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)
    https://doi.org/10.14419/ijet.v7i3.12721

    Received date: May 11, 2018

    Accepted date: July 18, 2018

    Published date: August 21, 2018

  • 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.

  • References

    1. Abolhassani, A., Jaridi, M., 2016. Productivity enhancement in North American automotive industry: strategies and techniques to re-duce hours-per-vehicle. International Journal of Productivity and Per-formance Management, 65(8), pp. 1112 -1136. https://doi.org/10.1108/IJPPM-08-2015-0117.
    2. Bajjou, M.S., Chafi, A. y En-Nadi, A., 2017. A comparative study between lean construction and the traditional production system. In-ternational Journal of Engineering Research in Africa, 29, pp.118 -132. https://doi.org/10.4028/www.scientific.net/JERA.29.118.
    3. Chankov, S., Hutt, M.T. y Bendul, J., 2016. Synchroniza-tion in manufacturing systems: quantification and relation to logistics performance. International Journal of Production Research, 54(20), pp. 6033 –6051. https://doi.org/10.1080/00207543.2016.1165876.
    4. Costas, J., De La Fuente, D., Pino, R. y Puche, J., 2015. Applying Goldratt's Theory of Constraints to Reduce the Bullwhip Effect through Agent-Based Modeling. Expert Systems with Applications, 42(4), pp. 2049-2060. https://doi.org/10.1016/j.eswa.2014.10.022.
    5. Filho, T.A.R, Pacheco, D.A.J., Pergher, I., Roehe Vaccaro, G.L. y Antunes, J.A.V., 2016. A new approach for decision making in dis-tribution supply chains: A theory of constraints perspective. Interna-tional Journal of Logistics Systems and Management, 25(2), pp. 266 – 282. https://doi.org/10.1504/IJLSM.2016.078916.
    6. Garcés, D.A, Castrillón, O.D., 2017. Design of intelligent technolo-gy to identify and reduce downtime in a production system. Infor-mación Tecnológica, 28(3), pp. 157-170. https://doi.org/10.4067/S0718-07642017000300017.
    7. Golmohammadi, D., 2015. A study of scheduling under the theory of constraints. International Journal of Production Economics, 165(1), art. num. 6034, pp. 38-50.
    8. Gundogar, E., Sari, M. y Kokcam, A.H., 2016. Dynamic bottleneck elimination in mattress manufacturing line using theory of constraints. Springer Plus, 5(1), art. num. 1276. https://doi.org/10.1186/s40064-016-2947-1.
    9. Hilmola, O.P, Gupta, M., 2015. Throughput accounting and perfor-mance of a manufacturing company under stochastic demand and scrap rates. Expert Systems with Applications, 42(22), pp. 8423- 8431. https://doi.org/10.1016/j.eswa.2015.06.056.
    10. Horning, E.S., Pérez, D.T., 2016. An ant colony algorithm for scheduling flexible flowshop with sequencedependent setup times and total tardiness minimization. Ingeniare, 24(3), pp. 502-509.
    11. Jasti, N.V.K., Kodali, R., 2016. An empirical study for implementa-tion of lean principles in Indian manufacturing industry. Benchmark-ing, 23(1), pp. 183-207. https://doi.org/10.1108/BIJ-11-2013-0101.
    12. Khayrullina, M., Kislitsyna,O. y Chuvaev, A., 2015. Production sys-tems continuous improvement modelling. Quality Innovation Pros-perity, 19(2), pp. 73-86. https://doi.org/10.12776/qip.v19i2.576.
    13. Lee, W.C., Chung, Y.H. y Wang, J. Y., 2017. A parallel- machine-scheduling problem with two competing agents. Engineering Opti-mization, 49(6), pp. 962-975. https://doi.org/10.1080/0305215X.2016.1227615.
    14. Lowalekar, H., Ravi, R.R., 2017. Revolutionizing blood bank inven-tory management using the TOC thinking process: An Indian case study. International Journal of Production Economics, 186(1), pp. 89-122. https://doi.org/10.1016/j.ijpe.2017.02.003.
    15. Nagano, M.S., Da Silva, A.A. Y Nogueira Lorena, L.A., 2014. An evolutionary clustering search for the no-wait flow shop problem with sequence dependent setup times. Expert Systems with Applica-tions, 41(8), pp. 3628-3633. https://doi.org/10.1016/j.eswa.2013.12.013.
    16. Obeidat, M.S., Al-Aomar, R. y Pei, Z.J., 2014. Lean manufacturing implementation in the sewing industry. Journal of Enterprise Trans-formation, 4(2), pp. 151-171. https://doi.org/10.1080/19488289.2014.890980.
    17. Pérez – Campdesuñer, R., Pérez –Pravia, M., Sánchez-Rodríguez,A. García-Vidal, G. y Martínez-Vivar,R., 2017. Application of a meth-odology based on the theory of constraints in the sector of tourism services. Journal of Industrial Engineering and Management, 10(1), pp. 7-27. https://doi.org/10.3926/jiem.2089.
    18. Puche, J., Ponte, B., Costas, J., Pino, R. y De la fuente, D., 2016. Systemic approach to supply chain management through the viable system model and the theory of constraints. Production Planning and Control, 27(5), pp. 421-430. https://doi.org/10.1080/09537287.2015.1132349.
    19. Pugazhenthi, R., Xavior, M.A., 2014. Minimizing material pro-cessing time and idle time of a critical machine in a flow shop. Ad-vanced Materials Research, 984-985, pp. 106-110. https://doi.org/10.4028/www.scientific.net/AMR.984-985.106.
    20. Salazar Hornig, E., Ávila Thieme, C., 2017. GRASP approach for the unrelated parallel machines scheduling problem with makespan minimization and sequence dependent setup times. Ingeniare, 25(3), pp. 524-534.
    21. Sanjika, T.M., Bezuidenhout, C.N., 2016. A primary influence ver-tex approach to identify driving factors in complex integrated agri-industrial systems – an example from sugarcance supply and pro-cessing Systems. International Journal of Production Research, 54(2), pp. 4506-4519. https://doi.org/10.1080/00207543.2015.1064552.
    22. Soroush, H.M., 2015. Scheduling with job-dependent past-sequence-dependent setup times and job-dependent position-based learning ef-fects on a single processor. European Journal of Industrial Engineer-ing, 9(3), pp. 277-307. https://doi.org/10.1504/EJIE.2015.069341
    23. Tamizharasi, G., Kathiresan, S., 2014. Lean manufacturing in car-riage building press shop. World Applied Sciences Journal, 29(10), pp. 1333-1340.
    24. Thurer, M., Qu, T., Stevenson, M., Li, C.D. y Huang, G. Q., 2017. Deconstructing bottleneck shiftiness: the impact of bottleneck posi-tion on order release control in pure flow shops. Production Planning and Control, 28(15), pp. 1223-1235. https://doi.org/10.1080/09537287.2017.1362486.
    25. Valencia, J.E., Lamban, M.P.C., Royo, J.A.S., Díaz, J.A.M. y Can-to, J.M., 2014. A software development for establishing optimal pro-duction lots and its application in academic and business environ-ments. Ingeniería e Investigación, 34(3), pp. 81-86. https://doi.org/10.15446/ing.investig.v34n3.41578.
    26. Wang, H., Gong, Q. y Wang, S., 2017. Information processing struc-tures and decision-making delays in MRP and JIT. International Journal of Production Economics, 188(1), pp. 41-49. https://doi.org/10.1016/j.ijpe.2017.03.016.
    27. Şimşit, Z.T., Günay, N.S. y Vayvay, O., 2014. Theory of Con-straints: A Literature Review. Procedia – Social and Behavioral Sci-ences, 150, pp. 930-936. https://doi.org/10.1016/j.sbspro.2014.09.104.
    28. García, R. (22 de agosto del 2016). El algodón pima piurano: uno de los productos más olvidados de la región. Walac. Recuperado de http://walac.pe/el-algodon-piurano-es-olvidado-entre-los-agricultores-de-la-region/.
    29. Inga, C. (19 de setiembre del 2016). ¿Puede rescatarse el algodón peruano? El Comercio. Recuperado de https://elcomercio.pe/economia/dia-1/rescatarse-algodon-peruano-260578.
    30. Instituto Nacional de Innovación Agraria - INIA. (2008). Tecnología en Algodón. 2018, de Ministerio de Agricultura y Riego Sitio web: http://www.inia.gob.pe/tecnologias/cultivos/132-cat-tecnologias/cultivos/392-tecnologia-en-algodon.
    31. Redacción LR. (27 de octubre del 2017). Dirección de Agricultura pide no sembrar algodón a comienzos del 2018 por plagas. La Repú-blica. Recuperado de http://larepublica.pe/economia/1136848-direccion-de-agricultura-pide-no-sembrar-algodon-a-comienzos-del-2018-por-plagas.
    32. Bellido, Y., Rosa, A.L., Torres, C., Quispe, G., Raymundo, C., 2018. Modelo de Optimización de Desperdicios Basado en Lean Manufacturing para incrementar la productividad en Micro y Peque-ñas Empresas del Rubro Textil (2018) CICIC 2018 - Octava Confe-rencia Iberoamericana de Complejidad, Informatica y Cibernetica, Memorias, 1, pp. 148-153.
    33. Arévalo, J., Quispe, G., Raymundo, C., 2017. Sustainable Energy Model for the production of biomass briquettes based on rice husk in low-income agricultural areas in Peru. Energy Procedia, 141, pp. 138-145. https://doi.org/10.1016/j.egypro.2017.11.026

  • Downloads

  • 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 and Technology, 7(3), 1718-1724. https://doi.org/10.14419/ijet.v7i3.12721