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:

  • EWMA and CUSUM Charts on Transformed Variables

    Authors: Paulo Maranhão (Military Institute of Engineering), Eugenio Epprecht (Pontifical Catholic University of Rio de Janeiro)
    Primary area of focus / application: Process
    Secondary area of focus / application: Modelling
    Keywords: Assignable directions, Transformed variables, Shewhart charts, Multivariate processes
    Submitted at 31-May-2014 21:17 by Paulo Maranhão
    Accepted
    23-Sep-2014 15:20 EWMA and CUSUM Charts on Transformed Variables
    Most of the works that propose schemes of Multivariate Statistical Process Control (MSPC) and that analyze the performance of these schemes consider changes in the observed variables. In an earlier paper, we propose a method for the monitoring of multivariate processes, consisting of control charts on the transformed variables, in which changes in the process parameters are due to special causes that affect non-observable variables and occur in assignable directions. Besides, we compare its performance with that of Shewhart charts on the observed variables, on the principal components, and with that of T2 charts on the vector of observed variables. Results obtained by simulation show that the proposed scheme, has better performance in most of the cases analyzed. The comparisons were based on the in-control and out-of-control probabilities of signal. However, it is known that of Shewhart´s Chart loses efficiency for small shifts in the mean or variance. This work extends the study of control charts on the transformed variables for versions EWMA and CUSUM. The analysis of performance is done assuming shifts in the mean of the assignable directions, since these are associated to special causes, and/or increases of the variance in these same directions.
  • Systems Architectures to Cope with the Growth of Big Data

    Authors: Lance Mitchell (Greenfield Research and Allen Systems Group)
    Primary area of focus / application: Mining
    Keywords: Big Data, Systems architecture, Moore's law, Structure
    Submitted at 31-May-2014 22:40 by Lance Mitchell
    Accepted
    24-Sep-2014 11:15 Systems Architectures to Cope with the Growth of Big Data
    One key to understanding Big Data is to see the Big Data movement as a response to some current issues:

    1. Moore’s law is breaking down
    2. Current hardware/software architectures are reaching scalability limits
    3. New architectures change the economics
    4. Relational models don’t fit all problems

    The introduction will discuss the general recognition of demise of Moore’s law, and what this means in the context of Big Data analysis.

    This will be followed by proposals as to how we could structure our data and the underlying systems to take best advantage of the complexity and size of Big Data.

    These will include suggestions as to which Big Data architectural types are suitable for various uses of the data.

    Finally, some methods of monitoring and measuring the usage trends so that we may act proactively to cope with the evolving state of our data.
  • European Funding Session

    Authors: Alessandro Di Bucchianico (Eindhoven University of Technology), Rainer Goeb (Wuerzburg University)
    Primary area of focus / application: Business
    Keywords: European Union , Horizon 2020, Industry-University collaboration, Grants, Funding
    Submitted at 3-Jun-2014 10:21 by Alessandro Di Bucchianico
    Accepted (view paper)
    23-Sep-2014 10:15 European Funding Session
    In this session we will introduce the audience to the world of European Union funding and discuss the opportunities that EU funding offers to ENBIS and its members.
    The session starts off with a discussion of the importance of obtaining EU funding from various points of view (academia, industry, European Union). Then we will present an overview of the various funding programmes. We will explain the different goals of these funding programmes, show how to obtain the relevant information and discuss. The second part of the session will review previous participation of ENBIS in EU funded projects, in particular the proposal on “Analytics, monitoring, and decision making in environments of high-volume and complex data” in the COST framework submitted in April 2014. We will then proceed with ideas for acquiring new projects. We conclude the session with an open discussion with the audience about ideas and wishes for EU funded projects with ENBIS members.

    It will be important to embed statistical approaches into topics of wider interest from the perspective of science, society, technology, economy, and business management. The following topics seem particularly appropriate: risk management; business continuity management; data integration, big data; business intelligence and analytics, in particular supply chain analytics, customer relationship analytics; energy, in particular energy demand, energy security, energy infrastructure, electricity nets, renewable energy; counter-terrorism; cyber defence; environmental security, in particular health risks, climate change, water scarcity, disaster and catastrophe prevention, water and food security, protection of natural resources, sustainability; metrology; demography, in particular ageing population; healthcare; transportation, mobility, traffic.
  • Key Characteristics Enabler for Safe, Reliable and Robust Products in Aircraft Industry

    Authors: Sören Knuts (GKN Aerospace Sweden)
    Primary area of focus / application: Reliability
    Secondary area of focus / application: Six Sigma
    Keywords: Key characteristics, KC, QFD, FMECA, PFMEA, SPC, Variation risk management
    Submitted at 3-Jun-2014 10:36 by Sören Knuts
    Accepted
    22-Sep-2014 11:25 Key Characteristics Enabler for Safe, Reliable and Robust Products in Aircraft Industry
    Aerospace Standard AS9100 requires that Key Characteristics, KC, is defined by product development. However, since it is not well motivated in the standard, as well as requested by the Aircraft engine OEM, the interpretation of the use of Key Characteristics (KC) has not been correctly addressing safety criticality instead of sensitivity to variation. The sensitivity to variation is a clear scope of another standard AS9103.
    A recent Master Thesis at GKN Aerospace has studied the current knowledge and procedures to handle KC in the Product Development organization relative to the academic foundation of KC and Variation Risk Management , to recommend improvements for proactivity regarding KC definition.
    The aim with this article is to further expand recommendations and be more explicit on how GKN Aerospace will introduce a proactive work with QFD, Flow down diagram, FMECA, PFMEA, and SPC data to provide assets in achieving safe, robust and reliable products.
    Note: GKN Aerospace is a Tier 2 supplier in the Aerospace business with a large variety of components that is available on more than 90% of all new engines.
  • Data Driven Continuous Improvement by Six Sigma in Aircraft Industry

    Authors: Sören Knuts (GKN Aerospace Sweden), Hans-Olof Svensson (GKN Aerospace Sweden), Peter Hammersberg (Department of Materials and Manufacturing Technology, Chalmers University of Technology)
    Primary area of focus / application: Six Sigma
    Secondary area of focus / application: Quality
    Keywords: Key Characteristics, KC, Continuous Improvement, Six Sigma, Measurement System Analysis
    Submitted at 3-Jun-2014 10:41 by Sören Knuts
    Accepted
    23-Sep-2014 16:00 Data Driven Continuous Improvement by Six Sigma in Aircraft Industry
    Continuous improvements initiated by unsatisfactory output variation often require data or combinations of data from other up-stream variation sources than previously monitored. Three Six Sigma projects have been recently carried out in order to improve the process capability of welded components at GKN Aerospace. Common to these three projects are problems with the measurement systems and sub-sequent analysis related to the Key Characteristics (KC) evaluation. Parameters are traditionally monitored relative their individual requirements but not to the combined set of measures that form the KC. All three projects identified a need to establish a standardized procedure to develop data collection and analysis procedures relative specific KC based on downstream requirements in order to be able to evaluate KC baseline capability.
    The aim with this article is to recognize the development of an overall measurement system (containing probes, data collection routines and analysis procedures) as enabler of Continuous Improvement of downstream KC capability requirements.
    Note: GKN Aerospace is a Tier 2 supplier in the Aerospace business with a large variety of components that is available on more than 90% of all new engines.
  • An Engineering Approach to Ship Fuel Consumption Monitoring Using Regression Analysis

    Authors: Dario Bocchetti (Grimaldi Group S.p.a., Technical Department, Energy Saving Team), Antonio Lepore (University of Naples “Federico II”, Department of Industrial Engineering), Biagio Palumbo (University of Naples “Federico II”, Department of Industrial Engineering), Luigi Vitiello (University of Naples “Federico II”, Department of Industrial Engineering)
    Primary area of focus / application: Process
    Secondary area of focus / application: Quality
    Keywords: Vessel energy efficiency, Ship performance monitoring, Multiple regression model, Carbon credit
    Submitted at 6-Jun-2014 22:48 by Biagio Palumbo
    Accepted
    24-Sep-2014 10:35 An Engineering Approach to Ship Fuel Consumption Monitoring Using Regression Analysis
    The reduction of environmental impact imposed by Kyoto Protocol, the growth of competitiveness imposed by the shipping market and the rise in fuel price have urged shipping companies to pay increasing attention to ship energy efficiency improvement and CO2 emission reduction. According to the Ship Energy Efficiency Management Plan (SEEMP) recommended by the International Maritime Organization (IMO) a statistical approach is introduced to exploit the navigation information usually available on modern ships and to support management in decision-making.
    The approach is based on a multiple linear regression, which allows for both pointwise and interval predictions of the fuel consumption. For each voyage the model can be exploited to alert management for a possible change in ship performance in all those situations where the actual fuel consumption lies outside the prediction interval. Moreover, the proposed approach can be effectively utilized also to demonstrate the effectiveness of a specific efficiency improvement operation, which is particularly profitable for shipping companies and operators in order to claim for carbon credits.
    An application to real data collected by sensor network installed on two twin ships Ro-Ro-Pax sailing the same route shows the capability of the proposed method in decision-making.