Empirical evidence on human learning and work characteristics in the transition to automated order picking

Abstract

Although technological innovation has enabled a new wave of warehouse automation, human involvement remains necessary for most order picking operations in grocery retailing. This has spawned new forms of interaction between humans, machines, and intelligent software, that is, cyber-sociotechnical systems. However, scant empirical field-based research has been conducted on how this transition impacts human learning and the perception of work characteristics. Considering that humans are an essential element of these systems, it is fundamentally important to quantify the impact of these transformations when aspiring to improve performance, quality, and workers' well-being as primary outcomes of order picking systems. This study utilized a mixed-methods design, developing and applying parametric and non-parametric approaches to operationalize learning progress, and semi-structured interviews were conducted to examine perceived work characteristics. The findings indicate that the perception–cognition–motor–action cycle for learning by doing tasks can be accelerated through real-time feedback provided by the order picking system. Furthermore, perceived work autonomy and feedback from the picking system are constant or perceived as greater when human decisions are accepted. The results have valuable implications for logistics practitioners, emphasizing the need for human-centered work system design.