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 |