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Data Storytelling and Decision-Making in Seaport Operations: A New Approach Based on Business Intelligence

Author

Listed:
  • Marco Gonçalves

    (Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, 3810-193 Aveiro, Portugal)

  • Cátia Salgado

    (Administração dos Portos de Sines e do Algarve (APS), 7521-953 Sines, Portugal)

  • Amaro de Sousa

    (Institute of Telecommunications—Pole of Aveiro (IT), Department of Electronics, Telecommunications and Informatics (DETI), University of Aveiro, 3810-193 Aveiro, Portugal)

  • Leonor Teixeira

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

Abstract

Seaports are experiencing several challenges due to the explosive growth of the maritime shipping business, which has led to the need for digitalized operations and more effective solutions. This article provides a comprehensive exploration of the process used to create a reliable business intelligence solution by analyzing the container delivery and pick-up services flow in one of Portugal’s largest maritime container ports, using the CRISP-DM methodology. The solution, built with Microsoft Power BI ® , provides the capability to identify and address data anomalies and present key performance indicators in visually dynamic dashboards. This solution empowers stakeholders to gain invaluable insights into the current and future operational status, thereby facilitating well-informed and adaptable decision-making, representing the main practical contributions. As a theoretical contribution, this study advances research by covering a gap in the literature and establishing the foundations for future business intelligence applications within the maritime industry, with a focus on addressing data dispersion challenges, enhancing logistics flow analysis, and reducing port congestion. The manuscript is structured into seven sections: introduction, literature review, port challenges, methodology, tool development, SWOT analysis, and conclusion.

Suggested Citation

  • Marco Gonçalves & Cátia Salgado & Amaro de Sousa & Leonor Teixeira, 2025. "Data Storytelling and Decision-Making in Seaport Operations: A New Approach Based on Business Intelligence," Sustainability, MDPI, vol. 17(1), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:337-:d:1560309
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    References listed on IDEAS

    as
    1. Nicoleta González-Cancelas & Beatriz Molina Serrano & Francisco Soler-Flores & Alberto Camarero-Orive, 2020. "Using the SWOT Methodology to Know the Scope of the Digitalization of the Spanish Ports," Logistics, MDPI, vol. 4(3), pages 1-20, September.
    2. Julian Neugebauer & Leonard Heilig & Stefan Voß, 2024. "Digital Twins in the Context of Seaports and Terminal Facilities," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 821-917, September.
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    5. Hokey Min & Yong-Kon Lim & Jong-Won Park, 2017. "Supply chain analytics for enhancing the maritime security," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 28(2), pages 164-179.
    6. Sugrue, Dennis & Adriaens, Peter, 2021. "A data fusion approach to predict shipping efficiency for bulk carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
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