Multi-assistance systems in manufacturing - a user study evaluating multi-criteria impact in a high-mix low-volume assembly setting

Abstract

Understanding how skilled workers interact with assistance systems in manufacturing and how they experience the factory environment is fundamental to modeling human interaction and optimizing the processes correctly. This paper investigates humans’ behaviors and perceived experiences while interacting with cognitive and physical assistance systems. To enable decisions about the combined use of more than one assistance system within a manufacturing process, comprehensive and comparable knowledge about the impact of applications on productivity and human factors is needed. A multidimensional evaluation model with a mixed-methods approach was developed and applied in a user study. In 300 run-throughs in six different scenarios with skilled workers and students, a questionnaire on human factors was completed after finishing the task. Furthermore, productivity and quality were measured during the study. A comparison between skilled workers and students demonstrated that the usability score of all assistance systems was rated higher among the students. The students rated the ergonomics aspects better for five out of six scenarios. Results show higher compatibility with values and experiences in all investigated combinations for skilled workers than for students. Considering the collected data among the skilled workers, the overall compatibility with experience and values of multi-assistance system scenarios was more positive than in the single-assistance system scenarios. Our results show no significant differences in ergonomics, mental, physical, and temporal workload between single and multi-assistance system settings. With 150 run-throughs of industrial professionals and campus recruits each, the survey joins only two studies with more than 100 participants. To the authors' knowledge, it is the first systematic multi-criteria evaluation for the combined use of several (cognitive and physical) industrial assistance systems. The results help to ease practitioners’ evaluation of technical support systems in manufacturing with an emphasis on multi-criteria evaluation and the consideration of interconnected (cognitive and physical) assistance systems. Furthermore, the results contribute to further research in human–machine interaction and its impact on productivity and human factors as they show potentials and prospective challenges of the implementation and application of multiple assistance systems.