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How machine learning can improve decisions and automate manual processes in freight forwarding

Author

Listed:
  • Palke, Ksenia

    (Airspace, USA)

  • Lunderman, Spence

    (Airspace, USA)

Abstract

Machine learning (ML) is becoming ubiquitous, yet it is still heavily underutilised in the logistics industry. This paper showcases the role of ML in modernising decision making in freight forwarding. After introducing the concept of ML at its simplest application in freight forwarding, a few examples from a time-critical tech-enabled logistics company, Airspace, are showcased to support the idea. The benefits and costs of ML are highlighted with a focus on what business metrics are improved by implementing ML. In the current world, any logistics company not investing in ML development is abdicating strategic advantage to their competition and losing the ability to compete with the more technologically forward-facing companies.

Suggested Citation

  • Palke, Ksenia & Lunderman, Spence, 2023. "How machine learning can improve decisions and automate manual processes in freight forwarding," Journal of Supply Chain Management, Logistics and Procurement, Henry Stewart Publications, vol. 5(3), pages 202-211, March.
    • Handle: RePEc:aza:jscm00:y:2023:v:5:i:3:p:202-211
    as

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    More about this item

    Keywords

    machine learning; artificial intelligence; predictive analytics; automation; Big Data; next generation; optimisation; innovation; efficiency;
    All these keywords.

    JEL classification:

    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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