How to maximise manual labour productivity in warehouse operations

Jim Liefer
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Jim Liefer: Ambi Robotics, USA

Journal of Supply Chain Management, Logistics and Procurement, 2023, vol. 5, issue 4, 313-324

Abstract: This paper discusses how to maximise manual labour productivity in warehouse operations by utilising a mix of tools, teams and techniques to help warehouses improve worker efficiency, safety and retention while putting people where they can make the most impact. Associates are critical to warehouse operations, and warehouses are seeking ways to maximise labour efficiencies while also reducing costly churn. To be successful, warehouses must solve one of the most basic challenges: how to staff hard-to-fill and hard-to-do jobs. Warehouses are known for being dirty, dull and dangerous. Turnover is high and the impact of labour challenges is enormous. Robots work hard so people can work smart. Warehouses that implement advanced technology realise enormous benefits, including up to 4x more throughput. The robots-as-a-service (RaaS) business model allows warehouses to implement advanced AI technology without an enormous upfront spend. The best use of advanced tech is to support the human workforce and unlock the best solution to attaining and retaining warehouse labour. Jobs become reimagined by ‘up-levelling’ warehouse work and labour productivity increases. Advanced artificial intelligence (AI) captures data that helps to forecast inbound parcel volume, understand the profile of the parcels, predict throughput required to meet business goals and more. Robotic solutions offer consistent and reliable performance in the warehouse, delivering dependable, round-the-clock productivity, so that humans do not have to be burdened with monotonous, repetitive robot work.

Keywords: warehouse operations; manual labour productivity; worker efficiency; safety and retention; robots-asa- service (RaaS); advanced AI technology (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:4:p:313-324

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