Systemic Practice and Action Research

, Volume 27, Issue 3, pp 287–306 | Cite as

Foreseeing Iron Ore Prices Using System Thinking and Scenario Planning

  • Maria Isabel Wolf Motta Morandi
  • Luis Henrique Rodrigues
  • Daniel Pacheco LacerdaEmail author
  • Isaac Pergher
Original Paper


In competitive and complex markets such as the commodities one, having the ability of planning scenarios and envisioning the behavior of prices in each of them, constitutes a competitive advantage for organizations. Within this context, this research aims at developing a method for the understanding of the key factors that impact the pricing of commodities through a systemic approach, allowing the estimation/evaluation of future prices in different scenarios. Action research was used as research methodology, which is characterized by the principles of collaborative and continuous learning. In order to implement the objectives of this research, an adaptation of the Systems Thinking and Scenario Planning method was developed and implemented in an industry of the iron ore market. The first phase led to the understanding of the main variables associated to the price; in the second phase, this learning was materialized in a system dynamic computational model, which was used in the third and final stage for the estimation/evaluation of future prices in some alternative scenarios. Interviews were conducted in parallel with the quantitative evaluation, in order to obtain subsidies for acceptance and refinement of the proposed method. The results obtained, the learning reported and the possible applications identified for the use of the model indicate that the proposed method allows for the understanding of the pricing of commodities through the systems thinking and the planning by scenarios.


Systems thinking Scenario planning Pricing Commodities 



We thank the National Counsel of Technological and Scientific Development (CNPq), an agency of the Brazilian Government, for supporting and encouraging the research.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Maria Isabel Wolf Motta Morandi
    • 1
  • Luis Henrique Rodrigues
    • 1
  • Daniel Pacheco Lacerda
    • 1
    Email author
  • Isaac Pergher
    • 2
  1. 1.Research Group on Modeling for Learning – GMAP|UNISINOS, Production and System Engineering Post-Graduation Program – PPGEPS/UNISINOSUniversidade do Vale do Rio dos Sinos – UNISINOSSão LeopoldoBrazil
  2. 2.Master’s Degree in Production Engineering and Systems – PPGEPS/UNISINOSCentro Universitário UnivatesLajeadoBrazil

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