Artificial intelligence in materials handling: How machine learning tools boost warehouse safety, productivity and cost-effectiveness

Brien Downie, Marc Gyöngyösi and Chris Kuehl
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Brien Downie: Holman Logistics, USA
Marc Gyöngyösi: OneTrack AI, USA
Chris Kuehl: Armada Corporate Intelligence, USA

Journal of Supply Chain Management, Logistics and Procurement, 2021, vol. 4, issue 1, 6-16

Abstract: This paper explores the growing potential of artificial intelligence (AI) and machine learning (ML) to bring about improvements in safety, which in turn can boost cost-effectiveness, productivity and operational efficiencies in a warehouse setting. While there is significant evidence in the literature on the impact AI is having in other areas of the supply chain, the authors believe the specific use of AI and ML in the warehouse has been underexplored. Companies that embrace machine-learning technologies and tools as a way to reduce incidents in their warehouses are improving worker safety, increasing productivity, and potentially yielding a competitive advantage for their businesses. This paper’s main purpose is to demonstrate, through a use-case approach, the clear benefits of these technologies and to promote further exploration of the potential of AI to drive improvements in the safety of materials handling in warehouse settings.

Keywords: case studies; materials management; technology management; transportation; distribution; logistics; warehousing (search for similar items in EconPapers)
JEL-codes: L23 M11 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:aza:jscm00:y:2021:v:4:i:1:p:6-16

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