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AI-Driven Energy Optimization in Urban Logistics: Implications for Smart SCM in Dubai

Author

Listed:
  • Baha M. Mohsen

    (Faculty of Business Management, Emirates Aviation University, Dubai P.O. Box 53044, United Arab Emirates)

  • Mohamad Mohsen

    (College of Business, Eastern Michigan University, Ypsilanti, MI 48197, USA)

Abstract

This paper aims to explore the role artificial intelligence (AI) technologies play in optimizing energy consumption levels in urban logistical systems, including the strategic implications of such technologies on smart supply chain management (SCM) in Dubai. The mixed-methods study was adopted and applied, in which quantitative measures of the performance of 16 public–private organizations were merged with qualitative evidence provided through semi-structured interviews and document analysis. AI solutions that were assessed in the research included the use of predictive routing, dynamic fleet scheduling, IoT-base monitoring, and smart warehousing. Results indicate an overall decrease of 13.9% in fuel consumption, 17.3% in energy and 259.4 kg in monthly CO 2 emissions by the organization on average by adopting AI. These findings were proven by the simulation model, which estimated that the delivery efficiency would increase within an AI-driven scenario and be scalable in the future. Other important impediments were also outlined in the study, such as constraint of legacy systems, skills gap, and interoperability of data. Implications point to the necessity of the incorporation of digital governance, data protocol standardization, and AI-compatible city planning to improve the urban SCM of Dubai, through the terms of sustainability and resilience. In this study, a transferable structure is provided that can be utilized by cities that are interested in matching AI innovation and energy and logistics goals, in terms of policy objectives.

Suggested Citation

  • Baha M. Mohsen & Mohamad Mohsen, 2025. "AI-Driven Energy Optimization in Urban Logistics: Implications for Smart SCM in Dubai," Sustainability, MDPI, vol. 17(18), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8301-:d:1750410
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    References listed on IDEAS

    as
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    2. Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
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    4. Baha M. Mohsen, 2024. "AI-Driven Optimization of Urban Logistics in Smart Cities: Integrating Autonomous Vehicles and IoT for Efficient Delivery Systems," Sustainability, MDPI, vol. 16(24), pages 1-24, December.
    5. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    6. Fahimnia, Behnam & Sarkis, Joseph & Davarzani, Hoda, 2015. "Green supply chain management: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 162(C), pages 101-114.
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