Performance Analysis on Health and Safety Issues of Companies from the Slaughterhouse Industry

Abstract

The objective of this article is to analyses the performance of companies of the slaughterhouse industry in health and safety issues. The research method is the quantitative modelling. The main research technique uses a mixed method based on multi-attribute utility method (MAUT) and artificial neural networks (ANN). The research object are 34 slaughterhouse companies located in Southern Brazil. Then, we ranked the companies and modeled their decision trees using the MAUT method. From these results, neural networks were used to benchmark and compare the methods. This resulted in a linear equation that represents the closest solution to the ideal and percentage error in the decision tree’s resolution. Thus, neural networks are most efficient, because they indicate which KPI’s (key performance indicators) most influence the organization’s performance. We numerically present the gain of information and the margin of error, concluding that some KPI’s do not influence competitiveness without requiring controls. The academic and social contribution is that through the union of MAUT and neural networks we can measure the performance and select the main KPIs that need to be controlled for any type of industry.