IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v11y2024i11p22-30.html
   My bibliography  Save this article

The Impact of Artificial Intelligence in Logistics and Supply Chain in the USA – Focusing on Leading Industries in the 21st Century

Author

Listed:
  • Benjamin Kwame Amponsah

    (Westcliff University, United States of America (USA))

  • Paul Boadu Asamoah

    (Westcliff University, United States of America (USA))

  • Manso Frimpong

    (Westcliff University, United States of America (USA))

Abstract

Artificial Intelligence plays a crucial role in global supply chain management and logistics. It builds opportunities for cost reduction in purchase requirement planning, demand forecasting, sales/marketing, transportation, packaging, warehousing, inventory, production planning, finance, customer services, and information services. It also offers a competitive edge to the firms utilizing it. Artificial intelligence will likely make insightful decisions and improve efficiency via exceptional capabilities. This research aims to highlight the contributions of AI to supply chain management and logistics by using a thorough literature review and systematic review of the current literature from the internet. This will focus on AI applications within top American industries, such as e-commerce, retail, pharmaceuticals, and automotive, by leveraging artificial intelligence tools such as machine learning, route optimization, predictive analytics, and robotics. This paper evaluates the benefits and challenges associated with artificial intelligence adoption, providing an extensive overview of how AI tools are rebuilding SCM performance while also spotlighting the challenges linked to implementation and data security. By supplementing the literature review with case studies and data analysis, this research highlights the pivotal role of AI in shaping the future of logistics and supply chain management in the 21st Century. This research shows that although AI provides significant opportunities for innovation and growth, it presents damaging challenges; hence, strategic planning and investment are needed to overcome these intertwined challenges. This study also offers insight and recommendations for industry stakeholders and policymakers aiming to leverage the full potential of AI in logistics and supply chain management operations.

Suggested Citation

  • Benjamin Kwame Amponsah & Paul Boadu Asamoah & Manso Frimpong, 2024. "The Impact of Artificial Intelligence in Logistics and Supply Chain in the USA – Focusing on Leading Industries in the 21st Century," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(11), pages 22-30, November.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:11:p:22-30
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-11-issue-11/22-30.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrsi/articles/the-impact-of-artificial-intelligence-in-logistics-and-supply-chain-in-the-usa-focusing-on-leading-industries-in-the-21st-century/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eugène Loos & Maria Sourbati & Frauke Behrendt, 2020. "The Role of Mobility Digital Ecosystems for Age-Friendly Urban Public Transport: A Narrative Literature Review," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
    2. Zulamir Hassani, Afdhal & Yusoff, Fazirah & Wan Zain, Wan Nor Aisyah, 2021. "Fair and Responsible in Logistics IR 4.0," MPRA Paper 108432, University Library of Munich, Germany.
    3. Fahad Alrukaibi & Rushdi Alsaleh & Tarek Sayed, 2019. "Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    4. Marya Butt & Ander de Keijzer, 2022. "Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells," Data, MDPI, vol. 7(9), pages 1-21, September.
    5. Sesil Koutra & Christos S. Ioakimidis, 2022. "Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges," Land, MDPI, vol. 12(1), pages 1-19, December.
    6. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    7. Dorota Kamrowska-Załuska, 2021. "Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities," Land, MDPI, vol. 10(11), pages 1-19, November.
    8. Volodymyr N. Skoropad & Stevica Deđanski & Vladan Pantović & Zoran Injac & Slađana Vujičić & Marina Jovanović-Milenković & Boris Jevtić & Violeta Lukić-Vujadinović & Dejan Vidojević & Ištvan Bodolo, 2025. "Dynamic Traffic Flow Optimization Using Reinforcement Learning and Predictive Analytics: A Sustainable Approach to Improving Urban Mobility in the City of Belgrade," Sustainability, MDPI, vol. 17(8), pages 1-31, April.
    9. Auwal Alhassan Musa & Salim Idris Malami & Fayez Alanazi & Wassef Ounaies & Mohammed Alshammari & Sadi Ibrahim Haruna, 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    10. Sohani Liyanage & Hussein Dia & Rusul Abduljabbar & Saeed Asadi Bagloee, 2019. "Flexible Mobility On-Demand: An Environmental Scan," Sustainability, MDPI, vol. 11(5), pages 1-39, February.
    11. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    12. Å. Jevinger & C. Zhao & J. A. Persson & P. Davidsson, 2024. "Artificial intelligence for improving public transport: a mapping study," Public Transport, Springer, vol. 16(1), pages 99-158, March.
    13. Waymond Rodgers & Mouza Al Habsi & George Gamble, 2019. "Sustainability and Firm Performance: A Review and Analysis Using Algorithmic Pathways in the Throughput Model," Sustainability, MDPI, vol. 11(14), pages 1-27, July.
    14. De Obesso Arias, María de las Mercedes & Pérez Rivero, Carlos Alberto & Carrero Márquez, Oliver, 2023. "Artificial intelligence to manage workplace bullying," Journal of Business Research, Elsevier, vol. 160(C).
    15. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    16. Phattarasuda Witchayaphong & Surachet Pravinvongvuth & Kunnawee Kanitpong & Kazushi Sano & Suksun Horpibulsuk, 2020. "Influential Factors Affecting Travelers’ Mode Choice Behavior on Mass Transit in Bangkok, Thailand," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    17. Giuseppe Guido & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Alessandro Vitale & Vittorio Astarita & Ashkan Shafiee Haghshenas, 2020. "Feasibility of Stochastic Models for Evaluation of Potential Factors for Safety: A Case Study in Southern Italy," Sustainability, MDPI, vol. 12(18), pages 1-24, September.
    18. Suleiman Hassan Otuoze & Dexter V. L. Hunt & Ian Jefferson, 2021. "Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    19. Przemysław Kowalik & Grzegorz Sobecki & Piotr Bawoł & Paweł Muzolf, 2023. "A Flow-Based Formulation of the Travelling Salesman Problem with Penalties on Nodes," Sustainability, MDPI, vol. 15(5), pages 1-28, February.
    20. Pamucar, Dragan & Deveci, Muhammet & Gokasar, Ilgin & Tavana, Madjid & Köppen, Mario, 2022. "A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:11:y:2024:i:11:p:22-30. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.