Modelos Híbridos de Contabilidad Logística para la Toma de Decisiones Estratégicas en Sistemas de Distribución Resilientes
DOI:
https://doi.org/10.70625/rmis/740Keywords:
Contabilidad logística, Modelos híbridos, Toma de decisiones, Distribución resiliente, PanamáAbstract
La creciente volatilidad de los mercados, las interrupciones en las cadenas de suministro y la digitalización de los procesos logísticos han incrementado la necesidad de desarrollar modelos híbridos de contabilidad logística que integren información financiera y operativa para fortalecer la toma de decisiones estratégicas. El problema de investigación se centra en la limitada articulación entre los sistemas contables tradicionales y los indicadores logísticos utilizados en sistemas de distribución resilientes, lo cual dificulta el análisis integral de costos, riesgos, eficiencia y capacidad de respuesta organizacional. El objetivo del estudio es proponer un modelo híbrido de contabilidad logística orientado a mejorar la toma de decisiones estratégicas en sistemas de distribución resilientes, con énfasis contextual en empresas logísticas y de distribución urbana en Panamá. Metodológicamente, se plantea un enfoque cuantitativo, de tipo aplicado, con diseño no experimental, transversal y correlacional-explicativo, apoyado en encuestas estructuradas, indicadores financieros-logísticos, validación por expertos, confiabilidad mediante Alpha de Cronbach y análisis estadístico multivariado. Como resultados esperados, se prevé que la integración de información contable, costos logísticos, indicadores de resiliencia y analítica operativa mejore la eficiencia, la trazabilidad y la capacidad estratégica de respuesta. El aporte principal consiste en un modelo interdisciplinario aplicable a organizaciones que buscan fortalecer competitividad, sostenibilidad operativa y resiliencia distributiva.
References
Bertalanffy, L. von. (1968). General system theory: Foundations, development, applications. George Braziller.
Blocher, E. J., Stout, D. E., Juras, P. E., & Smith, S. D. (2022). Cost management: A strategic emphasis (9th ed.). McGraw-Hill Education.
Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2019). Supply chain logistics management (5th ed.). McGraw-Hill.
Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177. https://doi.org/10.1016/j.compind.2018.02.010
CEPAL. (2022). Perspectivas del comercio internacional de América Latina y el Caribe 2022. Naciones Unidas.
Christopher, M. (2016). Logistics and supply chain management (5th ed.). Pearson Education.
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275
Cooper, D. R., & Schindler, P. S. (2019). Business research methods (13th ed.). McGraw-Hill Education.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning. Harvard Business Review Press.
Dolgui, A., & Ivanov, D. (2022). 5G in digital supply chain and operations management: Fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything. International Journal of Production Research, 60(2), 442–451. https://doi.org/10.1080/00207543.2021.2002969
Ellram, L. M., Tate, W. L., & Feitzinger, E. G. (2018). Factor-market rivalry and competition for supply chain resources. Journal of Supply Chain Management, 49(1), 29–46. https://doi.org/10.1111/jscm.12002
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.
Fosso Wamba, S., Queiroz, M. M., Wu, L., & Sivarajah, U. (2020). Big data analytics-enabled sensing capability and organizational outcomes. International Journal of Information Management, 50, 95–105. https://doi.org/10.1016/j.ijinfomgt.2019.05.015
Grant, R. M. (2019). Contemporary strategy analysis (10th ed.). Wiley.
Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T. (2017). Agile manufacturing practices: The role of big data and business analytics with multiple case studies. International Journal of Production Research, 56(1–2), 385–397. https://doi.org/10.1080/00207543.2017.1395488
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Heizer, J., Render, B., & Munson, C. (2020). Operations management: Sustainability and supply chain management (13th ed.). Pearson.
Hernández Sampieri, R., Fernández Collado, C., & Baptista Lucio, P. (2018). Metodología de la investigación (7.ª ed.). McGraw-Hill Education.
Horngren, C. T., Datar, S. M., & Rajan, M. (2021). Cost accounting: A managerial emphasis (17th ed.). Pearson.
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
Ivanov, D., Dolgui, A., Sokolov, B., Ivanova, M., & Werner, F. (2019). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. International Journal of Production Research, 54(2), 386–402. https://doi.org/10.1080/00207543.2014.999958
Kaplan, R. S., & Anderson, S. R. (2007). Time-driven activity-based costing: A simpler and more powerful path to higher profits. Harvard Business School Press.
Krajewski, L. J., Malhotra, M. K., & Ritzman, L. P. (2019). Operations management: Processes and supply chains (12th ed.). Pearson.
Laudon, K. C., & Laudon, J. P. (2022). Management information systems: Managing the digital firm (17th ed.). Pearson.
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Lee, H. L. (2004). The triple-A supply chain. Harvard Business Review, 82(10), 102–112.
Melnyk, S. A., Narasimhan, R., & DeCampos, H. A. (2014). Supply chain design: Issues, challenges, frameworks and solutions. International Journal of Production Research, 52(7), 1887–1896. https://doi.org/10.1080/00207543.2013.787175
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1–25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x
O’Brien, J. A., & Marakas, G. M. (2018). Management information systems. McGraw-Hill.
Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. Free Press.
Queiroz, M. M., Fosso Wamba, S., Machado, M. C., & Telles, R. (2020). Smart production systems drivers for business process management improvement: An integrative framework proposal. Business Process Management Journal, 26(5), 1075–1092. https://doi.org/10.1108/BPMJ-03-2019-0134
Romney, M. B., & Steinbart, P. J. (2021). Accounting information systems (15th ed.). Pearson.
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.
Sekaran, U., & Bougie, R. (2020). Research methods for business: A skill building approach (8th ed.). Wiley.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2021). Designing and managing the supply chain (4th ed.). McGraw-Hill.
Skyttner, L. (2020). General systems theory: Problems, perspectives, practice (3rd ed.). World Scientific Publishing. https://doi.org/10.1142/11109
Slack, N., Brandon-Jones, A., & Johnston, R. (2022). Operations management (10th ed.). Pearson.
Tristán-López, A. (2008). Modificación al modelo de Lawshe para el dictamen cuantitativo de la validez de contenido de un instrumento objetivo. Avances en Medición, 6, 37–48.
Turban, E., Pollard, C., & Wood, G. (2018). Information technology for management. Wiley.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2020). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
World Economic Forum. (2023). Digital transformation of supply chains. Geneva, Switzerland.
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