Systemic vision of flexibility analysis on perishables supply chains
DOI:
https://doi.org/10.18046/syt.v12i30.1858Keywords:
capacity, volume flexibility, perishable products, supply chain.Abstract
The Supply Chain Flexibility is determined by the response capacity related to volume and variety due of the change in the preferences of the consumers. This study evaluates a policy for a Supply Chain about the volume flexibility and its relation with a waste factor present in a perishable product. Through Systems Dynamics the distortion in the information due to the waste factor is analyzed and evaluated, as well as the effects of the flexibility decision on the service level afforded to the final client of the supply chain.References
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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.
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Karakaya, S., & Bakal, İ. S. (2013). Joint quantity flexibility for multiple products in a decentralized supply chain. Computers & Industrial Engineering, 64(2), 696–707.
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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.
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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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2014-09-30
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