ENBIS: European Network for Business and Industrial Statistics
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ENBIS-14 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:
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Recent Developments in Gaussian Random Field Modeling and Their Application in Manufacturing
Authors: Enrique del Castillo (Penn State University)
Primary area of focus / application: Modelling
Keywords: Gaussian random field, Spatial data, Gaussian markov random function
Submitted at 19-May-2014 09:03 by Enrique del Castillo
Accepted
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Statistical Methods for Degradation Data with Dynamic Covariates and an Application to Outdoor Weathering Prediction
Authors: William Q. Meeker (Iowa State University)
Primary area of focus / application: Modelling
Secondary area of focus / application: Reliability
Keywords: Covariate process, Environmental conditions, Lifetime prediction, Organic coatings, System health monitoring, Usage history
Submitted at 19-May-2014 09:06 by Kristina Krebs
Accepted
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Phase I Analysis of Autocorrelated Time Series Data
Authors: Erdi Dasdemir (Hacettepe University), Christian Weiß (Helmut Schmidt University), Murat Caner Testik (Hacettepe University), Sven Knoth (Helmut Schmidt University)
Primary area of focus / application: Process
Secondary area of focus / application: Quality
Keywords: Autocorrelation, AR (1) process, Control chart design, Parameter estimation, Phase I, Outliers, ARL
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Using Multiblock PLS Models and Optimization for Rapidly Developing New Product Formulations
Authors: John F. MacGregor (McMaster University and ProSensus, Inc.)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Product development , Latent variables, DoE approaches for optimally augmenting databases, Optimization
In this presentation we discuss multivariate latent variable analysis methods for extracting information from the development databases, multivariate DoE approaches for optimally augmenting the databases, and optimization methods for rapidly formulating products that meet all the desired quality specifications at minimum cost.
Some examples of using this approach include the development of functional polymers for medical devices and for the core materials used in the very popular Srixon golf balls (now used by a large number of the tour pros), the development of novel high performance polymeric coatings, and the development of new food formulations. -
Measuring and Controlling Operator Differences in Data from a Skilled Work Process
Authors: Froydis Bjerke (Animalia Meat and Poultry Research Centre)
Primary area of focus / application: Quality
Secondary area of focus / application: Process
Keywords: Linear regression, Graphical displays, Sources of variance, Meat processing, Calibration of workers
In Animalia’s pilot plant, experienced operators produce data of cut yield (weights) and quality (measures) from individual carcasses by following specified cutting patterns. Throughout, the operators’ ID is also registered, so the data can be used to estimate individual but unwanted differences between operators. Ideally, operators should not contribute to explained variance in a yield model. However, when they do, the differences can be measured and corrective actions taken.
Eyield= b0 +b1inweight+b2class+b3fatgroup+dj(operator j)
Linear regression models are used to predict yield of prime cuts and trimmings from carcass parts, and ways of quantifying and dealing with operator differences are presented. Choice of response variables, that is, absolute vs. relative (meat to carcass) weight variables in the models are discussed, bearing in mind that the raw material (animal carcasses) is subject to biological variation, and that carcass assessments (class, fat group) are uncertain variables, too. -
Designing Sequential Batch Experiments with Application to Pharmaceutical Processes
Authors: Verity Fisher (University of Southampton)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Modelling
Keywords: Nonparametric modelling, Gaussian process modelling, Expected improvement, Optimisation
In the pharmaceutical industry, investigation using automated equipment is often carried out in sequential batches of experiments. Therefore, we present a batch-sequential extension to EGO. Comparisons are made to one-point-at-a-time sequential designs.