Lean manufacturing and ergonomics integration: defining productivity and wellbeing indicators in a human–robot workstation

• Main Author: Colim, Ana
• Publication Date: 2021
• Other Authors: Morgado, Rita, Carneiro, P., Costa, Nélson Bruno Martins Marques da, Faria, Carlos, Sousa, Nuno Miguel Alves, Rocha, Luís A., Arezes, P.
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/1822/72450
• topic: Ergonomics and human factors; Lean manufacturing; Collaborative robotics; Productivity; Musculoskeletal risk; Science & Technology

Lean Manufacturing (LM), Ergonomics and Human Factors (E&HF), and Human–Robot Collaboration (HRC) are vibrant topics for researchers and companies. Among other emergent technologies, collaborative robotics is an innovative solution to reduce ergonomic concerns and improve manufacturing productivity. However, there is a lack of studies providing empirical evidence about the implementation of these technologies, with little or no consideration for E&HF. This study analyzes an industrial implementation of a collaborative robotic workstation for assembly tasks performed by workers with musculoskeletal complaints through a synergistic integration of E&HF and LM principles. We assessed the workstation before and after the implementation of robotic technology and measured different key performance indicators (e.g., production rate) through a time study and direct observation. We considered 40 postures adopted during the assembly tasks and applied three assessment methods: Rapid Upper Limb Assessment, Revised Strain Index, and Key Indicator Method. Furthermore, we conducted a questionnaire to collect more indicators of workers’ wellbeing. This multi-method approach demonstrated that the hybrid workstation achieved: (i) a reduction of production times; (ii) an improvement of ergonomic conditions; and (iii) an enhancement of workers’ wellbeing. This ergonomic lean study based on human-centered principles proved to be a valid and efficient method to implement and assess collaborative workstations, foreseeing the continuous improvement of the involved processes.