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Digital Transformation in Maritime Ports: Defining Smart Gates through Process Improvement in a Portuguese Container Terminal

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  • Juliana Basulo-Ribeiro

    (Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Intelligent Systems Associate Laboratory (LASI), University of Aveiro, 3810-193 Aveiro, Portugal)

  • Carina Pimentel

    (Algoritmi Research Center, Department of Production and Systems, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal)

  • Leonor Teixeira

    (Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Intelligent Systems Associate Laboratory (LASI), University of Aveiro, 3810-193 Aveiro, Portugal)

Abstract

As the digital paradigm stimulates changes in various areas, seaports, which are fundamental to logistics and the global supply chain, are also undergoing a digital revolution, evolving into smart ports. Smart gates are essential components in this transformation, playing a vital role in increasing port efficiency. In the context of smart gates, the aim of this study is to understand how process management can serve as a catalyst for digital transformation, promoting efficiency in traffic flow and logistics. To achieve this objective, the design science research (DSR) methodology was followed, which allowed for the integration of information from several sources of requirement, encompassing both theoretical and practical aspects. The practical component took place at one of Portugal’s largest container terminals, which allowed for the integration of information from various sources. As a result, this study presents the conceptual definition of a smart gate in terms of processes, main technologies, and key performance indicators that will support the monitoring and improvement of future operations. The results provide theoretical and practical contributions: on a practical level, they present a real application of the transformation towards a smart gate, serving as a model for other ports in their digitalization; on a theoretical level, they enrich the literature with a methodology for digitalizing maritime road gates, showing how the use of process management approaches, such as the BPMN, can increase operational efficiency in container terminals.

Suggested Citation

  • Juliana Basulo-Ribeiro & Carina Pimentel & Leonor Teixeira, 2024. "Digital Transformation in Maritime Ports: Defining Smart Gates through Process Improvement in a Portuguese Container Terminal," Future Internet, MDPI, vol. 16(10), pages 1-27, September.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:10:p:350-:d:1486981
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    References listed on IDEAS

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    1. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    2. Chen, Rui & Meng, Qiang & Jia, Peng, 2022. "Container port drayage operations and management: Past and future," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    3. Luka Vukić & Kee-hung Lai, 2022. "Acute port congestion and emissions exceedances as an impact of COVID-19 outcome: the case of San Pedro Bay ports," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-26, December.
    4. Andrew Castle & Rachel Harvey, 2009. "Lean information management: the use of observational data in health care," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 58(3), pages 280-299, March.
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    Cited by:

    1. Raquel Gil Pereira & Rui Borges Lopes & Ana Martins & Bernardo Macedo & Leonor Teixeira, 2024. "A Simulation Tool to Forecast the Behaviour of a New Smart Pre-Gate at the Sines Container Terminal," Sustainability, MDPI, vol. 17(1), pages 1-29, December.

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