Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry

Abstract

Data on occupational accidents are usually stored in large databases by worker compensation authorities, and by the safety and prevention teams of companies. An analysis of these databases can play an important role in the prevention of accidents and the reduction of risks, but it can be a complex procedure because of the dimensions and complexity of such databases. The SKM (SOM K-Means) method, a two-level clustering system, made up of SOM (Self Organizing Map) and K-Means clustering, has obtained positive results in identifying the dynamics of critical accidents by referring to a database of 1200 occupational accidents that had occurred in the wood industry. The present research has been conducted to validate the recently presented SKM methodology through the analysis of a larger data set of more than 4000 occupational accidents that occurred in Piedmont (Italy), between 2006 and 2013. This work has partitioned the accidents into groups of different accident dynamics families and has quantified the severity and frequency of occurrence of these accidents. The obtained information may be of help to Company Managers and National Authorities to better address preventive measures and policies concerning the clusters that have been identified as being the most critical within a risk-based decision-making framework.

Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

“…The management of occupational safety risks is a significant component of any business [42]. Analysis of the incidents helps occupational risk managers identify which hazards have contributed and led to the most frequent occupational accidents, and thus determine appropriate preventative actions [43]. Understanding the relationships between individual predictors of occupational incidents contributes to the development of focused mitigation strategies to prevent workers' injuries [39].…”

Risk Profiling from the European Statistics on Accidents at Work (ESAW) Accidents′ Databases: A Case Study in Construction Sites

“…In other words, the combination of the MoD filtering and the aggregation through the cluster analysis can provide practical suggestions of where and how to act in order to reduce the repetition of similar accidents. Such an approach accomplishes other studies that have dealt with the management of ESAW data by means of cluster analysis [31,32]. However, while the latter consider any kind of accident occurred in a certain type of industry providing a wide perspective on it, our study is focused on the analysis of a specific type of accident to effectively draw up an accident scenario to be used to determine specific risk profiles, consistently with [30].…”

“…More in detail, this research project was articulated into three different main phases, concerning: data collection; cluster analysis; and the identification of the incident′s total risk potential by means of the Analytic Hierarchy Process (AHP) method. As already underlined, other studies applied the Kohonen′s Self-Organizing Map (SOM) and the k-means clustering algorithm to identify the most critical groups of occupational accidents from ESAW data [31,32]. Therefore, on the one hand, the benefits emerging from the use of cluster analysis and its extensions for the elicitation of safety information from incidents statistics to be used also in a predictive manner are clearly deemed.…”

“…Palamara et al [31] proposed the joint use of the Self Organizing Map (SOM) artificial neural network as a supportive tool for the k-means clustering algorithm to filter ESAW data on accidents occurred in the wood processing industry. An augmentation of this approach to deal with accidents′ variables more effectively was proposed by Comberti et al [32], who also provided a review of studies of ESAW data treatment models to support risk assessment.…”

Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation

“…The management of occupational safety risks is a significant component of any business [41]. Analysis of the incidents helps occupational risk managers identify which hazards have contributed and led to the most frequent occupational accidents, and thus determine appropriate preventative actions [42]. Analyzing empirical data to extract risk indicators adds predictivity to risk scenarios and helps in efficiently planning and modifying loss approaches in agribusiness industries.…”