Exploring continuous improvement for safety management systems through artificial neural networks Online publication date: Fri, 08-Oct-2021
by Marialuisa Menanno; Matteo Mario Savino; Filippo Emanuele Ciarapica
International Journal of Product Development (IJPD), Vol. 25, No. 3, 2021
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Product Development (IJPD):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook