing and computer intensive simulations, and and briefly discuss the role of factorial experiments in statistical education.
Foldover Designs
Authors: Li William 
Published: September 2014

Abstract
Foldover is a commonly used follow‐up technique. A foldover design is constructed by reversing the signs of several columns of the initial design. We demonstrate how a foldover design can be used to untangle aliased effects, especially main effects and two‐factor interactions, found from the analysis of the initial design. We then discuss several methods for constructing foldover designs and introduce optimal foldover plans for both regular and nonregular factorial designs. The main advantage and disadvantage of the foldover method are also discussed.
L
atin Hypercube Designs
Authors: Tang Boxin 
Published: September 2014

Abstract
Latin hypercubes are a rich class of designs that are suitable for computer experiments and numerical integration. They are easy to generate and achieve maximum stratification in each of the univariate margins of the design region. This article introduces Latin hypercubes, explains how they can be used in computer experiments and numerical integration, and discusses their strengths and weaknesses. A design may not perform well in terms of other criteria such as those of space filling and orthogonality, simply because it is a Latin hypercube. The article concludes with a discussion on some of the methods for constructing Latin hypercubes that have better space‐filling or orthogonality properties.
L
enth's Method for the Analysis of Unreplicated Experiments
Authors: Lenth Russell V. 
Published: September 2014

Abstract
Lenth's method is an objective method for deciding which effects are active in the analysis of unreplicated experiments, when the model is saturated and hence there are no degrees of freedom for estimating the error variance. The article reviews its formulation, computation, and examples, and discusses the choice of critical values and the presentation and interpretation of the results.