ENBIS-7 in Dortmund

24 – 26 September 2007

My abstracts


The following abstracts have been accepted for this event:

  • Economical aspects of training Six Sigma

    Authors: Michal Tkác; (University of Economics Bratislava, Kosice, Slovak Republic)
    Primary area of focus / application:
    Submitted at 10-Sep-2007 11:11 by
    Costs of training, length of the training program, benefits from training, number of trained employees and other factors embody economical aspects of training Six Sigma. Mix influence of these factors and their management is the main topic of our paper. We are offering an analysis of a proposed business model of training Six Sigma Green Belts, which argues well-known questions of economical aspects of training programs. We will discuss how to measure the performance of training activities: the efficiency of investments into training of employees (from the financial as well as from qualitative perspective). How, our business model propagates the training of Six Sigma Green Belts and finally, how can we solve some antagonistic problems, when designing training programs.

    Key words: Education, Six Sigma Training, Return On Investment.
  • Brand Loyalty and Mixture Regression Models: Segmenting Customers in Jeans Market

    Authors: Ana Oliveira Brochado (1), Francisco Vitorino Martins (2) and Paula Cristina Rodrigues (3)
    Primary area of focus / application:
    Submitted at 10-Sep-2007 11:23 by Ana Brochado
    Brand value (BV) has been widely studied in recent years. The popularity of this subject
    is mainly due to its importance concerning strategic decisions as: differentiation,
    profitability and competitiveness of organizations, namely those evolving in an
    industrial environment.

    We intend to analyse loyalty - a brand-value effect, and test if the intangible value of a
    brand is (empirically) an effective determinant of BV, or, instead, if the only
    determinants of BV are tangible variables, as quality or notoriety.

    As the fashion industry (jeans) and their customers are very sensible to intangible
    factors, we use as explanatory variables brand personality and store image, despite the
    most classical determinants were brand personality and perceived quality.

    To evaluate the relevance of the intangible factors in explaining brand loyalty we
    consider the latent variable personality, witch were constructed based on the 15 items,
    proposed by the seminal article of Aaker.

    We intend to construct segments of customers based on brand loyalty and that present
    different responsiveness to the explanatory variables. As the dependent variable - brand
    loyalty - is a binary variable, we use a logistic mixture regression model.

    We collected information from 500 customers when they were shopping. We study 5 of
    the main brands of the jeans industry. Scales used in the questionnaire were the first
    factor analysed before the mixture study. Some managerial implications were drawn.

    Keywords: Brand-Value, Brand Loyalty, Brand Personality, Brand Quality, Fashion Industry, Jeans, Logistic Mixture Regression

    (1) Instituto Superior Técnico, Department of Civil Engineering and Architecture, Av. Rovisco Pais, 1049-
    001 Lisboa, Portugal, E-mail: abrochado@civil.ist.utl.pt

    (2) Universidade do Porto, Faculdade de Economia, Rua Dr. Roberto Frias
    4200-464 Porto, Portugal,E-mail: vmartins@fep.up.pt

    (3) Universidade Lusíada do Porto, Faculdade de Ciências Económicas e de Empresa, Rua Dr. Lopo de Carvalho,4369-006 Porto, Portugal, E-mail: 23010380@por.ulusiada.pt

    Specifics: poster presentation
  • A Two-Sided Multivariate p Control Chart

    Authors: Paolo Cozzucoli
    Primary area of focus / application:
    Submitted at 10-Sep-2007 11:29 by
    We assume that the operator is interested in monitoring a multinomial process, that is the items are classified into (k+1) ordered distinct and mutually exclusive categories; specifically, the first category is used to classify the conforming items, while the remaining k categories are used to classify the nonconforming items in k defect grades, with increasing degrees of nonconformity. Usually the process is said to be capable if the proportion of nonconforming items is very small and remains low, or declines, over time. In this case, because we have chosen to classify the nonconforming items into k defect grades, the overall proportion of nonconforming items depends on the k categories, which are not necessary independent, and we are interested in evaluating over time the proportion of nonconforming items in each category as well as the overall (across the k categories) proportion of nonconforming items. To achieve this goal, in this paper we propose i) a normalized index that can be used to evaluate the capability of the process and ii) a two sided Shewhart-type multivariate control chart with probabilistic limits to monitor the overall proportion of nonconforming items. In addition, we suggest a solution to the identification problem when an out control signal takes occurs. The same sample statistic is used to define the normalized index and the multivariate p control chart.
  • A practical experience with Corrugation height production process improvement using an SPC

    Authors: Erik Mønness, Hedmark University College. Matt Linsley, ISRU University of Newcastle.
    Primary area of focus / application:
    Submitted at 10-Sep-2007 12:29 by
    A product is corrugated into a zigzag. The corrugated profile height is considered to be a critical to quality factor.

    The monitoring regime in place was to record corrugation profile height (always in groups of four) at morning startup, and then after any break or after any maintenance. Thus there is always at least one recording a day but typically two to three but could be more. The plan was called “First article inspection”. Originally the plotting unit in the SPC was “day”, thus the control limits would vary with observation number. Also, the control limits were updated each month.

    We first advised using each recording (with four measurements) as plotting unit thus avoiding varying control limits. However, we experienced that 1) With this regime the process appeared to be out of control more often, and 2) The variation (estimated sigma) within day was twice the variation within a group-of-four. We fancied that the monitoring regime may not fully monitor the process; perhaps it was monitoring the effect of the interventions more than the running production.

    We therefore advised a one month experiment where recordings where taken each hour in addition to the current regime.

    The experiment revealed that the current regime had a much smaller variation than the variation of experimental data.

    We concluded that we still lack insight into the variation of the process, this should be further investigated. We recommended taking three daily recordings at stated hours in addition to the current regime. We recommended monitoring monthly variation to search for any significant changes of the process.
  • A practical guide to design conjoint experiments

    Authors: Roselinde Kessels*, Bradley Jones°, Marissa Langford* and Tim Clapp*
    Primary area of focus / application:
    Submitted at 10-Sep-2007 12:53 by
    * North Carolina State University, College of Textiles, Box 8301, 2401 Research Drive, Raleigh, NC 27695-8301. roselinde_kessels@ncsu.edu; marissa_langford@ncsu.edu; tclapp@ncsu.edu ° SAS Institute Inc., SAS Campus Drive, Cary, NC 27513. bradley.jones@jmp.com

    Understanding the Voice of the Customer (VOC) is a critical first step in developing a successful product or service. If a company can precisely predict customer preferences and needs, it has a competitive advantage to launch innovative products or services that lead to an increase in customer base. A popular way to predict people's choices for prospective goods is the use of conjoint experiments. In a conjoint experiment, respondents usually rate a set of goods on a scale. These goods are presented as profiles or alternatives of combinations of different component attributes. The usefulness of the predictions resulting from the analysis of the experimental data depends on the profiles and the number of test persons used. Also, the assignment of the profiles to the subjects plays a key role. To maximize the information gained you need an efficient experimental design. In this talk, we will show how to properly design conjoint surveys for both main-effects and interaction-effects models.
  • Standards in control of variability

    Authors: Jan M. Myszewski, Leon Kozminski Academy of Entrepreneurship, Warsaw, Poland
    Primary area of focus / application:
    Submitted at 10-Sep-2007 13:15 by
    The text deals with basic schemes of controlling variability

    We show that the main method of controlling variability in man driven systems consists in a use of standards – patterns of systems and processes. Imitation of a pattern is a way to achieve more repeatable course of operation and therefore more repeatable effect thereof.

    Scope of standards with regard to their content and domain of application is very wide – some of them are generic, used by broad groups of users and some are personal – used by single persons who are their inventor. Use of some standards may be obligatory (law system) and use of some of them may be optional (science system).

    Standards used in organisation reflect a knowledge that belongs to organization. A set of corporate standards includes various know-hows and instructions how to control a variability. Some elements of the knowledge are documented and therefore can be controlled. The rest remains in brains of members of organisation. This knowledge is accessible as long as they are in organisation

    Organisation should take care that corporate standards are being continually improved – improvement of standards is a way to improve organisational performance

    There are several models of standards systems – like ISO 9000 or EFQM Business Excellence Model. There are also schemes used to improve standards used in organization such complex as Six Sigma or such simple as Problem Solving algorithm. The Shewhart-Deming PDCA Cycle is a general algorithm of monitoring standard’s improvement

    There is a natural relation between standards and variability:

    standards used in organization represent corporate knowledge how to operate against factors producing variability.

    Variability (a differentiation which has causes not known to observer) represents those phenomena in organisational processes that are not controlled and fall beyond the corporate knowledge.