Human factors in order picking systems
A framework for integrating human factors in order picking planning models with an in-depth analysis of learning effects
Order picking, the process of retrieving items from storage to fulfill customer orders, is the most costly and time-consuming aspect of warehouse operations. Despite advancements in automation, most warehouses still rely heavily on manual labor, making workers critical to operational success. This labor-intensive process incurs significant costs, prompting warehouse managers to seek greater efficiency. Researchers have developed planning models to enhance manual order picking, but a significant oversight in these models is the neglect of human factors. This dissertation addresses this gap by proposing a conceptual framework that incorporates human elements into order picking planning. Through various research methods, it demonstrates that worker characteristics—such as experience, learning ability, and motivation—affect the efficiency of order picking and reveal potential for improvement. Additionally, the dissertation highlights the importance of integrating human factors into order picking system design to mitigate health risks for workers. The framework provides a foundation for further research into human factors in order picking and offers warehouse managers practical insights for achieving sustainable operations.
