Case Studies from The Center for Quality Improvements
UW37
Case Study: Experimental Design in a Pet Food Manufacturing Company
by Albert Prat and Xavier Tort, (October 1989).
Experimentation in the complex world of industry and service organizations requires a deep understanding of the basic engineering concepts underlying the process being studied, as well as relevant technical and economic constraints. The experimental design described in this report is a plant experiment where those constraints were taken into account. Several responses were measured, for the goal was not only to improve quality but also to increase productivity and reduce cost.
UW52
Quality Improvement Approaches for Chemical Processes
by William J. Hill and Lane Bishop, (August 1990).
Quality improvement of chemical processes through the use of design of experiments (DOE), variance component analysis, and process noise simulation models is the focus of this report. A case history of a nylon process serves as the backdrop as to how effective these "second generations" tools can be in the process industries. The memory of Dr. William G. Hunter and his philosophy provide the central theme and message for the discussion. Publication(s): Quality Engineering, 1990-91, Vol.3, No.2, pp.137-152.
UW59
Teaching Quality Improvement by Quality Improvement in Teaching
by Ian Hau, (February 1991).
In response to disturbing challenges ahead, leaders at the University of Wisconsin – Madison are committed to transform the institution to a Total Quality University. As a pilot project in the transformation, this paper describes how students and the instructor worked as a team to improve the quality of teaching in a class. Treating students as customers, the team identified 50 areas that affected the quality of teaching. A class survey revealed six areas where most students indicated problems. The instructor then implemented changes which dramatically reduced the defect rate as viewed by the customers in these areas. For example, the defect rate dropped from 78% to 22% for computer instruction, 56% to 8% for blackboard presentation, and 82% to 20% for overhead presentation. The team also developed a system to transfer their knowledge to the next team to ensure never-ending improvement in the future.
UW73
The Use of Statistics to Improve Manufacturing Systems
by Søren Bisgaard, (October 1991).
This article presents a general overview of statistical methods applied to solving manufacturing problems. We also provide a specific example of a statistically designed experiment used to study factors affecting robot accuracy. The robot experiment illustrates how manufacturing engineers can improve quality and productivity, and reduce costs by applying relatively simple statistical tools on the shop floor. Publication(s): appeared as "Statistical Tools for Manufacturing" in Manufacturing Review, Vol.6, No.3, pp.192-200.
UW93
Confounded Dispersion Effects in Robust Design Experiments with Noise Factors
by David M. Steinberg and Dizza Bursztyn, (December 1992).
Robust design experiments can be a very useful tool for improving quality. They enable engineers to reduce the variance of important quality characteristics by identifying design factors with dispersion effects and guiding the choice of nominal levels of those factors. Robust design experiments are especially effective when it is possible to build some variation directly into the experiment by including noise factors-factors that are impossible or too expensive to control during actual production or use. When noise factors are included, it is important to model their effects explicitly in the subsequent analysis. We present two examples in which failure to do so leads to incorrect conclusions about dispersion effects. Publication(s): to appear in Journal of Quality Technology.
UW97
Bringing Total Quality Improvement into the College Classroom
by W. Lee Hansen (March 1993).
This paper describes a recent effort to infuse the Total Quality Improvement (TQI) approach, popularized by Deming and others, into an upper-division, junior-senior economics course at the University of Wisconsin – Madison. The process of infusing TQI into instruction has received relatively little attention. Most efforts to bring TQI into higher education focus on improving administrative operations and establishing courses and programs for students to learn how to apply TQI in their future jobs. The challenge is in using TQI to help students realize their potential for learning in traditional courses.
UW124
A Case Study of the Use of Experimental Design and Multivariate Analysis in Product Improvement
by Marit Ellekjaer, M.A Ilseng and T Naes, (January 1995).
The overall purpose of this study is to identify an effective strategy for improving the sensory quality of a product. A study on processed cheese was used to develop and illustrate our ideas. A screening experiment, with seven processing and ingredients variables, was performed in order to identify the processing variables with the greatest effect on sensory quality. A fractional factorial design with resolution Iv was used to keep the number of experimental runs to a minimum. ANOVA and normal plots were used to evaluate the effects of the different factors on the sensory variables one by one. The same factors were identified as being important when the scores from a principal component analysis (PCA) of the sensory variables were analyzed. PCA was found to be of value in identifying samples that had improved properties compared to today’s product in addition to having a low intensity of undesirable properties.
UW129
Analysis of Unreplicated Split-Plot Experiments with Multiple Responses
by Marit Risberg Ellekjær, Howard T. Fuller and Kirsti Ladstein, (July 1995).
The purpose of this study is to demonstrate an effective strategy for unreplicated split-plot experiments with multiple responses. Through principal component analysis (PCA) the response variables are reduced to only those that describe different phenomena among the experimental samples. These selected response variables are then analyzed individually using ANOVA and Normal probability plots to identify the factors with the greatest influence on the quality and cost of the product. This approach makes it possible to take both the preferred quality characteristics and the production costs into account when studying a process or product. A case study from a fish food manufacturing company is used to illustrate our ideas.
Back to Top of Page
Pricing and Ordering