The increasing usage of cobot applications reshapes work environments and working conditions, requiring specific advancements in organizational practices for health and safety. Enterprises should shift from a technocentric risk management approach to considering cobot application as socio-technical systems, for which a resilience engineering approach is beneficial. This study presents an instantiation of the resilience analysis grid in cobot applications with the aim of measuring resilience potentials in terms of the four cornerstones of resilience engineering (respond, learn, monitor, and anticipate). The assessment has been provided via a questionnaire to 15 companies making use of cobot applications. Results revealed that companies mainly focus on the risk assessment of cobot applications with a traditional view of machine-centric safety, paying less attention to assessing contexts and process variables. This observation seems to arise mainly due to the lack of formally available safety methods or limited guidance from technical standards. Additionally, traditional industrial approaches to risk management lack monitoring of several risks that are essential for managing resilience, defined as the adaptive capacity of people, organizations, and human-machine systems. In addition, companies strongly rely on data from the cobot manufacturer for their safety assessment. The resilience analysis grid was confirmed as a valuable assessment tool for the participating companies to identify improvement areas and assess health and safety from a resilience engineering perspective.

Resilience potentials for health and safety management in cobot applications using the resilience analysis grid / Adriaensen, A.; Bernabei, M.; Costantino, F.; Falegnami, A.; Stabile, S.; Patriarca, R.. - In: JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. - ISSN 1087-1357. - 145:10(2023). [10.1115/1.4062786]

Resilience potentials for health and safety management in cobot applications using the resilience analysis grid

Adriaensen A.
;
Bernabei M.;Costantino F.;Falegnami A.;Patriarca R.
2023

Abstract

The increasing usage of cobot applications reshapes work environments and working conditions, requiring specific advancements in organizational practices for health and safety. Enterprises should shift from a technocentric risk management approach to considering cobot application as socio-technical systems, for which a resilience engineering approach is beneficial. This study presents an instantiation of the resilience analysis grid in cobot applications with the aim of measuring resilience potentials in terms of the four cornerstones of resilience engineering (respond, learn, monitor, and anticipate). The assessment has been provided via a questionnaire to 15 companies making use of cobot applications. Results revealed that companies mainly focus on the risk assessment of cobot applications with a traditional view of machine-centric safety, paying less attention to assessing contexts and process variables. This observation seems to arise mainly due to the lack of formally available safety methods or limited guidance from technical standards. Additionally, traditional industrial approaches to risk management lack monitoring of several risks that are essential for managing resilience, defined as the adaptive capacity of people, organizations, and human-machine systems. In addition, companies strongly rely on data from the cobot manufacturer for their safety assessment. The resilience analysis grid was confirmed as a valuable assessment tool for the participating companies to identify improvement areas and assess health and safety from a resilience engineering perspective.
2023
collaborative robot; cyber-socio-technical systems; design for manufacturing; process engineering; production systems optimization; resilience analysis grid; resilience assessment grid; resilience engineering; robotics and flexible tooling; Systemic potentials management
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Resilience potentials for health and safety management in cobot applications using the resilience analysis grid / Adriaensen, A.; Bernabei, M.; Costantino, F.; Falegnami, A.; Stabile, S.; Patriarca, R.. - In: JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. - ISSN 1087-1357. - 145:10(2023). [10.1115/1.4062786]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1693686
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