ENBIS: European Network for Business and Industrial Statistics
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ENBIS14 in Linz
21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014The following abstracts have been accepted for this event:

The Challenges of PCABased Statistical Process Monitoring: An Overview and Solutions
Authors: Eric Schmitt (KU Leuven), Bart De Ketelaere (KU Leuven), Tiago Rato (University of Coimbra), Marco Reis (University of Coimbra)
Primary area of focus / application: Process
Keywords: Control charts, TimeDependent data, HighDimensional data, Principal component analysis, Methodology implementation
Submitted at 31May2014 12:24 by Eric Schmitt
Accepted

The Use of Statistical Improvement Methods in Swedish Organizations
Authors: Peder Lundkvist (Luleå University of Technology), Bjarne Bergquist (Luleå University of Technology), Erik Vanhatalo (Luleå University of Technology)
Primary area of focus / application: Education & Thinking
Secondary area of focus / application: Economics
Keywords: Statistical methods, Statistical process control, Capability analysis, Design of Experiments, Survey, Swedish organizations
Submitted at 31May2014 12:31 by Erik Vanhatalo
Accepted
Our survey targeted alumni from the Master’s program in Industrial and Management Engineering at Luleå University of Technology (LTU) who are familiar with the methods from undergraduate courses. The respondents were asked to assess the use of the methods in their last two workplaces. In total 147 responses were obtained, giving a response rate of 36 %. These respondents assessed the use of the methods in 211 organizations in total.
The results indicate that the use of statistical methods in Swedish organizations has increased somewhat compared to earlier studies, and in particular, the use has become more systematic. Organizations larger than 250 employees have a relatively higher use of the methods compared to smaller organizations and their use is generally higher in industry compared to the service sector. The results clearly show that SPC is the most commonly used of the three methods and that DoE is the least used method. Furthermore, the most important aspects that are reported to hinder the use of the methods are insufficient resources in terms of time and low commitment of top and middle managers. 
Optimal Design of Discrete Choice Experiments with Partial Proﬁles
Authors: Daniel Palhazi Cuervo (University of Antwerp), Peter Goos (University of Antwerp, University of Leuven), Roselinde Kessels (University of Antwerp), Kenneth Sörensen (University of Antwerp)
Primary area of focus / application: Design and analysis of experiments
Keywords: Discrete choice experiments, Partial profiles, Doptimality criterion, Utilityneutral designs, Locally optimal designs, Bayesian optimal designs
Submitted at 31May2014 18:03 by Daniel Palhazi Cuervo
Accepted

Process Improvement via Design of Experiment: Applicative Examples in Technological Field
Authors: Flaviana Tagliaferri (University of Naples Federico II), Biagio Palumbo (University of Naples Federico II), Martin Dix (Chemnitz University of Technology), Michael Kuhl (Fraunhofer  IWU)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Quality
Keywords: Design of Experiment (DoE), Laser Assisted Machining (LAM), Water abrasive jet, Ceramic machining
Submitted at 31May2014 20:19 by Biagio Palumbo
Accepted
The efficacy of this approach is demonstrated here by three applicative examples in technological field, developed at Chemnitz University of Technology where the integration between statistical and technological knowledge has allowed: to characterize and to optimize some manufacturing processes; to catalyze the innovation process; to promote the technological transfer. Moreover this approach allows to put into action a virtuous cycle of sequential learning.
An accurate predesign (i.e. preexperimental planning phase) is the solid basis on which a statistical approach has to be built. The use of the predesign guide sheets provides a way to systematize the process by which an experimentation team does the experimental plan. In fact, these sheets drive the experimenter to clearly define the objectives and scope of an experiment and to gather information needed to design an experiment. 
Learning from Mistakes: Using Problems with a Small, Real Experiment to Teach Principles of DoE
Authors: Jacqueline Asscher (Kinneret College on the Sea of Galilee)
Primary area of focus / application: Education & Thinking
Secondary area of focus / application: Design and analysis of experiments
Keywords: Design of Experiments (DoE), Training, Teaching, Project based, Problems
Submitted at 31May2014 20:25 by Jacqueline Asscher
Accepted
The design of the experiment is identical for all participants, a simple twoway ANOVA with two levels for each factor and four replicates.
The assignment is deliberately vague, leaving the course participants to decide how to choose materials, measure the burn time and organize the 16 runs. Typically problems arise: for example: "we'll put the red candles in the freezer and leave the yellow ones at room temperature"; "who knew these candles would burn so long – I want to go to sleep"; "I ran the experiment at dusk, turned the light on in the middle and forgot that the ceiling fan turns on with the light"; "the candles surrounded by other candles burned faster".
This small experiment is useful for teaching the importance of a pilot experiment, as a point of reference for teaching the principles of DoE such as blocking, randomization and interaction, as a review of the material from a previous course in basic statistics and as preparation for a larger real experiment run by participants later in the course. 
Hidden Markov Model for the Detection of a Degraded Operating Mode of Optronic Equipment
Authors: Anne GégoutPetit (Université de Lorraine), Camille Baysse (Thales Optronique), Saracco Jérôme (INRIA Bordeaux)
Primary area of focus / application: Reliability
Keywords: HMM, Degradation, Detection, Reliability
Submitted at 31May2014 20:53 by Anne GégoutPetit
Accepted
This one is modelled by a Markov chain and the cdt is a noisy function of it. Thanks to filtering equations, we obtain results on the probability that an appliance is in degraded state at time t, knowing the history of the Tmf until this moment.
We have evaluated the numerical behavior of our approach on simulated data.
This method can be implemented in a HUMS (Health and Usage Monitoring System) and used to propose adaptive maintenance conditional to the estimated state.