A “Six Sigma”© Case Study: G.E.P. Box's Paper Helicopter Experiment—Part B

This article presents an application of the “Six Sigma” DMAIC model to G.E.P. Box's famous “paper helicopter” experiment. The Improve and Control Phases are presented here. The Define, Measure, and Analyze Phases were presented in an earlier paper. The intent of this article is to present the reader with a case study for structuring a “Six Sigma” project.     

A “Six Sigma”© Black Belt Case Study: G.E.P. Box's Paper Helicopter Experiment Part A

This article presents an application of the “Six Sigma” DMAIC model to G.E.P. Box's famous “paper helicopter” experiment. The define, measure, and analyze phases are presented here, and the improve and control phases are presented in a follow-up article. The intent of this article is to present the reader with a case study for structuring a “Six Sigma” Black Belt project.     

Paper Organizers International: A Fictitious Six Sigma Green Belt Case Study. I

“Six Sigma” management is in vogue in many of the world's largest and most successful corporations. However, for all of its popularity, there is much confusion as to the exact structure of a Six Sigma project. The purpose of this article is to present the first part of a detailed, step-by-step case study of a simple Six Sigma Green Belt project. This part of the case study presents the Define and Measure phases of the define-measure-analyze-improve-control (DMAIC) method for improving a process.     

Paper Organizers International: A Fictitious Six Sigma Green Belt Case Study. II

The purpose of this paper is to present the second part of a detailed, step-by-step case study of a simple Six Sigma Green Belt project. In the last edition of Quality Engineering, the first part of a Six Sigma Green Belt case study was presented. That paper showed the Define and Measure phases of the DMAIC method. This paper presents the rest of the case study, or the Analyze, Improve, and Control phases of the DMAIC method.     

A Case Study for Reducing Client Waiting Time in a Health Evaluation Center Using Design for Six Sigma

Abstract:

Long waiting times in health evaluations are an unnecessary waste of resources. This case study focuses on the Health Evaluation Center in the Chang Gung Medical Center in Taiwan and demonstrates an empirical application of reducing waiting time by using the Design for Six Sigma method. We redesigned the health evaluation process and proposed a method to develop a new capable system with radio frequency identification (RFID) technology. Client waiting time and total cycle time of the process were reduced by 56% and 40%, respectively.     

A Report on Mudd Design Workshop II: “Designing Design Education for the 21st Century”

This paper reports on a workshop on design education held at Harvey Mudd College (HMC) in May 1999. Mudd Design Workshop II was intended to provide a forum that would bring together design educators, design researchers, and designers from industry, in order to focus exclusively on the teaching of design in engineering education for the next century. Sessions were devoted to (1) design projects in both cornerstone and capstone courses, and metrics for selecting projects; (2) discipline‐based and cross‐disciplinary design courses; and (3) pedagogy, technology, and assessment in design education. Major emergent themes included the desirability of design throughout the curriculum, focuses on coaching and on learning, roles of projects and interactive learning, and the need to better address the interactions of grading and learning. Participants' specific commitments to future actions are also given.     

Development and validation of “grey-box” models for refrigeration applications: A review of key concepts

“Grey-box” modelling combines the use of first-principle based “white-box” models and empirical “black-box” models, offering particular benefits when: (a) there is a lack of fundamental theory to describe the system or process modelled; (b) there is a scarcity of suitable experimental data for validation or (c) there is a need to decrease the complexity of the model. The grey-box approach has been used, for example, to create mathematical models to predict the shelf life of chilled products or the thermal behaviour of imperfectly mixed fluids, or to create models that combine artificial neural networks and dynamic differential equations for control-related applications. This paper discusses the main characteristics of white-box, black-box and their integration into grey-box models, the requirements and sourcing of accurate data for model development and important validation concepts and measures.