Full shift usage of smart glasses in order picking processes considering a methodical approach of continuous user involvement

Abstract

Smart factories, in which processes are partially or completely automated and digitalized, are the future. Automobile production is also currently undergoing said change towards ‘Industry 4.0’. While the degree of automation is very high in car body construction due to the use of robots, the degree of automation in logistics varies depending on the process. By contrast, in transportation processes, such as the use of tugger trains, autonomous transport systems or forklifts, automation is at a very advanced stage of development. In picking processes the employee continues to be at the center of attention due to his flexibility in movement sequences and grasping. Assistance systems such as smart glasses, smart watches or scanner gloves can nevertheless support employees and provide process improvements. The present research project focuses on the use of Augmented Reality (AR) technologies, in particular the usage of smart glasses in manual man-to-goods order picking processes. The required information for order picking is displayed in smart glasses in the picker’s field of view. In scientific research, their usage has been tested primarily under laboratory conditions. I subsequently identified a research gap investigating a full shift usage of smart glasses under real industrial production conditions. For this reason, the present PhD thesis includes a full shift field study, which examines the impact of the smart glasses usage in order picking processes on the employee and the process. As a first step, a process for shop floor employee involvement in innovation projects based on Rogers’ ‘Diffusion of Innovations’ will be developed. Afterwards, the suitability of workstations for ARusage will be evaluated using the Rasmussen skills-rules-knowledge-framework. In addition, an objective procedure for classifying individual activities into Rasmussen’s framework will be developed. After selecting the test workstation, which contains most of the process steps supportable by AR-technologies, I will compare scanning mechanisms for the interaction with the warehouse management system. The core of the research project is a full shift field study with 23 employees in automotive assembly supply, which examines the impact of a smart glasses usage on the number of errors, the error types, the task completion time, the attention, the subjective strain, the visual fatigue of the employees and the likelihood of symptomatic occurrence of simulator sickness. In contrast to most existing studies, the field study is conducted in real production in accordance to the rhythm of the assembly line. The test system is connected to the warehouse management system. Employees interact with the warehouse management system with the aid of a scanner. The age range of the participants represents the regular workforce. Besides that, I will select a smart glasses hardware, which enables the inclusion of corrective glasses wearers. All the above points contribute to a test under real industrial conditions. As a basis for comparison serves the previous process, which displays the picking orders on a monitor attached to the target shelf. In this system a scan glove is used as interaction mechanism. The evaluation of the study results determine a time saving of 22% and an error reduction of at least 33%, depending on the error type. Due to these promising results, I explain further steps for series introduction. In addition, I discuss further potentials of the usage of data glasses in order picking processes.