- -

A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets, fuzzy programming and simulation approaches

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets, fuzzy programming and simulation approaches

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Mula, Josefa es_ES
dc.contributor.author Campuzano Bolarín, Francisco es_ES
dc.contributor.author Díaz-Madroñero Boluda, Francisco Manuel es_ES
dc.contributor.author Carpio, Katerine M. es_ES
dc.date.accessioned 2015-05-05T09:11:28Z
dc.date.available 2015-05-05T09:11:28Z
dc.date.issued 2013-07-01
dc.identifier.issn 0020-7543
dc.identifier.uri http://hdl.handle.net/10251/49679
dc.description.abstract This article proposes a simulation approach based on system dynamics for operational procurement and transport planning in a two-level, multi-product and multi-period supply chain. This work uses the Vensim((R)) simulation tool to highlight the potential of system dynamics for supply chain simulation. A real continuous simulation application is presented in an automobile supply chain. The effectiveness of the proposed model is validated through the comparison of the results provided by spreadsheet-based simulation, fuzzy multi-objective programming and system dynamics-based simulation models. The fundamental point of this paper is that the simulation model is the most effective approach in quantifying the trade-off between number of truck shipments and average inventory level. In this case, the number of truck shipments is to be minimised, resulting in a higher inventory level. If the average inventory level were minimised, then there would be more truck shipments. Here, it is shown the benefit of this type of simulation model in reducing inventory by about 10%. es_ES
dc.description.sponsorship This work has been funded by the Spanish Ministry of Science and Technology project: Production technology based on the feedback from production, transport and unload planning and the redesign of warehouses decisions in the supply chain (Ref. DPI2010-19,977). en_EN
dc.language Inglés es_ES
dc.publisher Taylor & Francis: STM, Behavioural Science and Public Health Titles es_ES
dc.relation.ispartof International Journal of Production Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Simulation es_ES
dc.subject System dynamics es_ES
dc.subject Supply chain dynamics es_ES
dc.subject Procurement es_ES
dc.subject Transport es_ES
dc.subject Operations planning es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets, fuzzy programming and simulation approaches es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/00207543.2013.774487
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2010-19977/ES/TECNOLOGIA DE PRODUCCION BASADA EN LA REALIMENTACION DE DECISIONES DE PLANIFICACION DE PRODUCCION, TRANSPORTE Y DESCARGAS Y EL REDISEÑO DE ALMACENES EN CADENA DE SUMINISTRO/
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Centro de Investigación de Gestión e Ingeniería de la Producción - Centre d'Investigació de Gestió i Enginyeria de la Producció es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Mula, J.; Campuzano Bolarín, F.; Díaz-Madroñero Boluda, FM.; Carpio, KM. (2013). A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets, fuzzy programming and simulation approaches. International Journal of Production Research. 51(13):4087-4104. https://doi.org/10.1080/00207543.2013.774487 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1080/00207543.2013.774487 es_ES
dc.description.upvformatpinicio 4087 es_ES
dc.description.upvformatpfin 4104 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 51 es_ES
dc.description.issue 13 es_ES
dc.relation.senia 253291
dc.identifier.eissn 1366-588X
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.description.references Archibald, G., N. Karabakal, and P. Karlsson. 1999. Supply Chain vs. Supply Chain: Using Simulation to Compete beyond the Four Walls.Proceedings of the 31st conference on Winter Simulation: Simulation – A Bridge to the Future - Volume 2. New York, USA: ACM, 1207–1214. es_ES
dc.description.references Barnabè, F. (2011). A «system dynamics‐based Balanced Scorecard» to support strategic decision making. International Journal of Productivity and Performance Management, 60(5), 446-473. doi:10.1108/17410401111140383 es_ES
dc.description.references Baumol, W. J., & Vinod, H. D. (1970). An Inventory Theoretic Model of Freight Transport Demand. Management Science, 16(7), 413-421. doi:10.1287/mnsc.16.7.413 es_ES
dc.description.references Beamon, B. M. (1998). Supply chain design and analysis: International Journal of Production Economics, 55(3), 281-294. doi:10.1016/s0925-5273(98)00079-6 es_ES
dc.description.references Biswas, S., & Narahari, Y. (2004). Object oriented modeling and decision support for supply chains. European Journal of Operational Research, 153(3), 704-726. doi:10.1016/s0377-2217(02)00806-8 es_ES
dc.description.references Bose, S., & Pekny, J. F. (2000). A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain. Computers & Chemical Engineering, 24(2-7), 329-335. doi:10.1016/s0098-1354(00)00469-5 es_ES
dc.description.references Bottani, E., & Montanari, R. (2009). Supply chain design and cost analysis through simulation. International Journal of Production Research, 48(10), 2859-2886. doi:10.1080/00207540902960299 es_ES
dc.description.references Byrne, M. ., & Bakir, M. . (1999). Production planning using a hybrid simulation – analytical approach. International Journal of Production Economics, 59(1-3), 305-311. doi:10.1016/s0925-5273(98)00104-2 es_ES
dc.description.references Byrne, M. D., & Hossain, M. M. (2005). Production planning: An improved hybrid approach. International Journal of Production Economics, 93-94, 225-229. doi:10.1016/j.ijpe.2004.06.021 es_ES
dc.description.references Cachon, G. P. (1999). Managing Supply Chain Demand Variability with Scheduled Ordering Policies. Management Science, 45(6), 843-856. doi:10.1287/mnsc.45.6.843 es_ES
dc.description.references Campuzano, F., & Mula, J. (2011). Supply Chain Simulation. doi:10.1007/978-0-85729-719-8 es_ES
dc.description.references Campuzano, F., Mula, J., & Peidro, D. (2010). Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems, 161(11), 1530-1542. doi:10.1016/j.fss.2009.12.002 es_ES
dc.description.references Chandra, P., & Fisher, M. L. (1994). Coordination of production and distribution planning. European Journal of Operational Research, 72(3), 503-517. doi:10.1016/0377-2217(94)90419-7 es_ES
dc.description.references Chatfield, D. C. (2013). Underestimating the bullwhip effect: a simulation study of the decomposability assumption. International Journal of Production Research, 51(1), 230-244. doi:10.1080/00207543.2012.660576 es_ES
dc.description.references Choi, K., Narasimhan, R., & Kim, S. W. (2012). Postponement strategy for international transfer of products in a global supply chain: A system dynamics examination. Journal of Operations Management, 30(3), 167-179. doi:10.1016/j.jom.2012.01.003 es_ES
dc.description.references Cid Yáñez, F., Frayret, J.-M., Léger, F., & Rousseau, A. (2009). Agent-based simulation and analysis of demand-driven production strategies in the timber industry. International Journal of Production Research, 47(22), 6295-6319. doi:10.1080/00207540802158283 es_ES
dc.description.references Erengüç, Ş. S., Simpson, N. C., & Vakharia, A. J. (1999). Integrated production/distribution planning in supply chains: An invited review. European Journal of Operational Research, 115(2), 219-236. doi:10.1016/s0377-2217(98)90299-5 es_ES
dc.description.references Ferreira, L., & Borenstein, D. (2011). Normative agent-based simulation for supply chain planning. Journal of the Operational Research Society, 62(3), 501-514. doi:10.1057/jors.2010.144 es_ES
dc.description.references Gen, M., & Syarif, A. (2005). Hybrid genetic algorithm for multi-time period production/distribution planning. Computers & Industrial Engineering, 48(4), 799-809. doi:10.1016/j.cie.2004.12.012 es_ES
dc.description.references Giannoccaro, I. and P. Pontrandolfo. 2001.Models for supply chain management: A taxonomy. In: Proceedings of the Production and Operations Management. Pom-2001 Conference: Pom Mastery in the New Millennium, 30 March to April 2, Orlando, Florida, 2001. es_ES
dc.description.references Gnoni, M. G., Iavagnilio, R., Mossa, G., Mummolo, G., & Di Leva, A. (2003). Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry. International Journal of Production Economics, 85(2), 251-262. doi:10.1016/s0925-5273(03)00113-0 es_ES
dc.description.references Helo, P. T. (2000). Dynamic modelling of surge effect and capacity limitation in supply chains. International Journal of Production Research, 38(17), 4521-4533. doi:10.1080/00207540050205271 es_ES
dc.description.references Hernández, J. E., Mula, J., Ferriols, F. J., & Poler, R. (2008). A conceptual model for the production and transport planning process: An application to the automobile sector. Computers in Industry, 59(8), 842-852. doi:10.1016/j.compind.2008.06.004 es_ES
dc.description.references Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., & Young, T. (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research, 203(1), 1-13. doi:10.1016/j.ejor.2009.06.004 es_ES
dc.description.references June-Young Bang, & Yeong-Dae Kim. (2010). Hierarchical Production Planning for Semiconductor Wafer Fabrication Based on Linear Programming and Discrete-Event Simulation. IEEE Transactions on Automation Science and Engineering, 7(2), 326-336. doi:10.1109/tase.2009.2021462 es_ES
dc.description.references Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering, 28(10), 2087-2106. doi:10.1016/j.compchemeng.2004.06.006 es_ES
dc.description.references Katsaliaki, K., Mustafee, N., Taylor, S. J. E., & Brailsford, S. (2009). Comparing conventional and distributed approaches to simulation in a complex supply-chain health system. Journal of the Operational Research Society, 60(1), 43-51. doi:10.1057/palgrave.jors.2602531 es_ES
dc.description.references Khaji, M. R., & Shafaei, R. (2011). A system dynamics approach for strategic partnering in supply networks. International Journal of Computer Integrated Manufacturing, 24(2), 106-125. doi:10.1080/0951192x.2010.531288 es_ES
dc.description.references Kim, B., & Kim, S. (2001). Extended model for a hybrid production planning approach. International Journal of Production Economics, 73(2), 165-173. doi:10.1016/s0925-5273(00)00172-9 es_ES
dc.description.references Klug, F. (2013). The internal bullwhip effect in car manufacturing. International Journal of Production Research, 51(1), 303-322. doi:10.1080/00207543.2012.677551 es_ES
dc.description.references Lai, Y.-J., & Hwang, C.-L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121-133. doi:10.1016/0165-0114(92)90318-x es_ES
dc.description.references Lee, Y. H., & Kim, S. H. (2002). Production–distribution planning in supply chain considering capacity constraints. Computers & Industrial Engineering, 43(1-2), 169-190. doi:10.1016/s0360-8352(02)00063-3 es_ES
dc.description.references Lee, Y. H., Kim, S. H., & Moon, C. (2002). Production-distribution planning in supply chain using a hybrid approach. Production Planning & Control, 13(1), 35-46. doi:10.1080/09537280110061566 es_ES
dc.description.references Liang, T.-F. (2006). Distribution planning decisions using interactive fuzzy multi-objective linear programming. Fuzzy Sets and Systems, 157(10), 1303-1316. doi:10.1016/j.fss.2006.01.014 es_ES
dc.description.references Lim, S. J., Jeong, S. J., Kim, K. S., & Park, M. W. (2005). A simulation approach for production-distribution planning with consideration given to replenishment policies. The International Journal of Advanced Manufacturing Technology, 27(5-6), 593-603. doi:10.1007/s00170-004-2208-2 es_ES
dc.description.references Lim, S. J., Jeong, S. J., Kim, K. S., & Park, M. W. (2005). Hybrid approach to distribution planning reflecting a stochastic supply chain. The International Journal of Advanced Manufacturing Technology, 28(5-6), 618-625. doi:10.1007/s00170-004-2398-7 es_ES
dc.description.references (s. f.). doi:10.1021/ie960901 es_ES
dc.description.references Mikati, N. (2009). Dependence of lead time on batch size studied by a system dynamics model. International Journal of Production Research, 48(18), 5523-5532. doi:10.1080/00207540903164628 es_ES
dc.description.references Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & Industrial Engineering, 43(1-2), 231-249. doi:10.1016/s0360-8352(02)00066-9 es_ES
dc.description.references Minegishi, S., & Thiel, D. (2000). System dynamics modeling and simulation of a particular food supply chain. Simulation Practice and Theory, 8(5), 321-339. doi:10.1016/s0928-4869(00)00026-4 es_ES
dc.description.references Othman, S. N. and N. H. Mustaffa. 2012. Supply Chain Simulation and Optimization Methods: An Overview. InProceedings of Third International Conference on Intelligent Systems, Modelling and Simulation(ISMS), Sabah, Malaysia, 161 –167. es_ES
dc.description.references Padhi, S. S., S. M. Wagner, T. T. Niranjan, and V. Aggarwal. 2012. A Simulation-Based Methodology to Analyse Production Line Disruptions.International Journal of Production Research51 (6): 1885–1897. es_ES
dc.description.references Peidro, D., M. Díaz-Madroñero, and J. Mula. 2009. Operational Transport Planning in an Automobile Supply Chain: An Interactive Fuzzy Multi-Objective Approach.Proceedings of the 8th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics. Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS), 121–127. es_ES
dc.description.references Petrovic, D., Roy, R., & Petrovic, R. (1998). Modelling and simulation of a supply chain in an uncertain environment. European Journal of Operational Research, 109(2), 299-309. doi:10.1016/s0377-2217(98)00058-7 es_ES
dc.description.references Qu, W. W., Bookbinder, J. H., & Iyogun, P. (1999). An integrated inventory–transportation system with modified periodic policy for multiple products. European Journal of Operational Research, 115(2), 254-269. doi:10.1016/s0377-2217(98)00301-4 es_ES
dc.description.references Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598. doi:10.1016/s0305-0483(99)00080-8 es_ES
dc.description.references Safaei, A. S., Moattar Husseini, S. M., Z.-Farahani, R., Jolai, F., & Ghodsypour, S. H. (2009). Integrated multi-site production-distribution planning in supply chain by hybrid modelling. International Journal of Production Research, 48(14), 4043-4069. doi:10.1080/00207540902791777 es_ES
dc.description.references Santa-Eulalia, L. A., D’Amours, S., & Frayret, J.-M. (2012). Agent-based simulations for advanced supply chain planning and scheduling: The FAMASS methodological framework for requirements analysis. International Journal of Computer Integrated Manufacturing, 25(10), 963-980. doi:10.1080/0951192x.2011.652177 es_ES
dc.description.references Sharda, B., & Akiya, N. (2012). Selecting make-to-stock and postponement policies for different products in a chemical plant: A case study using discrete event simulation. International Journal of Production Economics, 136(1), 161-171. doi:10.1016/j.ijpe.2011.10.001 es_ES
dc.description.references Tako, A. A., & Robinson, S. (2010). Model development in discrete-event simulation and system dynamics: An empirical study of expert modellers. European Journal of Operational Research, 207(2), 784-794. doi:10.1016/j.ejor.2010.05.011 es_ES
dc.description.references Thürer, M., Silva, C., & Stevenson, M. (2010). Optimising workload norms: the influence of shop floor characteristics on setting workload norms for the workload control concept. International Journal of Production Research, 49(4), 1151-1171. doi:10.1080/00207541003604836 es_ES
dc.description.references Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010 es_ES
dc.description.references Van der Vorst, J. G. A. ., Beulens, A. J. ., & van Beek, P. (2000). Modelling and simulating multi-echelon food systems. European Journal of Operational Research, 122(2), 354-366. doi:10.1016/s0377-2217(99)00238-6 es_ES
dc.description.references Le Hoa Vo, T., & Thiel, D. (2011). Economic simulation of a poultry supply chain facing a sanitary crisis. British Food Journal, 113(8), 1011-1030. doi:10.1108/00070701111153760 es_ES
dc.description.references Volling, T., & Spengler, T. S. (2011). Modeling and simulation of order-driven planning policies in build-to-order automobile production. International Journal of Production Economics, 131(1), 183-193. doi:10.1016/j.ijpe.2011.01.008 es_ES
dc.description.references Wang, R.-C., & Liang, T.-F. (2005). Applying possibilistic linear programming to aggregate production planning. International Journal of Production Economics, 98(3), 328-341. doi:10.1016/j.ijpe.2004.09.011 es_ES
dc.description.references Wangphanich, P., Kara, S., & Kayis, B. (2009). Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems–a simulation approach. International Journal of Production Research, 48(15), 4501-4517. doi:10.1080/00207540902950852 es_ES
dc.description.references Williams, J. F. (1981). Heuristic Techniques for Simultaneous Scheduling of Production and Distribution in Multi-Echelon Structures: Theory and Empirical Comparisons. Management Science, 27(3), 336-352. doi:10.1287/mnsc.27.3.336 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem