This thesis examines the problem of identifying patterns in process event logs that are correlated with binary events that are undetected until the end of the process. Specifically, we consider the task of identifying patterns in a machine shop manufacturing process that are correlated with product defect. We introduce a pattern mining algorithm based on Apriori to identify frequent patterns, and use binary correlation measures to identify patterns associated with elevated defect rate. We design a simulation model to generate synthetic datasets to test our algorithm. We compare the effectiveness of different correlation measures, target pattern complexities, and sample sizes with and without knowledge of the underlying process. We show that knowledge of the underlying process helps in identifying the pattern that is associated with defects. We also develop a decision support tool based on p-value simulation to help managers identify sources of error in real-life settings. Finally, we apply our method to real world data and extract useful information from the data to help plant managers make decisions related to investments and workforce planning. This thesis also explores the problem of predicting the defect probability given an ordered list of events and its defect status. We develop a supervised learning model using the frequency of patterns deduced from the event log as the feature set. We discuss the challenges faced in this approach and conclude that random forest algorithm performs better than other methods. We apply this approach to a real world case study and discuss the applications in the machine shop. Finally, the thesis explores the order-bidding process in the machine shop industry, and proposes an optimization-based model to maximize the profit of the machine shop. Through a case study example, we show the advantages of using the defect probability in the proposed optimization model to determine the machine-worker schedule to execute job orders in a machine shop.