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Full-Text Articles in Management and Operations
Segmentation Of Severe Occupational Incidents In Agribusiness Industries Using Latent Class Clustering, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Segmentation Of Severe Occupational Incidents In Agribusiness Industries Using Latent Class Clustering, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Faculty Publications
One of the principle objectives in occupational safety analysis is to identify the key factors that affect the severity of an incident. To identify risk groups of occupational incidents and the factors associated with them, statistical analysis of workers’ compensation claims data is performed using latent class clustering, for the segmentation of 1031 severe occupational incidents in agribusiness industries in the Midwest region of the United States between 2008–2016. In this study, severe incidents are those with workers’ compensation costs equal to or greater than $100,000 (USD). Based on the latent class clustering results, three risk groups are identified with …
Use Of Logistic Regression To Identify Factors Influencing The Post-Incident State Of Occupational Injuries In Agribusiness Operations, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Use Of Logistic Regression To Identify Factors Influencing The Post-Incident State Of Occupational Injuries In Agribusiness Operations, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Faculty Publications
Agribusiness industries are among the most hazardous workplaces for non-fatal occupational injuries. The term “post-incident state” is used to describe the health status of an injured person when a non-fatal occupational injury has occurred, in the post-incident period when the worker returns to work, either immediately with zero days away from work (medical state) or after a disability period (disability state). An analysis of nearly 14,000 occupational incidents in agribusiness operations allowed for the classification of the post-incident state as medical or disability (77% and 23% of the cases, respectively). Due to substantial impacts of occupational incidents on labor-market outcomes, …
Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Faculty Publications
Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, …
Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models And Monte Carlo Simulation, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models And Monte Carlo Simulation, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher
Faculty Publications
Insurance practitioners rely on statistical models to predict future claims in order to provide financial protection. Proper predictive statistical modeling is more challenging when analyzing claims with lower frequency, but high costs. The paper investigated the use of predictive generalized linear models (GLMs) to address this challenge. Workers’ compensation claims with costs equal to or more than US$100,000 were analyzed in agribusiness industries in the Midwest of the USA from 2008 to 2016. Predictive GLMs were built with gamma, Weibull, and lognormal distributions using the lasso penalization method. Monte Carlo simulation models were developed to check the performance of predictive …