ENBIS: European Network for Business and Industrial Statistics
Forgotten your password?
Not yet a member? Please register
ENBIS7 in Dortmund
24 – 26 September 2007The following abstracts have been accepted for this event:

Efficient Design in Conjoint Analysis and Alike
Authors: Rainer Schwabe
Primary area of focus / application:
Submitted at 7Sep2007 21:31 by
Accepted

Calibration of instruments using LogVariance models
Authors: Diego Zappa  Massimiliano Pesaturo
Primary area of focus / application:
Submitted at 7Sep2007 22:42 by
Accepted

Selecting explanatory variables with the modified version of Bayesian Information Criterion
Authors: Malgorzata Bogdan (Purdue University, West Lafayette, IN, USA)
Primary area of focus / application:
Submitted at 8Sep2007 10:23 by
Accepted

Design of Experiments for Mean and Variance
Authors: Marta Emmett, Peter Goos, Eleanor Stillman
Primary area of focus / application:
Submitted at 8Sep2007 11:28 by
Accepted
mean of a single response variable under homoscedasticity. However, in many
practical applications the variance structure is not known and the variance, as
well as the mean, needs to be estimated. Estimating the mean and variance
simultaneously is particularly relevant in quality control experiments. The
first person to bring attention to the importance of reducing variability in
such experiments was Taguchi in the 1980s. Taguchi methods seek to design a
product or a process whose performance meets a specified target on average and
exhibits little variability. This variability may be a consequence of
environmental factors, controllable and uncontrollable factors during the
manufacturing process and component deterioration.
More recently, Atkinson & Cook (1995) and Vining & Schaub (1996) developed
optimal design theory for estimation of mean and variance functions
simultaneously. Both papers assume that the variance function is estimated
using the residuals of the regression function for the mean. However,
researchers often prefer using sample variances for quantifying and modelling
variation. This has the advantage that the responses of the variance function
do not depend on the specification of the mean function. If sample variances
are utilized, the optimal design approaches of Atkinson & Cook (1995) and
Vining & Schaub (1996) are no longer ideal. Therefore, building on the work of
Goos, Tack and Vandebroek (2001), we propose a new optimal design criterion for
the simultaneous estimation of mean and variance functions, where it is assumed
that sample variances are used for estimating the latter function.
References:
Atkinson, A.C. and Cook, R.D. (1995). Doptimum designs for heteroscedastic
linear models. Journal of the American Statistical Association, 90, 204212.
Goos, Peter, Tack, L., Vandebroek, M. (2001). Optimal designs for variance
function estimation in using sample variances. Journal of Statistical Planning
and Inference. 92, 233252.
Vining, G.G. and Schaub, D. (1996). Experimental designs for estimating both
mean and variance functions. Journal of Quality Technology. 28, 135147.

Improvement of a manufacturing process by integrated physical and numerical experiments: a casestudy in the textile industry
Authors: Stefano Masala (1), Paola Pedone (2), Martina Sandigliano (1) and Daniele Romano (2)
Primary area of focus / application:
Submitted at 8Sep2007 11:49 by
Accepted
in the product development phase. It is easy to forecast that it will spread soon also in less knowledgeintensive sectors.
However, although Design of Experiments and Computer Experiments provide sound methodologies for running experiments in
physical and numerical settings respectively, the integration between the two kinds of investigation is still in its infancy.
Yet in that case the sequential experimentation approach, introduced by George Box for physical experiments some fifty years
ago, would have an even wider scope.
The work describes the results of a research project which is currently taking place at Technova Srl, a medium size textile
firm in Sardinia (Italy). The company produces flocked yarn, a component which, after weaving, becomes a fabric for a wide
range of technical applications. Typical end products are coverings for seats and other components in car interiors. The
yarn is formed by finely cut fibers (flock) applied to an adhesive coated carrier thread by the electrostatic force. The
research focuses on the improvement of the manufacturing process. To this end, we exploit all kind of information sources
available, from historical production data to physical experiments on pilot and production machines and experiments on
different process simulators. We show that the results obtained by this approach are well beyond the initial expectations of
the company in terms of enhanced product quality as well as process economy and flexibility.
Keywords: DoE, Computer experiments, Sequential experimentation, Flocking process, Quality improvement.
Affiliations:
(1) Technova Srl, Olbia, martina.sandigliano@novafloor.it
(2) University of Cagliari, Dept. of Mechanical Engineering, Cagliari, romano@dimeca.unica.it

An automotive experience in applying DoE to improve a process
Authors: Laura Ilzarbe, M. Tanco, M. Jesús Alvarez, E. Viles
Primary area of focus / application:
Submitted at 8Sep2007 13:38 by
Accepted
In this paper we present the application of the design of experiments in a car manufacturing company to improve their technical knowledge of the laser welding process and the positive impact that this research already had on the number of defects observed.