Visión sistémica del análisis de la flexibilidad en cadenas de suministro de productos perecederos
DOI:
https://doi.org/10.18046/syt.v12i30.1858Palabras clave:
Capacidad, cadena de suministro, flexibilidad de volumen, productos perecederosResumen
La flexibilidad de las cadenas de suministro está determinada por la capacidad de respuesta en términos de volumen y variedad ante cambios en los comportamientos de los consumidores. En el presente estudio se evalúa para una cadena de suministro, una política de flexibilidad de volumen y su relación con un factor de desperdicio inherente en la distribución de un producto perecedero. Mediante Dinámica de Sistemas, se analiza la distorsión en la información de demanda dada por el tipo de producto y se evalúan las implicaciones de la decisión de flexibilidad sobre el nivel de servicio brindado al cliente final.Referencias
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Kamath, N. B., & Roy, R. (2007). Capacity augmentation of a supply chain for a short lifecycle product: A system dynamics framework. European Journal of Operational Research, 179(2), 334–351.
Karakaya, S., & Bakal, İ. S. (2013). Joint quantity flexibility for multiple products in a decentralized supply chain. Computers & Industrial Engineering, 64(2), 696–707.
Kumar, S., & Nigmatullin, A. (2011). A system dynamics analysis of food supply chains – Case study with non-perishable products. Simulation Modelling Practice and Theory, 19(10), 2151–2168.
Li, R., Hongjie, L., & Mawhinney, J. (2010). A Review on Deteriorating Inventory Study. Journal of Service Science and Management, 03(01), 117–129.
Lloréns, F. J., Molina, L. M., & Verdú, A. J. (2005). Flexibility of manufacturing systems, strategic change and performance. International Journal of Production Economics, 98(3), 273–289.
Merschmann, U., & Thonemann, U. W. (2011). Supply chain flexibility, uncertainty and firm performance: An empirical analysis of German manufacturing firms. International Journal of Production Economics, 130(1), 43–53.
Minegishi, S., & Thiel, D. (2000). System dynamics modeling and simulation of a particular food supply chain. Simulation Practice and Theory, 8(5), 321–339.
Moon, K. K.-L., Yi, C. Y., & Ngai, E. W. T. (2012). An instrument for measuring supply chain flexibility for the textile and clothing companies. European Journal of Operational Research, 222(2), 191–203.
Nita.H. Shah. (1993). Probabilistic time-scheduling model for an exponentially decaying inventory when delays in payments are permissible. International Journal of Production Economics, 32(1), 77–82.
Oh, S., Ryu, K., & Jung, M. (2013). Reconfiguration framework of a supply network based on flexibility strategies. Computers & Industrial Engineering, 65(1), 156–165.
Padmanabhan, G., & Vrat, P. (1995). EOQ models for perishable items under stock dependent selling rate. European Journal of Operational Research, 86(2), 281–292.
Pathak, S. D., Dilts, D. M., & Biswas, G. (2007). On the evolutionary dynamics of supply network topologies. IEEE Transactions on Engineering Management,, 54((4)), 662–672.
Raafat, F. (1991). Survey of Literature on Continuosly Deterioring Inventory Models. Operational Ressearch Society, 42(1), 27–37.
Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131(1), 421–429.
Salamch, M. K., Fakhreddine, S. A., & Noueihed, N. (1999). Effect of deteriorating items on the instantaneous replenisment model with backlogging. Computers & Industrial Engineering, 37, 261–264.
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Stadtler, H., & Kilger, C. (2013). Supply Chain Management and Advanced Planning Concepts,Models, Software and Case Studies (Third Edit., p. 504). Springer Berlin / Heidelberg.
Suryani, E., Chou, S.-Y., Hartono, R., & Chen, C.-H. (2010). Demand scenario analysis and planned capacity expansion: A system dynamics framework. Simulation Modelling Practice and Theory, 18(6), 732–751.
Swafford, P. M., Ghosh, S., & Murthy, N. (2008). Achieving supply chain agility through IT integration and flexibility. International Journal of Production Economics, 116(2), 288–297.
Tako, A. a., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52(4), 802–815.
Wu, K.-S., Ouyang, L.-Y., & Yang, C.-T. (2006). An optimal replenishment policy for non-instantaneous deteriorating items with stock-dependent demand and partial backlogging. International Journal of Production Economics, 101(2), 369–384.
Fisher, M. L., Day, G. S., & Ryan, W. (1997). What is the Right Supply Chain for Your Product ? Harvard Business Review1 (pp. 105–116).
Forrester, W. (1961). Industrial Dynamics. Cambridge, MA: MIT.
Georgiadis, P., Vlachos, D., & Iakovou, E. (2005). A system dynamics modeling framework for the strategic supply chain management of food chains. Journal of Food Engineering, 70(3), 351–364.
Gosling, J., Purvis, L., & Naim, M. M. (2010). Supply chain flexibility as a determinant of supplier selection. International Journal of Production Economics, 128(1), 11–21.
Goyal, S. K., & Giri, B. C. (2001). Recent trends in modeling of deteriorating inventory. European Journal of Operational Research, 134(1), 1–16.
Harris, F. (1913). How Many Parts To Make At Once. The Magazine of Management, 10(2), 135–152.
Ivanov, D., Sokolov, B., & Kaeschel, J. (2010). A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations. European Journal of Operational Research, 200(2), 409–420.
Kamath, N. B., & Roy, R. (2007). Capacity augmentation of a supply chain for a short lifecycle product: A system dynamics framework. European Journal of Operational Research, 179(2), 334–351.
Karakaya, S., & Bakal, İ. S. (2013). Joint quantity flexibility for multiple products in a decentralized supply chain. Computers & Industrial Engineering, 64(2), 696–707.
Kumar, S., & Nigmatullin, A. (2011). A system dynamics analysis of food supply chains – Case study with non-perishable products. Simulation Modelling Practice and Theory, 19(10), 2151–2168.
Li, R., Hongjie, L., & Mawhinney, J. (2010). A Review on Deteriorating Inventory Study. Journal of Service Science and Management, 03(01), 117–129.
Lloréns, F. J., Molina, L. M., & Verdú, A. J. (2005). Flexibility of manufacturing systems, strategic change and performance. International Journal of Production Economics, 98(3), 273–289.
Merschmann, U., & Thonemann, U. W. (2011). Supply chain flexibility, uncertainty and firm performance: An empirical analysis of German manufacturing firms. International Journal of Production Economics, 130(1), 43–53.
Minegishi, S., & Thiel, D. (2000). System dynamics modeling and simulation of a particular food supply chain. Simulation Practice and Theory, 8(5), 321–339.
Moon, K. K.-L., Yi, C. Y., & Ngai, E. W. T. (2012). An instrument for measuring supply chain flexibility for the textile and clothing companies. European Journal of Operational Research, 222(2), 191–203.
Nita.H. Shah. (1993). Probabilistic time-scheduling model for an exponentially decaying inventory when delays in payments are permissible. International Journal of Production Economics, 32(1), 77–82.
Oh, S., Ryu, K., & Jung, M. (2013). Reconfiguration framework of a supply network based on flexibility strategies. Computers & Industrial Engineering, 65(1), 156–165.
Padmanabhan, G., & Vrat, P. (1995). EOQ models for perishable items under stock dependent selling rate. European Journal of Operational Research, 86(2), 281–292.
Pathak, S. D., Dilts, D. M., & Biswas, G. (2007). On the evolutionary dynamics of supply network topologies. IEEE Transactions on Engineering Management,, 54((4)), 662–672.
Raafat, F. (1991). Survey of Literature on Continuosly Deterioring Inventory Models. Operational Ressearch Society, 42(1), 27–37.
Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131(1), 421–429.
Salamch, M. K., Fakhreddine, S. A., & Noueihed, N. (1999). Effect of deteriorating items on the instantaneous replenisment model with backlogging. Computers & Industrial Engineering, 37, 261–264.
Senge, P. (2006). The fifth Discipline: the art and practice of the learning organization. New York, United States: Doubleday Currency.
Sicilia, J., González, M., Febles, J., & Alcaide, D. (2014). An inventory model for deteriorating items with shortages and time-varying demand. International Journal of Production Economics, (2003), 1–8.
Stadtler, H., & Kilger, C. (2013). Supply Chain Management and Advanced Planning Concepts,Models, Software and Case Studies (Third Edit., p. 504). Springer Berlin / Heidelberg.
Suryani, E., Chou, S.-Y., Hartono, R., & Chen, C.-H. (2010). Demand scenario analysis and planned capacity expansion: A system dynamics framework. Simulation Modelling Practice and Theory, 18(6), 732–751.
Swafford, P. M., Ghosh, S., & Murthy, N. (2008). Achieving supply chain agility through IT integration and flexibility. International Journal of Production Economics, 116(2), 288–297.
Tako, A. a., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52(4), 802–815.
Wu, K.-S., Ouyang, L.-Y., & Yang, C.-T. (2006). An optimal replenishment policy for non-instantaneous deteriorating items with stock-dependent demand and partial backlogging. International Journal of Production Economics, 101(2), 369–384.
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Publicado
2014-09-30
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Investigación científica y tecnológica
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Esta publicación está licenciada bajo los términos de la licencia CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.es)
