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Open AccessFeature PaperArticle

Managing Human Factors to Reduce Organisational Risk in Industry

1
Dipartimento dell’Innovazione Industriale e Digitale (DIID), Università degli Studi di Palermo, 90133 Palermo, Italy
2
Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain
3
Studio di Ingegneria Carpitella, 91100 Trapani, Italy
*
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2018, 23(4), 67; https://doi.org/10.3390/mca23040067
Received: 20 September 2018 / Revised: 23 October 2018 / Accepted: 23 October 2018 / Published: 25 October 2018
(This article belongs to the Special Issue Mathematical Modelling in Engineering & Human Behaviour 2018)
Human factors are intrinsically involved at virtually any level of most industrial/business activities, and may be responsible for several accidents and incidents, if not correctly identified and managed. Focusing on the significance of human behaviour in industry, this article proposes a multi-criteria decision-making (MCDM)-based approach to support organizational risk assessment in industrial environments. The decision-making trial and evaluation laboratory (DEMATEL) method is proposed as a mathematical framework to evaluate mutual relationships within a set of human factors involved in industrial processes, with the aim of highlighting priorities of intervention. A case study related to a manufacturing process of a real-world winery is presented, and the proposed approach is applied to rank human factors resulting from a previous organisational risk evaluation from which suitable inference engines may be developed to better support risk management. View Full-Text
Keywords: human behaviour; organisational risk; multi-criteria decision-making; DEMATEL; bottling process human behaviour; organisational risk; multi-criteria decision-making; DEMATEL; bottling process
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MDPI and ACS Style

Carpitella, S.; Carpitella, F.; Certa, A.; Benítez, J.; Izquierdo, J. Managing Human Factors to Reduce Organisational Risk in Industry. Math. Comput. Appl. 2018, 23, 67.

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