All individuals are unique in their characteristics. As such, their positive and negative contributions to system performance differ. In any system that is not fully automated, the effect of the human participants has to be considered when one is interested in the performance optimization of the system. Humans are intelligent, adaptive, and learn over time. At the same time, humans are error-prone. Therefore, in situations where human and hardware have to interact and complement each other, the system faces advantages and disadvantages from the role the humans play. It is this role and its effect on performance that is the focus of this dissertation. When analyzing the role of people, one can focus on providing resources to enable the human participants to produce more. Alternatively, one can strive to ensure the occurrence of less frequent and impactful errors. The focus of the analysis in this dissertation is the latter. Our analysis can be categorized into two parts. In the first part of our analysis, we consider a short term planning horizon and focus directly on failure risk analysis. What can be done about the risk stemming from the human participant? Any proactive steps that can be taken will have the advantage of reducing risk, but will also have a cost associated with it. We develop a cost-benefit analysis to enable a decision-maker to choose the optimal course of action for revenue maximization. We proceed to use this model to calculate the minimum acceptable level of risk, and the associated skill level, to ensure system profitability. The models developed are applied to a case study that comes from a manufacturing company in Ontario, Canada. In the second part of our analysis, we consider a longer planning horizon and are focused on output maximization. Human learning, and its effect on output, is considered. In the first model we develop, we use learning curves and production forecasting models to optimally assign operators, in order to maximize system output. In the second model we develop, we perform a failure risk analysis in combination with learning curves, to forecast the total production of operators. Similar to the first part of our analysis, we apply the output maximization models to the aforementioned case study to better demonstrate the concepts.

All individuals are unique in their characteristics. As such, their positive and negative contributions to system performance differ. In any system that is not fully automated, the effect of the human participants has to be considered when one is interested in the performance optimization of the system. Humans are intelligent, adaptive, and learn over time. At the same time, humans are error-prone. Therefore, in situations where human and hardware have to interact and complement each other, the system faces advantages and disadvantages from the role the humans play. It is this role and its effect on performance that is the focus of this dissertation. When analyzing the role of people, one can focus on providing resources to enable the human participants to produce more. Alternatively, one can strive to ensure the occurrence of less frequent and impactful errors. The focus of the analysis in this dissertation is the latter. Our analysis can be categorized into two parts. In the first part of our analysis, we consider a short term planning horizon and focus directly on failure risk analysis. What can be done about the risk stemming from the human participant? Any proactive steps that can be taken will have the advantage of reducing risk, but will also have a cost associated with it. We develop a cost-benefit analysis to enable a decision-maker to choose the optimal course of action for revenue maximization. We proceed to use this model to calculate the minimum acceptable level of risk, and the associated skill level, to ensure system profitability. The models developed are applied to a case study that comes from a manufacturing company in Ontario, Canada. In the second part of our analysis, we consider a longer planning horizon and are focused on output maximization. Human learning, and its effect on output, is considered. In the first model we develop, we use learning curves and production forecasting models to optimally assign operators, in order to maximize system output. In the second model we develop, we perform a failure risk analysis in combination with learning curves, to forecast the total production of operators. Similar to the first part of our analysis, we apply the output maximization models to the aforementioned case study to better demonstrate the concepts.