Nowadays, several industrial companies and distribution centres have even more interest in implementing automated systems for warehousing operations due to new market developments such as e-commerce. Robotized and Automated Warehouse Systems are generally composed of a storage area, with shuttles, mini-loads or robots, conveyor systems for handling storage bins and cartoon boxes and picking workstations. The bins’ storage is performed automatically while picking requires human interventions. Defining the number and the configuration of the picking workstations represents a tactical decision since they strongly influence the conveyor system length and, thus, the total cost of such types of warehousing solutions. Moreover, they influence the efficiency and productivity of the storage area too. Finally, in such types of systems, classified as parts-to-pickers systems, workers are exposed to repetitive movements of the upper extremities and long-standing working postures. Consequently, picking workstations must be appropriately designed by a human factor perspective, aiming to ensure the physical well-being of workers as well as high efficiency while performing order picking to fulfil customers' orders. Nevertheless, many studies on parts-to-pickers systems have focused on global system performance, while human factors and their influence on system efficiency have rarely been considered.For this reason, this paper aims to close the gap by investigating how body posture and ergonomic assessment can influence picking efficiency in some advanced picking workstation configurations that can be implemented in Robotized and Automated Warehouse Systems. We analyze 16 picking workstation solutions from an ergonomic and productivity point of view, helped by a motion capture system and an ergo-digital platform. All configurations are simulated in our laboratory. Based on the results obtained, managerial guidelines are provided by highlighting the pros and cons of each picking workstation. Moreover, we compare ergonomics aspects with the picking time according to the number of storage bins that arrives simultaneously on the picking workstation. Results demonstrate that configurations with the storage bins placed above the order bin and in front of the worker lead to better posture and lower picking time. However, additional parameters, such as the time required to change the storage bin, the number of order lines and the number of items to pick from each storage bin, strongly influence the choice of the most suitable picking workstation. For this reason, a decision support tool is proposed to guide managers in selecting the proper picking workstation
