Lean optimization model for managing the yield of pima cotton (gossipier Barba dense) in small- and medium-sized farms in the Peruvian coast areas

  • Authors

    • Claudia Rojas Universidad Peruana de Ciencias Aplicadas (UPC)
    • Grimaldo Quispe Universidad Peruana de Ciencias Aplicadas (UPC)
    • Carlos Raymundo Universidad Peruana de Ciencias Aplicadas (UPC)
    2018-08-21
    https://doi.org/10.14419/ijet.v7i3.12721
  • Lean Manufacturing, Theory of Constraints, Linear Programming, Crop Yield Measurement

  • Abstract

    This study proposes a lean manufacturing-based optimization model to standardize “Pima cotton†crop yields in the Peruvian coast areas toward the north of Piura as the study area. The study also discusses how Pima cotton is grown in the Peruvian cost areas. This is represented in 2 stages: diagnosis and development of the cotton crop model. The diagnosis stage consists of 3 steps, and the development stage consists of 2 steps. The interrelation of each stage has been identified with the lean manufacturing principle for the improvement of crop yield. Results showed that to increase current crop yield in Piura to 172 bushels/ha as well as determine the quantities of resources and raw materials required considering a standardized crop production process. The limitations of the research depend on the climatic conditions in Peru. The main contribution of this research is to propose a model using which farmers may produce Pima cotton by utilizing the indicators proposed to increase the process control. In addition, the paper proposes a standardized crop production process that farmers must follow, supported by a mathematical model simulation.

     

     

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  • How to Cite

    Rojas, C., Quispe, G., & Raymundo, C. (2018). Lean optimization model for managing the yield of pima cotton (gossipier Barba dense) in small- and medium-sized farms in the Peruvian coast areas. International Journal of Engineering & Technology, 7(3), 1718-1724. https://doi.org/10.14419/ijet.v7i3.12721

    Received date: 2018-05-11

    Accepted date: 2018-07-18

    Published date: 2018-08-21