Journal of CENTRUM Cathedra. Vol. 08, Issue 01

URI permanente para esta colecciónhttp://54.81.141.168/handle/123456789/194738

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JCC Volume 8 - Issue 1 - 2015
  • Adaptive Neuro-Fuzzy Optimization of the Net Present Value and Internal Rate of Return of a Wind Farm Project under Wake Effect Petković, Dalibor; 1
  • The Impossible Trinity and Financial Markets – An Examination of Inflation Volatility Spillovers Bosupeng, Mpho; 2
  • A Model-Based Method for Assessing Potentials in Distribution Logistics Becker, Julian; Fronia, Philip; Nyhuis, Peter; 3
  • Big Data Analytics Based on Logistical Models Nywlt, Johannes; Grigutsch, Michael; 4
  • Efficient Evaluation of Production Measures Based on Logistical Models Grigutsch, Michael; Nywlt, Johannes; 5
  • Model-Based Reorganization of Factory Structures Lübkemann, Jens; Richter, Lukas; Nyhuis, Peter; 6

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    Autorsearch.filters.applied.operator.equals: search.filters.author.Grigutsch, Michael

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      ÍtemAcceso Abierto
      Big Data Analytics Based on Logistical Models
      (Pontificia Universidad Católica del Perú. CENTRUM, 2015) Nywlt, Johannes; Grigutsch, Michael
      Over the past years, a change of feedback data in terms of quantity, quality, and timeliness could be observed in production. The generation of high resolution production feedback data enables producing companies to apply big data analytics in order to create competitive advantages. This paper describes how logistical models can be used to conduct big data analytics. It will be explained how such logistic-oriented big data analyses can be applied to improve the logistical performance of producing companies. The results will be illustrated with the help of a best practice project.
    • Miniatura
      ÍtemAcceso Abierto
      Efficient Evaluation of Production Measures Based on Logistical Models
      (Pontificia Universidad Católica del Perú. CENTRUM, 2015) Grigutsch, Michael; Nywlt, Johannes
      In the course of globalization, producing companies are confronted with new challenges to optimize their logistics performance based on logistical measures. In order to be able to create a quantifiable basis for the selection of optimal measures, a management tool was developed at the Institute for Production Systems and Logistics. The developed tool is based on logistical and transfer models for logistically and monetarily evaluating logistical measures. Based on implemented logistical measures, this paper focuses on the evaluation of improved logistics performance and its impact on sales.