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Submitted April 13, 2026
Published 2026-07-01

Artículos científicos

Vol. 15 No. 2 (2026): Centros Revista Científica Universitaria

Measuring multimodal logistics performance: : a systematic review of metrics, models, and determinants


DOI https://doi.org/10.48204/j.centros.v15n2.a9723

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DOI: 10.48204/j.centros.v15n2.a9723

Published: 2026-07-01

How to Cite

Velasco Jaén, A. L. (2026). Measuring multimodal logistics performance: : a systematic review of metrics, models, and determinants. Centros: Revista Científica Universitaria, 15(2), 71–99. https://doi.org/10.48204/j.centros.v15n2.a9723

Abstract

Measuring multimodal logistics performance in nodes and corridors has become a strategic issue for operations management and transport planning; however, the available evidence remains fragmented across indicators, modelling approaches, and explanatory factors. This article critically synthesizes the literature through three analytical axes: metrics, models, and determinants. A qualitative systematic review with thematic and narrative synthesis was conducted using explicit criteria for search, eligibility, selection, extraction, and coding. The search was carried out between May 2025 and February 2026 in Scopus, Web of Science, Google Scholar, Redalyc, SciELO, Dialnet, Latindex, and LILACS, complemented by backward reference tracking. Forty studies were included in the final synthesis. Findings show a predominance of recent publications and convergence around metrics such as cost, transit time, punctuality, capacity, reliability, and service level, with increasing incorporation of sustainability and resilience dimensions. The main model families include simulation, optimization, multicriteria approaches, network assignment, and hybrid strategies. The most recurrent determinants are governance, interorganizational coordination, digital maturity, traceability, capacity planning, operational uncertainty, and institutional fragmentation. The review concludes that the main challenge in the field is no longer the lack of studies, but the weak comparability across metrics, models, and contexts. It provides an integrative analytical basis for future research and for the design of evaluation frameworks in emerging multimodal environments.

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