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A systematic review of digital management tools and their impact on operational efficiency in heavy-duty freight transportation

Mark C. Barrueta et al.

World Review of Intermodal Transportation Research2025https://doi.org/10.1504/writr.2025.151056article
AJG 1
Weight
0.50

What the paper says

This study presents a systematic review of the literature on the impact of digital management tools on the operational efficiency of heavy-duty freight transportation. It analyses technologies such as artificial intelligence (AI), blockchain, and telemetry, which have proven to improve logistics planning, optimise route management, and reduce operational costs. Despite these benefits, significant barriers to implementation are identified in developing regions, where limited technological infrastructure and high costs hinder adoption. Results indicate improvements of up to 25% in operational efficiency for companies that have implemented digital tools. Furthermore, gaps in the literature suggest the need for research into the long-term impact of these technologies across diverse geographical and economic contexts. The study concludes with recommendations for future research and policies that facilitate a more inclusive and effective adoption of digital tools in the heavy freight transportation sector.

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https://doi.org/https://doi.org/10.1504/writr.2025.151056

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@article{mark2025,
  title        = {{A systematic review of digital management tools and their impact on operational efficiency in heavy-duty freight transportation}},
  author       = {Mark C. Barrueta et al.},
  journal      = {World Review of Intermodal Transportation Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/writr.2025.151056},
}

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A systematic review of digital management tools and their impact on operational efficiency in heavy-duty freight transportation

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.