This paper presents a novel method to quantify the effects of human-related factors on the risk of failure in manufacturing industries. When failures can be caused by operators, the decision maker must intervene to mitigate operator-related risk. There are numerous intervention methods possible; we develop a revenue model that provides the decision-maker with a systematic tool to perform a cost-benefit analysis, balancing the advantage of risk reduction, against the direct cost of the intervention method. A method is developed to incorporate human-related factors, in addition to machine-related factors, in machine failure analysis. This enables the revenue model to use the expected uptime and the probability of failure, given the operator skill level and working conditions, to calculate the expected revenue associated with each intervention method. A case study of a manufacturing company is considered incorporating two possible intervention methods: reducing the production rate to provide more cognition time and adding a shift expert to guide the operators. Different courses of action are chosen for the various skill-shift scenarios presented. 
Choosing the optimal intervention method to reduce human-related machine failures