Title: Exploring continuous improvement for safety management systems through artificial neural networks
Authors: Marialuisa Menanno; Matteo Mario Savino; Filippo Emanuele Ciarapica
Addresses: Department of Engineering, University of Sannio, Benevento, Italy ' Department of Engineering, University of Sannio, Benevento, Italy ' Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, Ancona 60131, Italy
Abstract: The present work investigates safety assessment in Safety Management Systems with the twofold objective of (i) developing safety audits with objective definition of risk levels for workers' activities and (ii) concurrent prioritising the corrective actions. The work is developed within a firm producing automotive components, where safety audits have been managed through a management approach able to (i) define the risk factors for each activity and (ii) quantify the corresponding risk level that may require corrective actions. Risk prediction matrices have been conceived to assess the risk levels. Then, each matrix has been the training set of an Artificial Neural Network used to quantify the values of risk levels. The findings of this study provided some general principles to integrate safety assessment within the continuous improvement and some outcome relative to the use of artificial intelligence for safety assessments.
Keywords: industrial safety; risk assessment; continuous improvement; artificial neural networks.
International Journal of Product Development, 2021 Vol.25 No.3, pp.213 - 241
Received: 29 Sep 2020
Accepted: 13 Mar 2021
Published online: 08 Oct 2021 *