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

Ksenia Palke and Spence Lunderman
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Ksenia Palke: Airspace, USA
Spence Lunderman: Airspace, USA

Journal of Supply Chain Management, Logistics and Procurement, 2023, vol. 5, issue 3, 202-211

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.

Keywords: machine learning; artificial intelligence; predictive analytics; automation; Big Data; next generation; optimisation; innovation; efficiency (search for similar items in EconPapers)
JEL-codes: L23 M11 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:aza:jscm00:y:2023:v:5:i:3:p:202-211

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