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Abstraction and Modelling of Agri-Food Chains as Complex Decision Making Systems

  • Bryceson, Kim P.
  • Smith, Carl S.
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    Agri-food chains are complex systems involving multiple multifaceted firms usually working together within specific industry sectors (e.g. grains, beef, wool, dairy) to satisfy an increasingly globalised market demand for high value food products. In so doing, the groupings of companies involved in an agri-food chain undertake activities that require multidimensional inter-organisational and cross organisational decision-making in the process of adding value to a raw commodity product through the production, manufacturing and distribution stages of the chain. Additional complexity is added by climate variability which impacts randomly and unpredictably on decision making in every component of the chain. The work outlined in this paper is a pilot investigation looking at a number of approaches to conceptualising and modelling an agri-food chain and its related decision making processes to better evaluate the impact and effects of that decision making and associated information flows across the components of the agri-food chains. The modelling approaches were (i) a multimedia model initially explored as an opportunity to visualise supply and value chain issues for educational purposes; (ii) an agent based model (ABM) using deterministic rules to architecturally synthesise a supply chain, and (iii) a baysian belief network (BBN) which we discuss as an approach for looking at the likelihood of certain decisions being made under certain scenarios.

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    File URL: http://purl.umn.edu/49773
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    Paper provided by European Association of Agricultural Economists in its series 110th Seminar, February 18-22, 2008, Innsbruck-Igls, Austria with number 49773.

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    Date of creation: Oct 2008
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    Handle: RePEc:ags:eea110:49773
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    1. Michael Boehlje, 1999. "Structural Changes in the Agricultural Industries: How Do We Measure, Analyze and Understand Them?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(5), pages 1028-1041.
    2. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    3. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, June.
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