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Business Process Modelling in Demand‐Driven Agri‐Food Supply Chains

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  • Verdouw, Cor N.
  • Beulens, Adriaan J.M.
  • Trienekens, Jacques H.
  • Wolfert, Sjaak

Abstract

Agri‐food companies increasingly participate in demand‐driven supply chains that are able to adapt flexibly to changes in the marketplace. The objective of this presentation is to discuss a process modelling framework, which enhances the interoperability and agility of information systems as required in such dynamic supply chains. The designed framework consists of two parts: an object system definition and a modelling toolbox. The object system definition provides a conceptual definition of business process in demand‐driven supply chains from a systems perspective. It includes an application of the Viable Systems Model of Stafford Beer to supply chains, and classifications of business processes, control systems and coordination mechanisms. The modelling toolbox builds on the terminology and process definitions of SCOR and identifies three types of process models: i) Product Flow Models: visualize the allocation of basic transformations to supply chain actors and the related product flows from input material into end products (including different traceability units based on the GS1 Global Traceability Standard); ii) Thread Diagrams: visualize how order‐driven and forecast‐driven processes are decoupled in specific supply chain configurations (positions Customer Order Decoupling Points), and how interdependences between processes are coordinated; iii) Business Process Diagrams: depict the sequence and interaction of control and coordination activities (as identified in Thread Diagrams) in BPMN notation. The framework is applied to several agri‐food sectors, in particular potted plants and fruit supply chains. The main benefits are: i) It helps to map supply chain processes, including its control and coordination, in a timely, punctual and coherent way; ii) It supports a seamless translation of high‐level supply chain designs to detailed information engineering models; iii) It enables rapid instantiation of various supply chain configurations (instead of dictating a single blueprint); iv) It combines sector‐specific knowledge with reuse of knowledge provided by generic cross‐industry standards (SCOR, GS1).

Suggested Citation

  • Verdouw, Cor N. & Beulens, Adriaan J.M. & Trienekens, Jacques H. & Wolfert, Sjaak, 2010. "Business Process Modelling in Demand‐Driven Agri‐Food Supply Chains," 2010 Internatonal European Forum, February 8-12, 2010, Innsbruck-Igls, Austria 100477, International European Forum on Innovation and System Dynamics in Food Networks.
  • Handle: RePEc:ags:iefi10:100477
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    1. Teresa Serra & David Zilberman & José M. Gil, 2008. "Differential uncertainties and risk attitudes between conventional and organic producers: the case of Spanish arable crop farmers," Agricultural Economics, International Association of Agricultural Economists, vol. 39(2), pages 219-229, September.
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    Keywords

    Agribusiness; Agricultural and Food Policy; Farm Management; Food Consumption/Nutrition/Food Safety; Production Economics; Research Methods/ Statistical Methods;

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