Human–robot vs. human–manual teams: Understanding the dynamics of experience and performance variability in picker-to-parts order picking
Year: 2024
Type: article
Abstract: Recent technological advances have enabled firms to automate an increasing number of intralogistics operations, yet manual material handling remains pivotal in several industries , particularly in retail applications where brick-and-mortar warehouses still rely on manual picker-to-parts systems. These systems, while labor-intensive, are increasingly being supplemented by technologies such as automated guided vehicles (AGVs) to enhance performance and reduce physical strain on workers. The present study analyzed a pilot test of a new industrial truck deployed as an AGV that automatically follows order pickers in their travels within the warehouse of a brick-and-mortar grocery retailer. The data set comprises 342,601 pick location visits performed in one dedicated warehouse from 01 November 2022 to 30 June 2023, with three order pickers working with an AGV in a human–robot setting and five order pickers working with a manual industrial truck in a human–manual setting, with both groups sharing the identical aisle work space. The human–robot teams demonstrated a 3.6% reduction in order picking time compared to the human–manual teams, with a significant initial performance boost that plateaued over time. Experience had different impacts on the two groups: in the human–robot teams, the benefits of experience diminished more rapidly, indicating a lower incremental gain from additional experience compared to human–manual teams. Conversely, human–manual teams showed continuous improvement in performance as experience accumulated, with each additional day of experience leading to significant gains in order picking performance. The study also highlighted the variation in performance increase in human–robot teams, suggesting that while AGVs may enhance performance, the potential for inconsistent operational methods among workers can lead to fluctuating performance outcomes. The findings provide guidance for researchers and managers in understanding the impacts of experience and automation on performance, thereby aiding in the development of targeted training programs and operational strategies to maximize the benefits of AGVs.
Author: Jonas Koreis
Institution: Technische Universität Darmstadt
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,440
OpenAlex ID: W4404958671