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:

Design of Experiments for Scenario and Risk Assessment
Authors: Winfried Theis (Shell Global Solutions International B.V.)
Primary area of focus / application: Business
Secondary area of focus / application: Design and analysis of experiments
Keywords: Design of Experiments, Simulation, Scenario, Risk assessment
Submitted at 30May2014 20:11 by Winfried Theis
Accepted
We now created a tool that introduces statistical Design of Experiments into the process to drive the exploration of the complex models and to investigate the impact of the uncertainties on the stability of the optimal solutions. Essentially the DuU (Decisions under Uncertainty) tool runs in a staged process: in the first phase the most influential variables are determined by exploring the space by slightly change screening designs or spacefilling designs. In the second phase the design space is reduced to the most important variables found and then space filling designs are used to create a number of scenarios. When the question is about the described investment decisions over time several sorts of random processes can be applied. When there is no time component simple random distributions are used.
We will present the tool and show the insights based on one practical examples. 
SPC for Condition Monitoring Using Irregularly Sampled Data
Authors: Bjarne Bergquist (Luleå University of Technology), Peter Söderholm (The Swedish Transport Administration)
Primary area of focus / application: Reliability
Keywords: Statistical process control (SPC), Irregular sampling, Interpolation, Linear assets, Maintenance
Submitted at 30May2014 21:37 by Bjarne Bergquist
Accepted

Process Capability Indices for Bounded Distributions: The DryEtching Semiconductor CaseStudy
Authors: Riccardo Borgoni (Bicocca University of Milan), Laura Deldossi (Cattolica University of Milan), Diego Zappa (Cattolica University of Milan)
Primary area of focus / application: Quality
Keywords: Capability indices, Rational subgroups, Spatial monitoring grid, Asymmetric tolerances
Submitted at 30May2014 23:25 by Diego Zappa
Accepted
Most of the references refers to processes with two sided specification limits but in many applications only a one sided specification limit is necessary.
That is the case of the so called dry etching phase in semiconductor manufacturing processes.
Lovelace and Swain (2009), Albing and Vännman (2009) have proposed appropriate generalization of the most common indices by assuming in the first case that data come from a truncated (from below) lognormal distribution, while in the second paper a parametrically more flexible index is proposed along with a test of significance. The latter proposal is attractive because of its flexibility but it suffers of some problems in the estimation of key parameters, while the former benefits of a somehow simple parametric assumption.
We will mainly consider the first proposal for a couple of reasons. First, on the basis of a Montecarlo simulation the authors assure that the proposal is fairly robust to departure from lognormality. Second in their proposal it is admitted the case of truncation and the key role of rational subgroups.
That is our case: since chipsets are built by splitting appropriately wafers into smaller pieces, process capability indices should reflect the precision of the production at the wafer stage. Differences among wafers are reflected by averaging precision of the production process, including specific factors that for the sake of capability computation are assumed not present into the single unit.
The uniqueness of this case study is that when data are filtered above a prefixed threshold, all the wafers with at least one measurement below it, cannot be used as rational subgroups and should be excluded from the computation of the capability indices.
That strategy may cause the rejection of many wafers depending on the value of the threshold chosen.
To avoid that we propose to compute sample statistics for each subset of a 9 sample grid For each possible threshold and each combination, boxplots are displayed Capability is computed conditionally to the dimension of the sample grid by averaging the indices with the dimension of the rational subgroups used for the computations. 
RuntoRun Profile Monitoring on Sensor Temporal Data for Tool Condition Diagnosis
Authors: Jakey Blue (École des Mines de SaintÉtienne), Jacques Pinaton (STMicroelectronics), Agnès Roussy (École des Mines de SaintÉtienne)
Primary area of focus / application: Process
Secondary area of focus / application: Reliability
Keywords: RuntoRun variation, Profile monitoring, SPC (Statistical Process Control), Tool condition hierarchy, Tool fault diagnosis
In 2012 a hierarchical tool condition monitoring scheme is proposed for efficient detection and diagnosis of tool faults [1]. The overall tool condition in the hierarchy provides a general view of tool behavior. The hierarchy then enables an intuitive breakdown of the detected anomaly in the overall tool condition into the sensor groups. However, to make the hierarchy more applicable, the drilldown diagnosis should be extended to the sensor level [2]. Therefore a temporal runtorun variation at sensor level is developed in this research.
The idea of runtorun variation is to capture the local changes (variances) between two consecutive wafers from time to time. It contains four critical steps to complete:
1. process recipe classification;
2. FDC temporal profiles synchronization;
3. runtorun variation calculation; and
4. SPC monitoring on R2R variations.
Through the case validation with real data collected from our industrial partner, the R2R variation indeed enables correlating tool faults with single sensor effectiveness. By combining the proposed R2R variation with the tool condition hierarchy, tool condition monitoring now becomes efficient and tool fault diagnosis can be systematically topdown.
[1] J. Blue, D. Gleispach, A. Roussy, and P. Scheibelhofer, “Tool Condition Diagnosis With a RecipeIndependent Hierarchical Monitoring Scheme,” IEEE Transactions on Semiconductor Manufacturing, vol. 26, no. 1, pp 8291, 2013.
[2] J. Moyne, N. Ward, and P. Hawkins, “Leveraging Advanced Process Control (APC) Technology in Developing Predictive Maintenance (PdM) Systems,” in Proceedings of 24th Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, USA, May, 2012, pp. 221226. 
Makin’ ‘em Do DoE…
Authors: Stefanie Feiler (AICOS Technologies AG), Philippe Solot (AICOS Technologies AG)
Primary area of focus / application: Education & Thinking
Secondary area of focus / application: Design and analysis of experiments
Keywords: Education, Consulting, Statistical Design of Experiments (DoE), Quality, Psychology
So what can you do if you want to promote the use of DoE? What are the psychological barriers to overcome? Which strategies can be used for convincing somebody to try out DoE? How to sell education in DoE and statistics?
The aim of the talk is to provide a thorough overview of different approaches for advertising and implementing DoE – but we are in particular looking forward to a lively discussion where you can share your own experience!
Reference: The starting point of the talk is a discussion on this topic on LinkedIn, initiated by ENBIS member Phil Kay. 
Customized Fetal Growth Modeling and Monitoring  A Statistical Process Control Approach
Authors: Diamanta BensonKarhi (The Open University of Israel), Haim Shore (BenGurion University of the Negev), Maya Malamud (BenGurion University of the Negev), Asher Bashiri (Soroka University Medical Center and BenGurion University of the Negev)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Customized fetal growth modeling and monitoring, Least absolute deviation, Nonlinear profiles, Response modeling methodology, Short runs, Statistical process control
Submitted at 31May2014 09:49 by Diamanta BensonKarhi
Accepted
developed to detect early deviations of fetal biometry from expected normal growth. Using response modeling methodology (RMM), a fetal growth model is dynamically estimated and integrated in a regressionadjusted SPC control scheme, based on a new median control chart and a control chart for residuals variation. Hadlock’s reference centiles are also integrated in the monitoring scheme. Longitudinal data from normal pregnancies and
those with adverse medical outcomes have been analyzed. Results show that nonsmooth growth trajectory, expressed in exceptionally large absolute deviations from predicted median values, is a good precursor to possible
prenatal adverse outcomes.