@article{ref1,
title="Prioritization of OHS key performance indicators that affecting business competitiveness - a demonstration based on MAUT and Neural Networks",
journal="Safety science",
year="2019",
author="Nara, Elpidio Oscar Benitez and Sordi, Diane Cristina and Schaefer, Jones Luis and Schreiber, Jacques Nelson Corleta and Baierle, Ismael Cristofer and Sellitto, Miguel Afonso and Furtado, João Carlos",
volume="118",
number="",
pages="826-834",
abstract="This research wanted to provide evidence that health and worker safety affects the competitiveness of companies and to identify the KPIs that influence the competitive results. For this, we choose to study the Brazilian slaughterhouse industry, which is characterized by an extensive and dangerous work. We identified 33 key performance indicators (KPI) of Occupational Health and Safety (OHS) based on Norm 36 of the Ministry of Labor in Brazil. With the application of the Multi Attribute Utility Theory (MAUT) method we determined the values of Individual Competitiveness Rate (ICR) of each company and ranked them. For ranking and verification purposes we used Artificial Neural Networks (ANN), which allowed the identification of relevant KPIs and to rank, identify, and visualize the differences in relative importance among them. The results show that the Global Competitiveness Rate (GCR) ideal for companies as provided by specialists is 3.48 (1 to 4 scale), and that only 10% of companies have ICR equal to or greater than the GCR pointed out as ideal by specialists. With this data, you can make the companies act in a more targeted way on the indicators, always looking to raise their ICR. The originality of this article is that, through the integration of MAUT and ANN, we can accelerate probabilistic calculations and through the information gain we can identify which KPIs have the most influence on competitiveness and therefore need special attention.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2019.06.017",
url="http://dx.doi.org/10.1016/j.ssci.2019.06.017"
}