Cooperación en la Cadena de Suministro mediante PAR y Reducción de Variabilidad
Article Sidebar
Main Article Content
http://orcid.org/0009-0004-4737-0479
María Susana Hernández Bonilla
http://orcid.org/0009-0001-4274-3107
Oliverio Cruz Mejia
http://orcid.org/0000-0001-7362-6408
Resumen
La cooperación en la cadena de suministro es un factor clave para mejorar el desempeño organizacional en entornos caracterizados por incertidumbre y volatilidad[1]. Sin embargo, la variabilidad del comportamiento, reflejada en prácticas como el agrupamiento de pedidos, errores de pronóstico, decisiones oportunistas y variabilidad en los tiempos de entrega, continúa generando inconsistencias tales como el efecto látigo, altos costos operativos y bajos niveles de servicio. Este estudio propone un marco híbrido que integra el Proceso Analítico en Red (PAR) y la simulación para evaluar cómo la reducción acordada de la variabilidad del comportamiento puede fortalecer la cooperación y mejorar el desempeño de la cadena de suministro.
La metodología identifica los principales factores de variabilidad y evalúa mecanismos de cooperación como el intercambio de información, contratos de incentivos, generación de confianza y toma conjunta de decisiones. El método PAR se utiliza para priorizar dichos mecanismos considerando las interdependencias entre factores conductuales, estrategias de cooperación y métricas de desempeño. Posteriormente, se desarrolla un modelo de simulación de eventos discretos para validar los resultados bajo condiciones estocásticas de demanda y tiempos de entrega.
Los resultados muestran que el intercambio de información y la planeación colaborativa son los mecanismos más efectivos para reducir la variabilidad y mejorar indicadores como nivel de servicio, costos totales, efecto látigo y estabilidad de tiempos de entrega. Además, los análisis de sensibilidad y Monte Carlo confirman la robustez del modelo propuesto.
##plugins.generic.usageStats.downloads##
Article Details
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
[2] P. Fontoura and A. Coelho, “More cooperative ... more competitive? Improving competitiveness by sharing value through the supply chain,” Management Decision, vol. 60, no. 3, pp. 758–783, Feb. 2022, Doi: 10.1108/MD-09-2020-1225.
[3] C. D. Pedron and F. B. Coelho, “Collaboration in the supply chain: challenges and opportunities of collaborative planning in the healthcare sector,” Gestão & Produção, vol. 32, 2025, Doi: 10.1590/1806-9649-2025v32e10723.
[4] L. de C. Gomes, “Mitigation of Supply Chain Vulnerability Through Collaborative Planning, Forecasting, and Replenishment (CPFR),” 2022, pp. 95–119. Doi: 10.1007/978-3-031-09183-4_5.
[5] F. S. Goudarzi, P. Bergey, and D. Olaru, “Behavioral operations management and supply chain coordination mechanisms: a systematic review and classification of the literature,” Supply Chain Management: An International Journal, vol. 28, no. 1, pp. 140–161, Jan. 2023, Doi: 10.1108/SCM-03-2021-0111.
[6] A. Manousiadou, “Behavioral theories in inventory management and forecasting: Driving supply chain resilience,” Journal of Supply Chain Management Science, vol. 5, no. 3–4, pp. 143–158, Dec. 2024, doi: 10.59490/jscms.2024.7758.
[7] M. Brauch, M. Mohaghegh, and A. Größler, “Causes of the bullwhip effect: a systematic review and categorization of its causes,” Management Research Review, vol. 47, no. 7, pp. 1127–1149, May 2024, Doi: 10.1108/MRR-05-2023-0392.
[8] F. Moreno-Baca, P. Cano-Olivos, D. Sánchez-Partida, and J.-L. Martínez-Flores, “The Bullwhip Effect and Ripple Effect with Respect to Supply Chain Resilience: Challenges and Opportunities,” Logistics, vol. 9, no. 2, p. 62, May 2025, Doi: 10.3390/logistics9020062.
[9] M. El-Beheiry, R. Taha, T. Ismail, and Y. Adel, “A novel study of the impact of supply chain complexity on the bullwhip effect,” Journal of Transport and Supply Chain Management, vol. 18, no. 0, Dec. 2024, Doi: 10.4102/JTSCM.v18i0.1077.
[10] I. Mohammed and J. Mandal, “The Impact of Lead Time Variability on Supply Chain Management,” International Journal of Supply Chain Management, vol. 8, no. 2, pp. 41–55, Oct. 2024, Doi: 10.47604/ijscm.3075.
[11] Sunil. Chopra and K. Saxena, Supply chain management : strategy, planning and operation, 7a. Pearson Education, 2019.
[12] J. Ralfs and G. P. Kiesmüller, “Inventory management with advance demand information and flexible shipment consolidation,” OR Spectrum, vol. 44, no. 4, pp. 1009–1044, Dec. 2022, Doi: 10.1007/s00291-022-00686-9.
[13] D. Koutsandreas, E. Spiliotis, F. Petropoulos, and V. Assimakopoulos, “On the selection of forecasting accuracy measures,” Journal of the Operational Research Society, vol. 73, no. 5, pp. 937–954, May 2022, Doi: 10.1080/01605682.2021.1892464.
[14] R. D. Klassen, A. Shafiq, and P. Fraser Johnson, “Opportunism in supply chains: Dynamically building governance mechanisms to address sustainability-related challenges,” Transp. Res. E Logist. Transp. Rev., vol. 171, p. 103021, Mar. 2023, Doi: 10.1016/j.tre.2023.103021.
[15] Adedoyin Tolulope Oyewole, Chinwe Chinazo Okoye, Onyeka Chrisanctus Ofodile, and Emuesiri Ejairu, “Reviewing predictive analytics in supply chain management: Applications and benefits,” World Journal of Advanced Research and Reviews, vol. 21, no. 3, pp. 568–574, Mar. 2024, Doi: 10.30574/wjarr.2024.21.3.0673.
[16] P. Digkoglou and J. Papathanasiou, “Application of multiple criteria decision aiding in environmental policy-making processes,” International Journal of Environmental Science and Technology, vol. 22, no. 8, pp. 6967–6982, Apr. 2025, Doi: 10.1007/s13762-024-06101-w.
[17] T. L. Saaty, “What is the Analytic Hierarchy Process?,” in Mathematical Models for Decision Support, Berlin, Heidelberg: Springer Berlin Heidelberg, 1988, pp. 109–121. Doi: 10.1007/978-3-642-83555-1_5.
[18] N. Akbarian-Saravi, T. Sowlati, and A. S. Milani, “A Robust Analytical Network Process for Biocomposites Supply Chain Design: Integrating Sustainability Dimensions into Feedstock Pre-Processing Decisions,” Sustainability, vol. 17, no. 15, p. 7004, Aug. 2025, Doi: 10.3390/su17157004.
[19] H. Taherdoost and M. Madanchian, “Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations,” Journal of Data Science and Intelligent Systems, vol. 1, no. 1, pp. 12–18, May 2023, doi: 10.47852/bonviewJDSIS3202885.
[20] L. M. Evans, E. Sözümert, B. E. Keenan, C. E. Wood, and A. du Plessis, “A Review of Image-Based Simulation Applications in High-Value Manufacturing,” Archives of Computational Methods in Engineering, vol. 30, no. 3, pp. 1495–1552, Apr. 2023, Doi: 10.1007/s11831-022-09836-2.
[21] C. Baah et al., “Effect of information sharing in supply chains: understanding the roles of supply chain visibility, agility, collaboration on supply chain performance,” Benchmarking: An International Journal, vol. 29, no. 2, pp. 434–455, Feb. 2022, Doi: 10.1108/BIJ-08-2020-0453.