ENBIS: European Network for Business and Industrial Statistics
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ENBIS-8 in Athens
21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008The following abstracts have been accepted for this event:
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A methods utilization model for the acquisition of information in logistics networks
Authors: Sonja Kuhnt, Sigrid Wenzel
Affiliation: Eindhoven University of Technology/ Institute of Production Engineering and Logistics, University of Kassel
Primary area of focus / application:
Submitted at 29-Apr-2008 09:45 by Sonja Kuhnt
Accepted
involve model-based analyses of the network. The usefulness of such an
analysis highly depends on the quality of the input data, which naturally
should capture the real circumstances best possible. We provide a methods
utilization model for a structured acquisition of information in this
context, which guides the user through the steps of a procedure model. It
further includes a methods utilization kid to allow for the integrative,
goal-oriented use of methods from different disciplines such as data
acquisition, visualisation and statistics. -
Exploratory data analysis in quality improvement projects
Authors: Jeroen de Mast
Affiliation: IBIS UvA
Primary area of focus / application:
Keywords: Discovery; Entropy; Graphical Data Analysis; Hypothesis Generation; Pattern Discovery.
Submitted at 29-Apr-2008 12:35 by Jeroen de Mast
Accepted
In this presentation I will show a number of explicated principles for EDA that can be taught to practitioners and statisticians to help them master this art faster. The framework is developed on the basis of a large number of real-life applications. The purpose and process of EDA are defined, and contrasted to the purpose and process of confirmatory data analysis and descriptive data analysis.
In the process of EDA, three steps are discerned: display the data, identify salient features, and interpret salient features. The details of each of these steps are elaborated, and I will present the underlying principles, such as Shewhart’s assignable causes, the maximum entropy principle, abduction, and explanatory coherence. Furthermore, the roles of probabilistic reasoning and automatic statistical procedures in EDA are discussed. Finally, I will place EDA in the wider context of hypothesis and idea generation, a discipline that is studied in philosophy of science (discovery), the cognitive sciences (problem solving), and the medical sciences (diagnosis). We will study what approaches for hypothesis generation there are besides EDA, and we will analyse how EDA compares to these other approaches.
The resulting framework provides structure and practical advice which facilitates teaching of EDA to practitioners and statisticians alike. The precise definitions, delineations and references to relevant scientific disciplines helps the further theoretical understanding and development of EDA. -
Desirability Analysis in Construction Design Quality Improvement
Authors: E.S. Telis, G. Besseris and C. Stergiou
Affiliation: Technological and Educational Institute of Piraeus
Primary area of focus / application:
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An Application of Design of Experiments in a Real Lift Test Rig
Authors: E. Viles M. Tanco I. Isasa U. Arteche X. Sagarzazu
Affiliation: TECNUN-University of Navarra & Ikerlan – IK4
Primary area of focus / application:
The test rig was a device specially designed to carry out the experiments on it. It was built in order to have results extremely correlated to the real situation. The device is 5 metres height, but only 2.3 metres is the useful trip for the lift. And it was constructed in order to make possible the modification of many variables, such as loads, speed, off-centres, guides narrowing, etc.
The experimental goal was to know about the influence of many factors (parameters) on the lift comfort. So the research team chose the Design of Experiments (DoE) techniques as the best techniques available to maximize the information that we would obtain from the experiments.
The first option was to analyze the influence of many factors using to screen a factorial fractional design. However, when we were planning the experiments aroused many complications which were dealt with different literature solution. We finally chose a split-plot design with two dummy factors simulating a three-level factor.
This article shows the steps carried out to plan and analyse the DoE problem, giving special importance to the planning steps along every phases of the DoE application. -
Spc tools for short production runs - overview and case study from Polish industry
Authors: Dr Eng. Agnieszka KUJAWIŃSKA, MSc. Eng. Michał ROGALEWICZ
Affiliation: Poznan University of Technology, Faculty of Mechanical Engeneering and Management, Institute of Mechanical Technology, Div. of Production Management
Primary area of focus / application:
The purpose of this article is to describe the control charts (e.g. difference-from-nominal) and indices for providing statistical control of a short-run process.
A case from Polish machining industry is presented. -
Optimal strip-plot experimental plans for two-stage batch processes
Authors: Heidi Arnouts (1), Bradley Jones (2), Peter Goos (1)
Affiliation: (1) University of Antwerp, (2) SAS Institute Inc.
Primary area of focus / application:
Submitted at 29-Apr-2008 16:05 by Heidi Arnouts
Accepted
randomization. Examples of designs with a restricted randomization are split-plot
and split-split-plot designs, which are commonly used in industry when some ex-
perimental factors are harder to change than others. Another, lesser known type
of experimental design plan is the strip-plot experimental design, also known as
the strip-block experimental design (Miller, 1997). Strip-plot congurations are an
economically attractive design option in situations where the process under inves-
tigation consists of two distinct stages, and it is possible to apply the second stage
to groups of semi-nished products from the rst stage. They have a correlation
structure similar to row-column designs and can be seen as special cases of split-lot
designs (Mee and Bates, 1998; Butler, 2004). In this contribution, we present an
algorithm for the construction of optimal strip-plot designs. Several examples will
be discussed.