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Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.