Mostrar el registro sencillo del documento

Modelado de la gestión de la calidad en cadenas de suministro usando un enfoque multi-etapas

dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.advisorCorrea Espinal, Alexander Alberto
dc.contributor.authorCogollo Flórez, Juan Miguel
dc.date.accessioned2021-06-19T14:22:24Z
dc.date.available2021-06-19T14:22:24Z
dc.date.issued2020
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/79655
dc.descriptionilustraciones
dc.description.abstractLa investigación en el área de gestión de la calidad en cadenas de suministro evidencia falta de desarrollos enfocados en el análisis de estructuras relacionales para la toma de decisiones táctico-estratégicas. En esta tesis se propone un modelo analítico para la coordinación e integración de la gestión de la calidad en cadenas de suministro usando un enfoque multi-etapas. La metodología de modelado propuesta integra mapas cognitivos grises difusos multicapa para la configuración estructural y diseños factoriales fraccionados para validar el desempeño dinámico del modelo. Las variables que representan el desempeño global de la gestión de la calidad en cadenas de suministro están agrupadas en la capa principal. Las variables del desempeño en calidad en las tres etapas de la cadena de suministro están agrupadas en submapas en una segunda capa. La validación del modelo vía experimentos de simulación computacional permitió identificar los factores principales estadísticamente significativos en cada mapa y determinar la asignación de valores grises o concretos a los mismos. Finalmente, los aportes realizados en esta investigación constituyen un punto de partida para futuras aplicaciones en sectores específicos y la integración de otras técnicas cuantitativas. (Tomado de la fuente)
dc.description.abstractResearch in Supply Chain Quality Management lacks developments focused on the analysis of relational structures for tactical-strategic decision making. This doctoral thesis proposes an analytical model for Supply Chain Quality Management coordination and integration, by using a multi-stage approach. The proposed modeling methodology integrates Multi-layer Fuzzy Grey Cognitive Maps for the structural configuration and fractional factorial designs to validate the dynamic performance of the model. The variables that represent the overall performance of Supply Chain Quality Management are grouped in the main layer. The quality performance variables in the three stages of the supply chain are grouped into submaps in a second layer. The validation of the model via computational simulation experiments made it possible to identify statistically significant factors main in each map and to determine the assignment of gray or specific values to them. Finally, the contributions made in this research constitute a starting point for future applications in specific sectors and the integration of other quantitative techniques. (Tomado de la fuente)
dc.format.extent132 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddcIngeniería industrial
dc.subject.ddc650 - Gerencia y servicios auxiliares::658 - Gerencia general
dc.titleModelado de la gestión de la calidad en cadenas de suministro usando un enfoque multi-etapas
dc.typeTrabajo de grado - Doctorado
dcterms.audienceEspecializada
dc.type.driverinfo:eu-repo/semantics/doctoralThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programMedellín - Minas - Doctorado en Ingeniería - Industria y Organizaciones
dc.contributor.researchgroupMODELAMIENTO PARA LA GESTIÓN DE OPERACIONES (GIMGO)
dc.description.degreelevelDoctorado
dc.description.degreenameDoctor en Ingeniería
dc.description.methodsSe utilizó una metodología secuencial exploratoria que permite integrar referentes teóricos para un posterior análisis cuantitativo
dc.description.researchareaGestión de la Cadena de Suministro
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentDepartamento de Ingeniería de la Organización
dc.publisher.facultyFacultad de Minas
dc.publisher.placeMedellín
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellín
dc.relation.referencesAjalli, M., & Mozaffari, M. M. (2018). Appraisal the key factors of SCQM using a combined approach of SWARA-FISM. International Journal of Supply Chain Management, 7(4), 13–21.
dc.relation.referencesAmer, Y., Luong, L., & Lee, S. H. (2010). Case study: Optimizing order fulfillment in a global retail supply chain. International Journal of Production Economics, 127(2), 278–291. https://doi.org/10.1016/j.ijpe.2009.08.020
dc.relation.referencesBautista-Santos, H., Martínez-Flores, L., Fernández-Lambert, G., Bernabé-Loranca, M. B., Sánchez-Galván, F., & Sablón-Cossío, N. (2015). Integration model of collaborative supply chain. Dyna, 82(193), 145–154. https://doi.org/10.15446/dyna.v82n193.47370
dc.relation.referencesBayo-Moriones, A., Bello-Pintado, A., & Merino-Díaz-de-Cerio, J. (2011). Quality assurance practices in the global supply chain: the effect of supplier localisation. International Journal of Production Research, 49(1), 255–268. https://doi.org/10.1080/00207543.2010.508953
dc.relation.referencesBorner, K., Chen, C., & Boyack, K. (2003). Visualizing Knowledge Domains. Annual Review of Information Science and Technology, 37(1), 179–255.
dc.relation.referencesBowersox, D., Closs, D., Cooper, M., & Bowersox, J. (2020). Supply Chain Logistics Management (5th ed.). New York, NY: McGrawHill.
dc.relation.referencesBrandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312. https://doi.org/10.1016/j.ejor.2013.09.032
dc.relation.referencesBray, R. L., Serpa, J. C., & Colak, A. (2019). Supply Chain Proximity and Product Quality. Management Science, 65(9), 4079–4099. https://doi.org/10.1287/mnsc.2018.3161
dc.relation.referencesÇankaya, S. Y. (2020). The effects of strategic sourcing on supply chain strategies. Journal of Global Operations and Strategic Sourcing, 13(2), 129–148. https://doi.org/10.1108/JGOSS-01-2019-0002
dc.relation.referencesCarmignani, G. (2009). Supply chain and quality management: The definition of a standard to implement a process management system in a supply chain. Business Process Management Journal, 15(3), 395–407. https://doi.org/10.1108/14637150910960639
dc.relation.referencesChaghooshi, A. J., Soltani-Neshan, M., & Moradi-Moghadam, M. (2015). Canonical correlation analysis between supply chain quality management and competitive advantages. Foundations of Management, 7(1), 83–92.
dc.relation.referencesChang, K. H., & Lin, G. (2015). Optimal design of hybrid renewable energy systems using simulation optimization. Simulation Modelling Practice and Theory, 52, 40–51. https://doi.org/10.1016/j.simpat.2014.12.002
dc.relation.referencesChardine-Baumann, E., & Botta-Genoulaz, V. (2014). A framework for sustainable performance assessment of supply chain management practices. Computers and Industrial Engineering, 76, 138–147. https://doi.org/10.1016/j.cie.2014.07.029
dc.relation.referencesChen, J., Fan, T., & Pan, F. (2021). Urban delivery of fresh products with total deterioration value. International Journal of Production Research, 59(7), 2218–2228. https://doi.org/10.1080/00207543.2020.1828638
dc.relation.referencesCheung, K. L., & Leung, K. F. (2000). Coordinating replenishments in a supply chain with quality control considerations. Production Planing and Control, 11(7), 697–705. https://doi.org/10.1080/095372800432160
dc.relation.referencesChiadamrong, N., & Wajcharapornjinda, P. (2012). Developing an economic cost model for quantifying supply chain costs. International Journal of Logistics Systems and Management, 13(4), 540–571. https://doi.org/10.1504/IJLSM.2012.050171
dc.relation.referencesChinello, E., Lee Herbert-Hansen, Z. N., & Khalid, W. (2020). Assessment of the impact of inventory optimization drivers in a multi-echelon supply chain: Case of a toy manufacturer. Computers and Industrial Engineering, 141, 106232. https://doi.org/10.1016/j.cie.2019.106232
dc.relation.referencesChopra, S. (2018). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). New York: Pearson.
dc.relation.referencesChoudhary, D., Shankar, R., Tiwari, M. K., & Purohit, A. K. (2016). VMI versus information sharing: an analysis under static uncertainty strategy with fill rate constraints. International Journal of Production Research, 54(13), 3978–3993. https://doi.org/10.1080/00207543.2016.1168943
dc.relation.referencesChristoforou, A., & Andreou, A. S. (2017). A framework for static and dynamic analysis of multi-layer fuzzy cognitive maps. Neurocomputing, 232, 133–145. https://doi.org/10.1016/j.neucom.2016.09.115
dc.relation.referencesChristova, N., Stylios, C., & Groumpos, P. (2003). Production Planning for Complex Plants using Fuzzy Cognitive Maps. IFAC Proceedings Volumes, 36(3), 81–86. https://doi.org/10.1016/S1474-6670(17)37739-X
dc.relation.referencesCogollo-Flórez, Juan M., & Correa-Espinal, A. A. (2019). Analytical modeling of supply chain quality management coordination and integration: A literature review. Quality Management Journal, 26(2), 72–83. https://doi.org/10.1080/10686967.2019.1580553
dc.relation.referencesCogollo-Flórez, Juan M, & Correa-Espinal, A. A. (2017). Modeling Supply Chain Quality Management Performance. In Proceedings of the International Conference on Modeling and Applied Simulation 2017 (pp. 115–122). Barcelona, Spain.
dc.relation.referencesCogollo-Flórez, Juan Miguel, & Correa-Espinal, A. A. (2018). Rule-based Modeling of Supply Chain Quality Management. In A. Bruzzone, F. De Felice, C. Frydman, F. Longo, M. Massei, & A. Solis (Eds.), Proceedings of The International Conference on Modeling and Applied Simulation 2018 (pp. 120–125). Budapest, Hungary.
dc.relation.referencesCogollo Flórez, J. M., & Ruiz Vásquez, C. (2019). Prácticas de responsabilidad sostenible de cadenas de suministro: Revisión y propuesta. Revista Venezolana de Gerencia, 24(87), 668–683.
dc.relation.referencesCogollo, J., & Correa, A. (2019). Modeling Supply Chain Quality Management using Multi-Layer Fuzzy Cognitive Maps. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1–6). New Orleans, LA: IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8858995
dc.relation.referencesCooper, M., Lambert, D., & Pagh, J. (1997). Supply Chain Management: More Than a New Name for Logistics. The International Journal of Logistics Management, 8(1), 1–14. https://doi.org/10.1108/09574099710805556
dc.relation.referencesCouncil of Supply Chain Management Professionals. (n.d.). CSCMP Supply Chain Management Definitions and Glossary. Retrieved June 13, 2020, from https://cscmp.org/CSCMP/Academia/SCM_Definitions_and_Glossary_of_Terms/CSCMP/Educate/SCM_Definitions_and_Glossary_of_Terms.aspx?hkey=60879588-f65f-4ab5-8c4b-6878815ef921
dc.relation.referencesCoyle, J., Langley, J., Novack, R., & Gibson, B. (2017). Supply Chain Management: A Logistics Perspective (10th ed.). Boston, USA: Cengage Learning.
dc.relation.referencesCreswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Los Angeles, CA: SAGE.
dc.relation.referencesCruz Trejos, E., Correa Espinal, A. A., & Cogollo Florez, J. M. (2012). Supply Chain Social Responsibility. Gestión y Región, 13, 89–106.
dc.relation.referencesDas, K., & Sengupta, S. (2010). Modelling supply chain network: a quality-oriented approach. International Journal of Quality & Reliability Management, 27(5), 506–526. https://doi.org/10.1108/09574090910954864
dc.relation.referencesDas, Kanchan, & Lashkari, R. S. (2015). A Supply Chain Product Delivery and Distribution Planning Model. Operations and Supply Chain Management: An International Journal, 8(1), 22–27. https://doi.org/10.31387/oscm0190129
dc.relation.referencesDellana, S., & Kros, J. (2014). An exploration of quality management practices, perceptions and program maturity in the supply chain. International Journal of Operations & Production Management, 34(6), 786–806. https://doi.org/10.1108/09574090910954864
dc.relation.referencesDickerson, J. A., & Kosko, B. (1994). Virtual Worlds as Fuzzy Cognitive Maps. Presence: Teleoperators and Virtual Environments, 3(2), 173–189. https://doi.org/10.1109/VRAIS.1993.380742
dc.relation.referencesDuman, E. (2007). Decision making by simulation in a parcel transportation company. Journal of The Franklin Institute, 344(5), 672–683. https://doi.org/10.1016/j.jfranklin.2006.02.030
dc.relation.referencesEdmonds, W., & Kennedy, T. (2017). An Applied Guide to Research Designs: Quantitative, Qualitative, and Mixed Methods (2nd ed.). Los Angeles, CA: SAGE.
dc.relation.referencesEvans, J. R., Foster, S. T., & Linderman, K. (2014). A Content Analysis of Research in Quality Management and a Proposed Agenda for Future Research. Quality Management Journal, 21(2), 17–44.
dc.relation.referencesFernandes, A. C., Sampaio, P., & Carvalho, M. do S. (2014). Quality Management and Supply Chain Management Integration: a conceptual model. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 773–780). Bali, Indonesia.
dc.relation.referencesFlynn, B., & Zhao, X. (2015). Global Supply Chain Quality Management: Product Recalls and Their Impact. Boca Raton: CRC Press.
dc.relation.referencesFoster, S. T. (2008). Towards an understanding of supply chain quality management. Journal of Operations Management, 26(4), 461–467. https://doi.org/10.1016/j.jom.2007.06.003
dc.relation.referencesFoster, S. T. (2017). Managing Quality: Integrating the Supply Chain (6th ed.). New Jersey: Pearson.
dc.relation.referencesGalindo-Pacheco, G. M., Paternina-Arboleda, C. D., Barbosa-Correa, R. A., & Llinás-Solano, H. (2012). Non-linear programming model for cost minimization in a supply chain, including non-quality and inspection costs. International Journal of Operational Research, 14(3), 301–323. https://doi.org/10.1504/IJOR.2012.047092
dc.relation.referencesGao, C., Cheng, T. C. E., Shen, H., & Xu, L. (2016). Incentives for quality improvement efforts coordination in supply chains with partial cost allocation contract. International Journal of Production Research, 54(20), 6213–6231. https://doi.org/10.1080/00207543.2016.1191691
dc.relation.referencesGumrukcu, S., Rossetti, M. D., & Buyurgan, N. (2008). Quantifying the costs of cycle counting in a two-echelon supply chain with multiple items. International Journal of Production Economics, 116(2), 263–274. https://doi.org/10.1016/j.ijpe.2008.09.006
dc.relation.referencesGutiérrez, H., & De La Vara, R. (2012). Análisis y diseño de experimentos (3rd ed.). México: McGrawHill.
dc.relation.referencesGylling, M., Heikkilä, J., Jussila, K., & Saarinen, M. (2015). Making decisions on offshore outsourcing and backshoring: A case study in the bicycle industry. International Journal of Production Economics, 162, 92–100. https://doi.org/10.1016/j.ijpe.2015.01.006
dc.relation.referencesHarrison, A., Van Hoek, R., & Skipworth, H. (2014). Logistics Management and Strategy: Competing Throug the Supply Chain (5th ed.). Harlow, UK: Pearson.
dc.relation.referencesHasani, A., Zegordi, S. H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50(16), 4649–4669. https://doi.org/10.1080/00207543.2011.625051
dc.relation.referencesHatwagner, M. F., Buruzs, A., Torma, A., & Koczy, L. T. (2015). Introduction of Modeling Complex Management Systems using Fuzzy Cognitive Map. The 7th International Conference on Information Technology, 2015, 508–514. https://doi.org/10.15849/icit.2015.0092
dc.relation.referencesHugos, M. (2018). Essentials of Supply Chain Management (4th ed.). Hoboken, NJ: Wiley.
dc.relation.referencesHuo, B., Ye, Y., Zhao, X., & Zhu, K. (2016). Supply chain quality integration: A taxonomy perspective. International Journal of Production Economics, In Press, 1–11. https://doi.org/10.1016/j.ijpe.2016.05.004
dc.relation.referencesJacobs, F., & Chase, R. (2018). Operations and Supply Chain Management (15th ed.). New York, NY: McGrawHill.
dc.relation.referencesJaqueta, S. D. J., Mashilo, E. N., Mocke, K., & Agigi, A. F. A. (2020). Physical distribution challenges and adaptations: A qualitative study of South Africa-based organisations operating in emerging African markets. Journal of Transport and Supply Chain Management, 14(1), 1–16. https://doi.org/10.4102/jtscm.v14i0.475
dc.relation.referencesJetter, A. J., & Kok, K. (2014). Fuzzy Cognitive Maps for futures studies-A methodological assessment of concepts and methods. Futures, 61, 45–57. https://doi.org/10.1016/j.futures.2014.05.002
dc.relation.referencesKitchenham, B. (2004). Procedures for Performing Systematic Reviews. Joint Technical Report. Australia: Department of Computer Science. Keele University. https://doi.org/10.1.1.122.3308
dc.relation.referencesKleijnen, J. (2005). An overview of the design and analysis of simulation experiments for sensitivity analysis. European Journal of Operational Research, 164(2), 287–300. https://doi.org/10.1016/j.ejor.2004.02.005
dc.relation.referencesKleijnen, J. P. C., & Smits, M. T. (2003). Performance metrics in supply chain management. Journal of the Operational Research Society, 54(5), 507–514. https://doi.org/10.1057/palgrave.jors.2601539
dc.relation.referencesKonti, A., & Damigos, D. (2018). Exploring strengths and weaknesses of bioethanol production from bio-waste in Greece using Fuzzy Cognitive Maps. Energy Policy, 112, 4–11. https://doi.org/10.1016/j.enpol.2017.09.053
dc.relation.referencesKosko, B. (1986). Fuzzy cognitive maps. Int. J. Man-Machine Studies, 24, 65–75.
dc.relation.referencesKuei, C.-H., & Madu, C. N. (2001). Identifying critical success factors for supply chain quality management (SCQM). Asia Pacific Management Review, 6(4), 409–423. https://doi.org/10.4018/jsds.2010070104
dc.relation.referencesKuei, C.-H., Madu, C. N., & Lin, C. (2011). Developing global supply chain quality management systems. International Journal of Production Research, 49(15), 4457–4481. https://doi.org/10.1080/00207543.2010.501038
dc.relation.referencesKuei, C.-H., Madu, C. N., & Winch, J. K. (2008). Supply chain quality management: a simulation study. Information and Management Sciences, 19(1), 131–151.
dc.relation.referencesKumar, S., & Schmitz, S. (2011). Managing recalls in a consumer product supply chain - Root cause analysis and measures to mitigate risks. International Journal of Production Research, 49(1), 235–253. https://doi.org/10.1080/00207543.2010.508952
dc.relation.referencesLaguna, M., & Marklund, J. (2019). Business Process Modeling, Simulation and Design (3rd ed.). Boca Raton, FL: CRC Press.
dc.relation.referencesLambertini, L. (2018). Coordinating research and development efforts for quality improvement along a supply chain. European Journal of Operational Research, 270(2), 599–605. https://doi.org/10.1016/j.ejor.2018.03.037
dc.relation.referencesLavin, E., & Giabbanelli, P. (2017). Analyzing and simplifying model uncertainty in fuzzy cognitive maps. In Proceedings of the 2017 Winter Simulation Conference (pp. 1868–1879). Las Vegas, NV, USA. https://doi.org/10.1109/WSC.2017.8247923
dc.relation.referencesLaw, A. (2015). Simulation Modeling and Analysis (5th ed.). New York, NY: McGrawHill.
dc.relation.referencesLaw, A. (2017). A tutorial on Design of Experiments for simulation modeling. In Proceedings of the 2017 Winter Simulation Conference (pp. 550–564). Las Vegas, NV, USA.
dc.relation.referencesLejarza, F., & Baldea, M. (2020). Closed-loop optimal operational planning of supply chains with fast product quality dynamics. Computers and Chemical Engineering, 132, 106594. https://doi.org/10.1016/j.compchemeng.2019.106594
dc.relation.referencesLeón, M., Rodriguez, C., García, M. M., Bello, R., & Vanhoof, K. (2010). Fuzzy Cognitive Maps for Modeling Complex Systems. In Proceedings of 9th Mexican International Conference on Artificial Intelligence, MICAI 2010 (pp. 166–174). https://doi.org/10.1007/978-3-642-16761-4_15
dc.relation.referencesLi, B., & Jiang, Y. (2019). Impacts of returns policy under supplier encroachment with risk-averse retailer. Journal of Retailing and Consumer Services, 47, 104–115. https://doi.org/10.1016/j.jretconser.2018.11.011
dc.relation.referencesLin, C., Chow, W. S., Madu, C. N., Kuei, C.-H., & Pei Yu, P. (2005). A structural equation model of supply chain quality management and organizational performance. International Journal of Production Economics, 96(3), 355–365. https://doi.org/10.1016/j.ijpe.2004.05.009
dc.relation.referencesLiu, S., & Lin, Y. (2006). Grey Information: Theory and Practical Applications. London, UK: Springer.
dc.relation.referencesLiu, Y., Fang, S., Fang, Z., & Hipel, K. (2012). Petri net model for supply-chain quality conflict resolution of a complex product. Kybernetes, 41(7/8), 920–928. https://doi.org/10.1108/K-01-2015-0009
dc.relation.referencesLorscheid, I., Heine, B. O., & Meyer, M. (2012). Opening the “black box” of simulations: increased transparency and effective communication through the systematic design of experiments. Computational and Mathematical Organization Theory, 18(1), 22–62. https://doi.org/10.1007/s10588-011-9097-3
dc.relation.referencesLou, P., Liu, Q., Zhou, Z., & Quan, S. (2009). Production-Outsourcing Supply Chain Quality Management Based on Multi-Agent System. In Proceedings of The 16th International Conference on Industrial Engineering and Engineering Management, 2009. IE&EM ’09. (pp. 1555–1559).
dc.relation.referencesLuthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698. https://doi.org/10.1016/j.jclepro.2016.09.078
dc.relation.referencesMallick, R. K., Manna, A. K., & Mondal, S. K. (2018). A supply chain model for imperfect production system with stochastic lead time demand. Journal of Management Analytics, 5(4), 309–333. https://doi.org/10.1080/23270012.2018.1530619
dc.relation.referencesMarucheck, A., Greis, N., Mena, C., & Cai, L. (2011). Product safety and security in the global supply chain: Issues, challenges and research opportunities. Journal of Operations Management, 29(7–8), 707–720. https://doi.org/10.1016/j.jom.2011.06.007
dc.relation.referencesMasoudipour, E., Amirian, H., & Sahraeian, R. (2017). A novel closed-loop supply chain based on the quality of returned products. Journal of Cleaner Production, 151, 344–355. https://doi.org/10.1016/j.jclepro.2017.03.067
dc.relation.referencesMelnyk, S. a., Lummus, R. R., Vokurka, R. J., Burns, L. J., & Sandor, J. (2009). Mapping the future of supply chain management: a Delphi study. International Journal of Production Research, 47(16), 4629–4653. https://doi.org/10.1080/00207540802014700
dc.relation.referencesMendes Dos Reis, J. G. (2011). Modelo de Avaliação da Qualidade para Redes de Suprimentos. Universidade Paulista: Tese de Doutoramento em Engenharia de Produção.
dc.relation.referencesMerigó, J. M., & Yang, J. B. (2017). A bibliometric analysis of operations research and management science. Omega, 73, 37–48. https://doi.org/10.1016/j.omega.2016.12.004
dc.relation.referencesModak, N. M., Panda, S., & Sana, S. S. (2016). Three-echelon supply chain coordination considering duopolistic retailers with perfect quality products. International Journal of Production Economics, 182, 564–578. https://doi.org/10.1016/j.ijpe.2015.05.021
dc.relation.referencesMoharana, H., Murty, J. S., Senapati, S. K., & Khuntia, K. (2012). Coordination, Collaboration and Integration for Supply Chain Management. International Journal of Interscience Management Review (IMR), 2(2), 46–50.
dc.relation.referencesMontevechi, J. A. B., De Almeida Filho, R. G., Paiva, A. P., Costa, R. F. S., & Medeiros, A. L. (2010). Sensitivity analysis in discrete-event simulation using fractional factorial designs. Journal of Simulation, 4(2), 128–142. https://doi.org/10.1057/jos.2009.23
dc.relation.referencesMontgomery, D. (2017). Design and Analysis of Experiments (9th ed.). Hoboken, NJ: John Wiley & Sons.
dc.relation.referencesMontoya-Torres, J. R., & Ortiz-Vargas, D. A. (2014). Collaboration and information sharing in dyadic supply chains: A literature review over the period 2000–2012. Estudios Gerenciales, 30, 343–354. https://doi.org/10.1016/j.estger.2014.05.006
dc.relation.referencesMontoya-Torres, J. R., & Ortiz, D. (2011). Analysis of the collaboration concept in supply chain: A scientific literature review. In Proceedings of Ninth Latin American and Caribbean Conference. (pp. 1–10). August 3-5, Medellín, Colombia.
dc.relation.referencesMota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2015). Towards supply chain sustainability: Economic, environmental and social design and planning. Journal of Cleaner Production, 105, 14–27. https://doi.org/10.1016/j.jclepro.2014.07.052
dc.relation.referencesMourhir, A., Papageorgiou, E., Kokkinos, K., & Rachidi, T. (2017). Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps. Sustainability, 9(7), 1241. https://doi.org/10.3390/su9071241
dc.relation.referencesMpelogianni, V., Marnetta, P., & Groumpos, P. P. (2015). Fuzzy Cognitive Maps in the Service of Energy Efficiency. IFAC-PapersOnLine, 48(24), 1–6. https://doi.org/10.1016/j.ifacol.2015.12.047
dc.relation.referencesNagar, L., & Jain, K. (2008). Supply chain planning using multi-stage stochastic programming. Supply Chain Management: An International Journal, 13(3), 251–256. https://doi.org/10.1108/13598540810871299
dc.relation.referencesNarasimhan, R., & Nair, A. (2005). The antecedent role of quality, information sharing and supply chain proximity on strategic alliance formation and performance. International Journal of Production Economics, 96(3), 301–313. https://doi.org/10.1016/j.ijpe.2003.06.004
dc.relation.referencesNarasimhan, V., Venkatasubbaiah, K., & Avadhani, P. S. (2013). Identification of Critical SSCM Activities Through Confirmatory Factor Analysis. International Journal for Quality Research, 7(2), 239–248.
dc.relation.referencesObiedat, M., & Samarasinghe, S. (2016). A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems. Applied Soft Computing Journal, 48, 91–110. https://doi.org/10.1016/j.asoc.2016.06.001
dc.relation.referencesPang, J., & Tan, K. H. (2018). Supply chain quality and pricing decisions under multi-manufacturer competition. Industrial Management & Data Systems, 118(1), 164–187. https://doi.org/10.1108/IMDS-03-2017-0092
dc.relation.referencesPapageorgiou, E. I., Aggelopoulou, K. D., Gemtos, T. A., & Nanos, G. D. (2013). Yield prediction in apples using Fuzzy Cognitive Map learning approach. Computers and Electronics in Agriculture, 91, 19–29. https://doi.org/10.1016/j.compag.2012.11.008
dc.relation.referencesPapageorgiou, E., Markinos, A., & Gemptos, T. (2009). Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Systems with Applications, 36(10), 12399–12413. https://doi.org/10.1016/j.eswa.2009.04.046
dc.relation.referencesParast, M. M. (2013). Supply chain quality management: An inter-organizational learning perspective. International Journal of Quality & Reliability Management, 30(5), 511–529. https://doi.org/10.1108/09574090910954864
dc.relation.referencesParast, M. M. (2019). A learning perspective of supply chain quality management: empirical evidence from US supply chains. Supply Chain Management: An International Journal, 25(1), 17–34. https://doi.org/10.1108/SCM-01-2019-0028
dc.relation.referencesPark, Y. B. (2005). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research, 43(6), 1205–1224. https://doi.org/10.1080/00207540412331327718
dc.relation.referencesPelta, D. A., & Cruz Corona, C. (2018). Soft Computing Based Optimization and Decision Models. Berlin: Springer. https://doi.org/10.1007/978-3-319-64286-4
dc.relation.referencesPeng, X., Prybutok, V., & Xie, H. (2019). Integration of supply chain management and quality management within a quality focused organizational framework. International Journal of Production Research, 58(2), 448–466. https://doi.org/10.1080/00207543.2019.1593548
dc.relation.referencesPettersson, A. I., & Segerstedt, A. (2013). Measuring supply chain cost. International Journal of Production Economics, 143(2), 357–363. https://doi.org/10.1016/j.ijpe.2012.03.012
dc.relation.referencesPhan, A. C., Abdallah, A. B., & Matsui, Y. (2011). Quality management practices and competitive performance: Empirical evidence from Japanese manufacturing companies. International Journal of Production Economics, 133(2), 518–529. https://doi.org/10.1016/j.ijpe.2011.01.024
dc.relation.referencesPoczeta, K., & Papageorgiou, E. I. (2018). Implementing Fuzzy Cognitive Maps with Neural Networks for Natural Gas Prediction. In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1026–1032). Volos, Greece: IEEE. https://doi.org/10.1109/ICTAI.2018.00158
dc.relation.referencesRashid, K., & Aslam, M. M. H. (2012). Business excellence through total supply chain quality management. Asian Journal on Quality, 13(3), 309–324. https://doi.org/10.1108/09574090910954864
dc.relation.referencesReisman, A. (2004). How can OR/MS Educators Benefit From Creating and Using Taxonomies? INFORMS Transactions on Education, 4(3), 55–65. https://doi.org/10.1287/ited.4.3.55
dc.relation.referencesRobinson, C. J., & Malhotra, M. K. (2005). Defining the concept of supply chain quality management and its relevance to academic and industrial practice. International Journal of Production Economics, 96(3), 315–337. https://doi.org/10.1016/j.ijpe.2004.06.055
dc.relation.referencesRomero, J. C., Coudert, T., Geneste, L., & De Valroger, A. (2012). Collaborative methodology for supply chain quality management: Framework and integration with strategic decision processes in product development. In 6th European Conference on Information Management and Evaluation, ECIME 2012 (pp. 418–427).
dc.relation.referencesRushton, A., Croucher, P., & Baker, P. (2017). The Handbook of Logistics and Distribution Management: Understanding the Supply Chain (6th ed.). New York, NY: Kogan Page.
dc.relation.referencesSalmeron, J. (2010). Modelling grey uncertainty with Fuzzy Grey Cognitive Maps. Expert Systems with Applications, 37(12), 7581–7588. https://doi.org/10.1016/j.eswa.2010.04.085
dc.relation.referencesSanders, N. (2018). Supply Chain Management: A Global Perspective (2nd ed.). Hoboken, NJ: John Wiley & Sons.
dc.relation.referencesSarkar, B., Majumder, A., Sarkar, M., Kim, N., & Ullah, M. (2018). Effects of variable production rate on quality of products in a single-vendor multi-buyer supply chain management. The International Journal of Advanced Manufacturing Technology, 99, 567–581. https://doi.org/10.1007/s00170-018-2527-3
dc.relation.referencesSayama, H. (2015). Introduction to the Modeling and Analysis of Complex Systems. New York: Open SUNY Textbooks.
dc.relation.referencesShah, J. (2016). Supply Chain Management: Text and Cases (2nd ed.). Noida, India: Pearson.
dc.relation.referencesSharma, A., Garg, D., & Agarwal, A. (2012). Quality Management in Supply Chains: the Literature Review. International Journal for Quality Research, 6(3), 193–206.
dc.relation.referencesSharma, A., Garg, D., & Agarwal, A. (2014). Product recall: Supply chain quality issue? International Journal of Intelligent Enterprise, 2(4), 277–293. https://doi.org/10.1504/IJIE.2014.069059
dc.relation.referencesSimchi-Levi, D., Chen, X., & Bramel, J. (2014). The Logic of Logistics: Theory, Algorithms, and Applications for Logistics Management (3rd ed.). New York, NY: Springer.
dc.relation.referencesSkład, A. (2019). Assessing the impact of processes on the Occupational Safety and Health Management System’s effectiveness using the fuzzy cognitive maps approach. Safety Science, 117, 71–80. https://doi.org/10.1016/j.ssci.2019.03.021
dc.relation.referencesSlack, N., Brandon-Jones, A., & Johnston, R. (2016). Operations Management (8th ed.). Harlow, UK: Pearson.
dc.relation.referencesSlack, N., & Lewis, M. (2017). Operations Strategy (5th ed.). Harlow, UK: Pearson.
dc.relation.referencesSong, T., Li, Y., Song, J., & Zhang, Z. (2014). Airworthiness considerations of supply chain management from Boeing 787 Dreamliner battery issue. Procedia Engineering, 80, 628–637. https://doi.org/10.1016/j.proeng.2014.09.118
dc.relation.referencesSteven, A. B., Dong, Y., & Corsi, T. (2014). Global sourcing and quality recalls: An empirical study of outsourcing-supplier concentration-product recalls linkages. Journal of Operations Management, 32(5), 241–253. https://doi.org/10.1016/j.jom.2014.04.003
dc.relation.referencesSu, Q., & Liu, Q. (2011). Supply Chain Quality Management by Contract Design. In D. Önkal & E. Aktas (Eds.), Supply Chain Management - Pathways for Research and Practice (pp. 57–74). Rijeka: InTech.
dc.relation.referencesSuard, S., Hostikka, S., & Baccou, J. (2013). Sensitivity analysis of fire models using a fractional factorial design. Fire Safety Journal, 62, 115–124. https://doi.org/10.1016/j.firesaf.2013.01.031
dc.relation.referencesSun, P., & Li, Q. (2010). Study on Supply Chain Quality Management Model Based on Immune Theory. 2010 International Conference on Management and Service Science, 1–4. https://doi.org/10.1109/ICMSS.2010.5576336
dc.relation.referencesSusniene, D., Torma, A., Buruzs, A., Hatwágner, M. F., & Kóczy, L. T. (2014). Using Fuzzy Cognitive Map Approach to model the casual relationships in stakeholder management at companies. In 5th IEEE International Conference on Cognitive Infocommunications (pp. 121–124). Vietri sul Mare, Italy.
dc.relation.referencesTarashioon, S., Van Driel, W. D., & Zhang, G. Q. (2014). Multi-physics reliability simulation for solid state lighting drivers. Microelectronics Reliability, 54(6–7), 1212–1222. https://doi.org/10.1016/j.microrel.2014.02.019
dc.relation.referencesTruong, H. Q., Sampaio, P., Sameiro, M., & Fernandez, A. (2016). An extensive structural model of supply chain quality management and firm performance. International Journal of Quality & Reliability Management, 33(4), 444–464.
dc.relation.referencesTruong, H., Sampaio, P., Carvalho, M. S., Fernandes, A. C., Binh An, D. T., & Vilhenac, E. (2016). An extensive structural model of supply chain quality management and firm performance. International Journal of Quality & Reliability Management, 33(4), 444–464. https://doi.org/10.1108/IJQRM-11-2014-0188
dc.relation.referencesTsadiras, A. K. (2008). Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Sciences, 178(20), 3880–3894. https://doi.org/10.1016/j.ins.2008.05.015
dc.relation.referencesTsai, T. P., & Wang, F.-C. (2004). Improving Supply Chain Management: A Model for Collaborative Quality Control. In Advanced Semiconductor Manufacturing, 2004. ASMC ’04. IEEE Conference and Workshop (pp. 36–42). https://doi.org/10.1109/ASMC.2004.1309531
dc.relation.referencesWieland, A., Handfield, R. B., & Durach, C. F. (2016). Mapping the Landscape of Future Research Themes in Supply Chain Management. Journal of Business Logistics, 37(3), 205–212.
dc.relation.referencesWood, L. C., Wang, J. X., Olesen, K., & Reiners, T. (2017). The effect of slack, diversification, and time to recall on stock market reaction to toy recalls. International Journal of Production Economics, 193, 244–258. https://doi.org/10.1016/j.ijpe.2017.07.021
dc.relation.referencesWu, Y., Yang, Y., Wang, Z., & Yuan, J. (2013). Macro Quality Chain Management and Coordination Optimization Research. Journal of Software, 8(8), 2023–2031. https://doi.org/10.4304/jsw.8.8.2023-2031
dc.relation.referencesXiao, T., Yang, D., & Shen, H. (2011). Coordinating a supply chain with a quality assurance policy via a revenue-sharing contract. International Journal of Production Research, 49(1), 99–120. https://doi.org/10.1080/00207543.2010.508936
dc.relation.referencesYan, J., Sun, S., Wang, H., & Hua, Z. (2010). Ontology of Collaborative Supply Chain for Quality Management. World Academy of Science, Engineering and Technology, 4(4), 365–370.
dc.relation.referencesYao, D. Q., & Zhang, N. (2009). Contract design for supply chain quality management. International Journal of Value Chain Management, 3(2), 129–145. https://doi.org/10.1504/IJVCM.2009.026954
dc.relation.referencesYoo, S. H. (2014). Product quality and return policy in a supply chain under risk aversion of a supplier. International Journal of Production Economics, 154, 146–155. https://doi.org/10.1016/j.ijpe.2014.04.012
dc.relation.referencesYoo, S. H., & Cheong, T. (2018). Quality improvement incentive strategies in a supply chain. Transportation Research Part E: Logistics and Transportation Review, 114, 331–342. https://doi.org/10.1016/j.tre.2018.01.005
dc.relation.referencesYu, Y., & Huo, B. (2018). Supply chain quality integration: relational antecedents and operational consequences. Supply Chain Management: An International Journal, 23(3), 188–206. https://doi.org/10.1108/SCM-08-2017-0280
dc.relation.referencesZeng, J., Phan, C. A., & Matsui, Y. (2013). Supply chain quality management practices and performance: An empirical study. Operations Management Research, 6(1–2), 19–31. https://doi.org/10.1007/s12063-012-0074-x
dc.relation.referencesZhang, M., Guo, H., Huo, B., Zhao, X., & Huang, J. (2017). Linking supply chain quality integration with mass customization and product modularity. International Journal of Production Economics, 207, 227–235. https://doi.org/10.1016/j.ijpe.2017.01.011
dc.relation.referencesZimon, D. (2017). The Impact of TQM Philosophy for the Improvement of Logistics Processes in the Supply Chain. International Journal for Quality Research, 11(1), 3–16. https://doi.org/10.18421/IJQR11.01-01
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembControl de calidad
dc.subject.lembCalidad de los productos
dc.subject.proposalGestión de la calidad en cadenas de suministro
dc.subject.proposalModelado analítico multietapa
dc.subject.proposalMapas cognitivos grises difusos
dc.subject.proposalDiseño factorial fraccionado
dc.subject.proposalSupply Chain Quality Management
dc.subject.proposalMulti-layer Analytical Modeling
dc.subject.proposalFuzzy Grey Cognitive Maps
dc.subject.proposalFractional Factorial Design
dc.title.translatedModeling supply chain ouality management using a multi-stage approach
dc.type.coarhttp://purl.org/coar/resource_type/c_db06
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TD
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


Archivos en el documento

Thumbnail
Nombre:
78752898.2020.pdf
Tamaño:
16.80Mb
Formato:
PDF
Descripción:
Tesis de Doctor en Ingeniería – ...
Descargar
Thumbnail
Nombre:
license_rdf
Tamaño:
805bytes
Formato:
application/rdf+xml
Descargar

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

Mostrar el registro sencillo del documento

Atribución-NoComercial-SinDerivadas 4.0 InternacionalEsta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.Este documento ha sido depositado por parte de el(los) autor(es) bajo la siguiente constancia de depósito