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Supply Chain Safety: A Diversification Model Based on Clustering

  • Andreas Brieden
  • Peter Gritzmann
  • Michael Öllinger
Part of the Lecture Notes in Logistics book series (LNLO)

Abstract

The issue of supply chain safety has received broad attention which has led to a wide range of methodologically different approaches; for a survey see (Pfohl, Köhler & Thomas, 2010). The present paper introduces a novel quantitative algorithm that provides a multiple covering of the commodity graph via constrained clustering. In fact, we construct supply chain components in the overall supply network of a company, each being able to account for some percentage of the company’s overall production. They are all isomorphic to and can hence be viewed as different realizations of the commodity graph which are most independent with respect to known hazards. Consequently, suppliers (of each level) are assigned to supply chain components so as to minimize the probability for a total (or severe enough) breakdown. The basic new model is given in detail, complemented by an outline of more involved ramifications that are able to deal with realistic scenarios. Also, we give computer simulations that indicate the favorable behavior already of our basic model in terms of risk reduction.

Keywords

Supply Chain Supply Network Supply Chain Network Supply Chain Performance Supply Chain Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreas Brieden
    • 1
  • Peter Gritzmann
    • 2
  • Michael Öllinger
    • 3
  1. 1.Inhaber der Professur für Statistik, insbesondere Risikomanagement (English: Statistics and Risk Management)Universität der Bundeswehr MünchenNeubibergGermany
  2. 2.Zentrum Mathematik, Lehrstuhl für Angewandte Geometrie und Diskrete Mathematik (English: Applied Geometry und Discrete Mathematics)Technische Universität MünchenGarchingGermany
  3. 3.Wissenschaftlicher Mitarbeiter an der Professur für Statistik, Insbesondere RisikomanagementUniversität der Bundeswehr MünchenNeubibergGermany

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