Human factors in order picking systems

Order picking, which is defined as the process of retrieving items from their storage locations in a warehouse to fulfill customer orders, represents the most costly and time-consuming warehouse process. Despite various technical possibilities to automate order picking, it continues to be performed predominantly manually with a high amount of human work elements in most warehouses, which makes the worker a determinant of operational outcome. This renders the labor-intensive order picking process a very costly activity, which is why warehouse managers aim at performing order picking as efficiently as possible. For that reason, researchers have developed various planning models that help to improve manual order picking processes. This cumulative dissertation identifies a major drawback in existing management decision and planning models for order picking, namely that human factors have been ignored so far in these models. Eric Grosse develops a conceptual framework that facilitates the integration of human factors in order picking planning models. Employing various research methods, this cumulative dissertation shows that worker characteristics, such as experience level, learning ability or motivation determine order picking process efficiency and improvement possibilities. Despite the impact on performance, the need for integrating human factors in order picking system design to reduce the risk of workers developing health problems is discussed. Researchers may build on the conceptual framework, and they may address the plethora of research opportunities in considering human factors in order picking that have been identified. Warehouse managers may benefit from the various practical insights that may facilitate long-term sustainable warehouse management.