ENBIS: European Network for Business and Industrial Statistics
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ENBIS17 in Naples
9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017The following abstracts have been accepted for this event:

Estimation of Variance Components and Use of Tolerance Interval for Accuracy Measure on Assay Qualification and Validation
Authors: Dan Lin (GSK), Bernard Francq (GSK), Walter Hoyer (GSK)
Primary area of focus / application: Quality
Secondary area of focus / application: Modelling
Keywords: Assay qualification and validation, Intermediate precision, Accuracy, Total error, Tolerance interval, Graphical user interface
Submitted at 6Mar2017 11:48 by Dan Lin
Accepted

Design of Experiments Used in Engineering Using Virtual Simulations
Authors: Bryan Dodson (SKF Group Six Sigma), Giacomo Landi (SKF USA Inc.), Rene Klerx (SKF Group Six Sigma)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Six Sigma
Keywords: Design of Experiments, Simulations, Engineering, Second order polynomials
Submitted at 6Mar2017 12:12 by Rene Klerx
Accepted

Clustering Time Series from Call Networks to Predict Churn
Authors: María Óskarsdóttir (KU Leuven), Tine Van Calster (KU Leuven), Bart Baesens (KU Leuven), Wilfried Lemahieu (KU Leuven), Jan Vanthienen (KU Leuven)
Primary area of focus / application: Mining
Secondary area of focus / application: Business
Keywords: Social network analytics, Time Series analysis, Time Series clustering, Call detail records, Churn
Submitted at 6Mar2017 12:20 by María Óskarsdóttir
Accepted
We propose a method to discover common behavior among churnprone telecom customers and to distinguish them from loyal customers. More precisely, we use call detail records (CDR) of the customers of a telecommunication provider to build call networks on a weekly basis over the period of six months. From each network, we extract features based on each customer’s connections within the network, resulting in individual time series of linkbased measures. In order to identify common behavior, we then apply time series clustering techniques and assign the customers to different groups at subsequent time points. Finally, we analyze how the customers move between the clusters to identify frequent patterns, especially amongst churners.
Our approach offers the possibility to discover behavioral patterns of potential churners, depending on the temporal aspect of phone usage as well as individual call networks. The result, once the patterns have been extracted, is a model that is simple in deployment and easily expandable. 
SelfStarting Control Charts and their Accurate RunLength Distributions
Authors: Yoshiaki Kunikawa (Tokyo University of Science), Seiichi Yasui (Tokyo University of Science)
Primary area of focus / application: Process
Keywords: Short run process, Average run length, Standard deviation of run length, Recursive control limits
In general, occurance of the outofcontrol signal for a certain plot is not independent of those for any other previous plot in selfstaring control charts. This is the reason why the data judged as incontrol is incorporated with the last control limits to judge the next plot. As a result, though simulation is used to determine appropriate control limits, we are able to analytically calculate control limits of proposed charts and Q type control charts.
In this paper, it is demonstrated to derive exact run length distributions for proposed control charts and Q type control charts (Startup Shewhart Xbar charts) which allow for proper control for the desired incontrol ARL, and the performance is evaluated. 
Statistical Standards and Open Source Software: Synergies and Challenges
Authors: Emilio L. Cano (University of CastillaLa Mancha), Matías Gámez (University of CastillaLa Mancha), Noelia García (University of CastillaLa Mancha)
Primary area of focus / application: Economics
Secondary area of focus / application: Quality
Keywords: Standardisation, Quality control and improvement, Statistical software, ISO standards, SPC, Good practice
Statistical methods for quality control and improvement are basically general statistical methods applied to industry. Thus, the use of base R in industrial environments should be natural. However, the lack of a sort of seamless Graphical User Interface for standardized tasks is a barrier to use R by nonstatisticallyskilled professionals. We know that this is not optimal, but it is also true that, in many cases, the statistical tools are just standardized procedures and it is enough for the sake of the business to interpret the outputs given the correct input.
In this work, standards from the ISO Technical Committee 69 (Applications of statistical methods) and AIAG are reviewed. An R infrastructure with the language and procedures of industry is proposed. The main result shows how Free and Open Source Software like R and its ecosystem can fill the existing gaps, leading to innovation in business and industry. Furthermore, international standards, well known and accepted by the industry, proves to be a catalyst for adopting R. 
Statistical Modelling, Semiconductor Process Optimization and Control: The Integrate Experience
Authors: Giuseppe Garozzo (STMicroelectronics)
Primary area of focus / application: Other: Monitoring and optimization of semiconductor processes
Keywords: European project, Semiconductor factory, Process control, Process modelling, Dry etching
Submitted at 6Mar2017 15:37 by Giuseppe Garozzo
Accepted
We report the main goal obtained by this synergy and in particular our experience on process optimization (i.e. data sampling reduction, cycle time improvement) and control (i.e. out of specification reduction, yield enhancement). Finally we present a case study where statistical inference and physical modelling can interact in order to improve efficiency in process development time and cost.