Högskolan i Skövde

This study applies an intersectional approach to address concerns about diversity of data acquisition when applying computer vision systems in human order picking. The study draws empirical data from a single case study conducted at an automotive manufacturer. It identifies critical factors of intersectionality for the use of vision systems to enrich data collection in human order picking at four levels including form and function, experience and services, systems and infrastructure, and paradigm and purpose. These findings are helpful for mitigating bias and ensuring accurate representation of the target population in training datasets. The results of our study are indispensable for enhancing human-centricity when applying computer vision systems, and facilitating the acquisition of unstructured data in human order picking. The study contributes to enhancing diversity in human order picking, a situation that is highly relevant because of the variations in age, gender, cultural background, and language of staff. The study discusses theoretical and managerial implications of findings, alongside suggestions for future research.