ENBIS-14 in Linz

21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014

My abstracts


The following abstracts have been accepted for this event:

  • A Cause and Effect Diagram and AHP Based Methodology for Selection of Quality Improvement Projects

    Authors: Ozlem Muge Aydın (Hacettepe University), Amir Shaygan (Hacettepe University), Erdi Dasdemir (Hacettepe University)
    Primary area of focus / application: Quality
    Secondary area of focus / application: Six Sigma
    Keywords: Cause and effect diagram, Analytical Hierarchy Process, Project selection, Six Sigma, Quality improvement
    Submitted at 30-May-2014 14:27 by Ozlem Testik
    Accepted (view paper)
    24-Sep-2014 11:15 A Cause and Effect Diagram and AHP Based Methodology for Selection of Quality Improvement Projects
    Project selection is an important component of many quality improvement initiatives, such as total quality management and six sigma. With the six sigma methodology, project selection gained more importance, where projects are selected, often considering bottomline results, at the Define stage of the DMAIC project management. Despite its importance, simple-to-implement analytical tools are needed for selecting suitable projects for improvement. In this paper, we propose to use a methodology based on the cause and effect diagram, integrated with the Analytical Hierarchy Process (AHP), where expert opinions are taken into account, to select projects based on expert and domain knowledge. The methodology combines the knowledge from both management and domain experts. An implementation of the methodology to hospital billing processes is illustrated. Since the hospital managers determined billing errors as one of the most costly problems in the hospital, we conducted a cause and effect diagram to identify the reasons of this problem. Weights of each cause are calculated by using AHP methodology, which is based on evaluating judgments of the field experts.
  • On the Effect of Prediction Uncertainty in the Design of New Pharmaceutical Products

    Authors: Pierantonio Facco (University of Padova)
    Primary area of focus / application: Quality
    Secondary area of focus / application: Process
    Keywords: Pharmaceutical product/process development, Latent variable modeling, Projection on latent structures, Uncertainty
    Submitted at 30-May-2014 14:45 by Pierantonio Facco
    Accepted (view paper)
    24-Sep-2014 09:20 On the Effect of Prediction Uncertainty in the Design of New Pharmaceutical Products
    In the last decade the pharmaceutical industry has been incentivized (FDA, 2004) to adopt holistic and science-based modeling strategies for a fast and inexpensive development of new products and for the design of the respective manufacturing processes. This promoted the use of multivariate statistical techniques as an effective tool to exploit the wealth of information available in historical databases. Several activities can benefit from this approach, such as process understanding, identification of critical process parameters and critical quality attributes, process monitoring, quality control, and the development of process analytical technologies.
    One very useful application of multivariate statistics in pharmaceutical product/process development is the inversion of the latent variables models (Jaeckle and MacGregor, 1998). In their direct form, latent variables models are usually utilized to explain the correlation between a set of input variables (e.g., raw materials characteristics, settings, process parameters, etc..) and a set of outputs (e.g., the target product quality profile), often with predictive purposes. In their inverse form, latent variables models are utilized in product and process development to suggest the most appropriate raw materials characteristics, settings, process parameters, etc… (i.e., the design parameters) that are expected to lead to a product of desired quality. Accordingly, these methodologies may drive developers and scientists to identify, within the knowledge space of the historical data, the design space within which experimental campaigns may be carried out (MacGregor and Bruwer, 2008).
    However, latent variable models are typically affected by uncertainty (e.g.: on calibration data, Faber and Kowalski, 1997; on the model parameters, Martens and Martens, 2000; on prediction), which should be considered in the exercise of model inversion for product formulation and process design.
    In this presentation a methodology is proposed to characterize how the prediction uncertainty (Zhang and Garcia-Munoz, 2009) backpropagates from the quality of a desired new product to the design parameters suggested by the model inversion. In particular, once the knowledge space is identified by means of a latent variable model that correlates the design parameters to the product quality based on a historical dataset of other products already manufactured, the proposed methodology segments the knowledge space to identify a subset of it that can be thought as a reasonable design space within which the experimental campaign may be carried out.
    The proposed methodology is tested in a typical pharmaceutical process, namely powder granulation.
    Department of Health and Human Services - U.S Food and Drug Administration, 2004. Pharmaceutical CGMPs for the 21st century — A risk-based approach. Final report.
    Faber, Kowalski, 1997. J. Chemom. 11, 181–238.
    Fernández Pierna, Jin, Wahl, Faber, Massart, 2003. Chemom. Intell. Lab. Syst. 65, 281–291.
    Jaeckle, MacGregor, J., 1996. Comput. Chem. Eng., European Symposium on Computer Aided Process Engineering-6 20, Supplement 2, S1047–S1052.
    MacGregor, Bruwer, 2008. J. Pharm. Innov. 3, 15–22.
    Martens, Martens, 2000. Food Qual. Prefer. 11, 5–16.
    Reis, Saraiva, 2005. AIChE J. 51, 3007–3019.
    Zhang, Garcia-Munoz, 2009. Chemom. Intell. Lab. Syst. 97, 152–158.
  • Case Presentation: Screening Experimentation on a Pilot Scale Oven for Factor Identification

    Authors: Søren Juhl Pedersen (DTU Food), Murat Kulahci (DTU Compute and Luleå University of Technology), Stina Frosch (DTU Food)
    Primary area of focus / application: Design and analysis of experiments
    Secondary area of focus / application: Education & Thinking
    Keywords: Screening, Statistical thinking, Engineering interpretation, Fractional factorial design
    Submitted at 30-May-2014 14:46 by Søren Juhl Pedersen
    23-Sep-2014 10:35 Case Presentation: Screening Experimentation on a Pilot Scale Oven for Factor Identification
    We present an experiment performed on a new pilot scale oven specifically built for elucidating the mechanisms in industrial baking of food by convection heating. The work was motivated by the manufacturer of the oven with which we were involved in a collaborative project concerning optimization of baking processes and equipment. The specific experiment in this study was motivated by two questions; one concerning a specific design variable’s influence on baking conditions and the second question concerning the evaluation of the efficacy of the current setup’s ability for experimentation. The case depicts an intricate relationship between the subject matter knowledge and statistical thinking in relation to both engineering research and consulting. The specifics concerning the design of the experiments, the constraints involved and how the experiments were performed is presented.

    The specific question posed by the company concerned how the distance between air inlet holes and baking sheet would influence the convective heat transfer. A fractional factorial design was used and a full fold-over performed in the follow-up experimentation. The factors / variables of the experiment were; speed of airflow, temperature, distance between inlet holes and baking sheet, band speed, amount of fresh air intake and restriction on airflow through inlet. The results comply with the theoretical expectations by highlighting airflow as the most important factor and the distance as a factor of minor influence but there was a caveat. The findings suggested a faulty design of the specific oven and also the measurement equipment.

    The results raised important questions for further experimentation concerning optimization of convective baking processes. One of the key learning experiences from the experiment was the valuable insight on process improvement gained by introducing statistical thinking in engineering applications and also introducing engineering knowledge into the framework of statistical process improvement. For example, the systematic data collection scheme through designed experiments generated the insight of equipment related issues that would not have been detected otherwise.
  • Modelling a Fragmentation Process of Printed Circuit Boards

    Authors: Antonio Pievatolo (CNR-IMATI)
    Primary area of focus / application: Modelling
    Secondary area of focus / application: Design and analysis of experiments
    Keywords: Electronic waste recovery, Population balance models, Markov process, Planning of experiments
    Submitted at 30-May-2014 16:52 by Antonio Pievatolo
    22-Sep-2014 15:40 Modelling a Fragmentation Process of Printed Circuit Boards
    We describe results obtained from experiments carried out in an innovative test industrial plant for the recovery of electronic waste, such as printed circuit boards (PCBs). PCBs that cannot be reworked are fragmented for the recovery of metals, some of which are rare. Fragmentation is modelled through a Markov process which describes the evolution of particle size distribution. Experiments are planned and executed for the estimation of the parameters of the Markvov process, so that it can be used as a model for predicting the output size distribution of particles as a function of the machine parameters, a piece of information which is to be used later for material separation.
  • Response Surface Approximation for Profile Monitoring in Circular Domains

    Authors: Esperan Padonou (Ecole des Mines de Saint-Etienne, STMicroelectronics), Olivier Roustant (Ecole des Mines de Saint-Etienne), Jakey Blue (Ecole des Mines de Saint-Etienne), Hugues Duverneuil (STMicroelectronics)
    Primary area of focus / application: Process
    Secondary area of focus / application: Quality
    Keywords: Profile monitoring, Statistical Process Control, Zernike regression, Gaussian process regression, Circular domains
    Submitted at 30-May-2014 17:04 by Esperan Padonou
    22-Sep-2014 16:20 Response Surface Approximation for Profile Monitoring in Circular Domains
    The production of Integrated Circuit (IC) is subject to high quality standard, and many control steps are incorporated in complex manufacturing processes. Conventionally, Statistical Process Control (SPC) tools such as control charts are intensively used for the sake of quality monitoring improvement in semiconductor production plants.

    The ICs are produced on thin slices of semiconductor materials, called wafers. In our study, a wafer is a 300-mm diameter circular domain. To monitor its quality, several types of physical metrology measurements (such as thickness, depth, width, angles and overlay) are collected on a fixed number of preselected locations. However, standard SPC techniques hardly detect defects such as curvature change, which is critical in semiconductors manufacturing, as different wafer “profiles”, related to different process issues, may have the same mean and variance over the measurement points. Furthermore, spatial correlation is not taken into account. To overcome these problems, the two-phase profile monitoring procedure [3] is often preferred:

    1. For each time step, fit a response surface based on the measurement points ;
    2. Monitor the response surface parameters over time.

    In this work, we focus on step 1. Our contributions are twofold. Firstly, we compare different approximation techniques in circular domains: Zernike regression [1, 4] , and Gaussian process regression [1, 2] with standard and customized covariance kernels. Secondly, we exhibit the link between the model and key process parameters.

    [1] G. Pistone and G. Vicario, Kriging prediction from a circular grid: application to wafer diffusion, A.S.M.B.I., 29(4), 350–361, 2013
    [2] C.E. Rasmussen and C.K.I. Williams, Gaussian Processes for Machine Learning, The MIT Press, 7–30, 2006.
    [3] W.H. Woodall, Current research on profile monitoring, Production, 17(3), 420-425.
    [4] F. Zernike, Diffraction theory of the cut procedure and its improved form, Physica, 1, 689–704, 1934.
  • Application of Kansei Engineering to Design an Industrial Enclosure

    Authors: Lluis Marco-Almagro (UPC Universitat Politécnica de Catalunya, Barcelona Tech), Xavier Tort-Martorell (UPC Universitat Politécnica de Catalunya, Barcelona Tech)
    Primary area of focus / application: Business
    Secondary area of focus / application: Consulting
    Keywords: Kansei engineering, Statistical engineering, Cluster analysis, Factorial designs, Ordinal logistic regression, Data visualization
    Submitted at 30-May-2014 18:34 by Xavier Tort-Martorell
    Accepted (view paper)
    23-Sep-2014 10:35 Application of Kansei Engineering to Design an Industrial Enclosure
    Kansei Engineering (KE) is a technique used to incorporate emotions in the product design process. Its basic purpose is discovering in which way some properties of a product convey certain emotions in its users. It is a quantitative method, and data is typically collected using questionnaires. Japanese researcher Mitsuo Nagamachi is the founder of Kansei Engineering. Products where KE has been successfully applied include cars, phones, packaging, house appliances, clothes or websites, among others.

    Kansei Engineering studies typically follow a model with three main steps: (1) spanning the semantic space: defining the responses, those emotions that will be studied; (2) spanning the space of properties: deciding on the technical properties of the products that can be freely changed and that might affect the responses (factors in a DOE factorial design) and (3) the synthesis phase, where both spaces are linked (that is, how each factor affects each response is discovered).

    In an earlier paper we claimed that KE is a good example of what Roger W. Hoerl and Ron Snee call statistical engineering: focusing not in advancement of statistics – developing new techniques, fine tuning existing ones – but on how current techniques can be best used in a new area. This presentation is a practical application of the ideas exposed there to the design of electrical enclosures.

    The presentation will show how well-known statistical methods (DOE, principal component analysis and regression analysis) are used together in conjunction with other non-statistical techniques and in the presence of practical real world restrictions to discover how different technical characteristics of the enclosures affect the selected “emotions”.