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Technological advances allow manufacturers to collect and access data from aproduction system effectively. The objective of data collection is to deploythe collected data in developing decision support systems for performanceevaluation, problem identification, and production control. The goal of thisdissertation is to investigate how the collected data can be used to evaluateperformance and optimize manufacturing systems, analytically and empirically.In the first part of the thesis, I investigate how can the collected data fromthe shop-floor be used in the efficient control and design of manufacturingsystems? To investigate the impact of possible dependency in the inter-eventtimes on the optimal control and performance measures of the system, amanufacturing system that is controlled by using a single-threshold productioncontrol policy is analyzed. It is shown that ignoring autocorrelation ininter-arrival or service times leads to overestimation (underestimation) of theoptimal threshold level for negatively (positively) correlated processes. Then,it is shown that the optimal control policy of a production/inventory systemwith correlated inter-arrival and service times modeled as Markovian ArrivalProcesses is a state-dependent threshold policy. In the second part of thethesis, an exploratory data analysis is conducted by using a large industrialdataset related to the flow of the products at a semiconductor waferfabrication. Then, the cycle times of the products are predicted using MachineLearning methods, and the performance of different prediction algorithms isassessed. The analytical and empirical results presented in this dissertationshow that effective use of collected data from a manufacturing system enablescontrolling the system effectively and predicting its main performance measuresaccurately.

Modelling and Control of Production Systems based on Observed Inter-event Times: An Analytical and Empirical Investigation (Ph.D. Thesis)