PhD, University of Cincinnati, 2008, Engineering : Industrial Engineering

Safety professionals and practitioners are always searching for methods to accurately assess the association between exposures and possible occupational disorders or diseases and predict the outcome of any outcome. Statistical analysis and logistic regression in particular are among the most popular tools being used by them. Artificial Neural Network (ANN) models are another method of predicting outcomes, which are gradually finding their way in the safety field. It has been shown that they are capable of predicting outcomes more accurately than logistic regression, but they are incapable of demonstrating the direct correlation between exposure variables and possible outcome variables. The first objective in this research was to demonstrate that Artificial Neural Network models can perform better that logistic regression models with data sets made of all ordinal variables, which has not been done so far. All the publications in this area were about either dichotomous or a combination of dichotomous and continuous variables. The second objective of this study was to develop a mathematical function that can produce a measure to evaluate the direct association between exposure and possible outcome variables. This function was referred to as the function of Magnitude-of-Effect (MoE). Safety experts and practitioners can use the MoE function to interpret how strongly an exposure variable can affect the possible outcome variable. The significance of such achievement is that it can eliminate the artificial neural network models' shortcoming and make them more applicable in the occupational safety and health engineering field. The result of this study showed that artificial neural network models performed significantly better than logistic regression models with a data set of all ordinal variables. And also the suggested MoE function was capable and valid enough to show any correlation between exposure and possible outcome variables.

Committee: Richard Shell PhD (Committee Chair); Ash Genaidy PhD (Committee Member); Anca Ralescu PhD (Committee Member); Gary Weckman PhD (Committee Member); John Funk PhD (Committee Member) Subjects: Environmental Science; Health; Industrial Engineering; Occupational Safety

MS, University of Cincinnati, 2007, Engineering : Industrial Engineering

Background. The Work Compatibility Framework work is a comprehensive approach to improve human performance at work, embedding previous models including motivation-hygiene theory, job characteristics theory, balance theory, person-environment fit, and demand-control. It also encourages the study of the positive and the negative aspects of work for the ultimate improvement of work performance. Objectives. The study objectives were: (a) to examine the positive and negative characteristics of work in the machining department in a small manufacturing plant in the Midwest USA, and, (b) to report the prevalence of musculoskeletal and stress outcomes. Methods. A focus group consisting of worker experts from the different job categories in the machining department confirmed the management's concerns. Accordingly, fifty-four male and female workers, employed in three shifts, were surveyed on the demand/energizer profiles of work characteristics and self-reported musculoskeletal/ stress symptoms. Results. Workers recognized the risk and protective characteristics of work and reported varied level of prevalence of musculoskeletal and stress symptoms. The prevalence of musculoskeletal and stress disorders increased with a decrease in work compatibility. Conclusions. The results of this case study confirm the importance of adopting a comprehensive view for work improvement and sustainable growth opportunities. It is paramount to consider the negative and positive aspects of work characteristics to ensure optimum organizational performance. Significance. The Work Compatibility Improvement Framework, proposed in the reported research, is an important endeavor toward the ultimate improvement and sustainable growth of human and organizational performance.

Committee: Dr. Ash Genaidy (Advisor) Subjects: Engineering, Industrial

MS, University of Cincinnati, 2004, Engineering : Industrial Engineering

Objective: This study proposes a decision-support framework to track the state of the human-at-work system on an operational basis. The work compatibility model provides a multi-dimensional diagnostic of the human-at-work system. By integrating the work compatibility model into a visual framework, management can make informative decisions that contribute to the improvement of human performance Background: Examples of multidimensional models can be found in performance measuring systems and human performance measures. Performance measuring systems, either hierarchical or balanced models, are focused on the reduction of operational variables to financial performance. Human performance measures rely on respondents to weigh different factors and define the best rules for aggregation. In the work compatibility model, respondents rate work-factors as energizers and demands on the system. An expert model uses a matrix function to quantify the degree of compatibility. Based on the matrix output, operating zones determine how far the system is from the optimum performance. Conceptual model: The matrix function of the expert solution is transformed into a plane with two regions set along the compatibility line. Work factors are aggregated into two vectors, one on each region of the plane, and the resultant of the two vectors provides two indexes: the yield ratio, a relative measure of the magnitude of vector, and the efficiency ratio, a relative measure of the angle between the two vectors. A Visual Basic application is developed to implement the proposed quantitative methods. The continuous model is tested with data from a manufacturing company to analyze the behavior of the response function. Case study: The yield ratio follows a normal distribution, and the efficiency ratio follows a chi-square distribution. An analysis of variance shows that the yield ratio provides a performance levels similar to those of the expert solution. The decision process starts by setting target (open full item for complete abstract)

Committee: Dr. Ash Genaidy (Advisor) Subjects: Engineering, Industrial