A procedural model for exoskeleton implementation in intralogistics - Carsten Feldmann; Victor Kaupe; Martin Lucas (Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29, 2020)
Purpose: Exoskeletons are robotic devices worn on the human body which mechanically support the operator's muscle skeleton. This study answers the following research question: Given insights drawn from a comprehensive literature analysis and two case studies which concern success factors for deployment projects, how can a systematic procedural model be used to support exoskeleton implementations in intralogistics? Methodology: This study follows the design-science research process developed by Peffers et al. (2006). The research gap was identified based on a systematic and comprehensive review of literature which reflects the current state of research. Insights gained via this process were compared with empirical data from pilot installations at two case companies: a Swedish market leader in the furniture industry and a leading German coatings manufacturer. Findings: A procedural model was designed to systematically consider success factors for an implementation which involves (1) workplace context; (2) human context and exoskeleton selection; (3) economic context; (4) pilot testing, evaluation, and maintenance; (5) deployment and training; and (6) go-live and support. It addresses technical, commercial, and social domains. The latter is critical to success, as it ensures staff acceptance. Originality: Exoskeletons can contribute to solving challenges such as demographic transitions and skills shortages in logistics. The procedural model closes a research gap from a scientific perspective and enables practitioners to exploit the potentials of successful exoskeleton introduction. Case studies in two different branches ensure practical relevance and significantly expand the state of research regarding the efficient achievement of implementation goals.
urn:nbn:de:gbv:830-882.0114868; hdl:10419/228914; https://econpapers.repec.org/RePEc:zbw:hiclpr:29
Keywords: 650; Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science