  Technological advances allow manufacturers to collect and access data from a
production system effectively. The objective of data collection is to deploy
the collected data in developing decision support systems for performance
evaluation, problem identification, and production control. The goal of this
dissertation is to investigate how the collected data can be used to evaluate
performance and optimize manufacturing systems, analytically and empirically.
In the first part of the thesis, I investigate how can the collected data from
the shop-floor be used in the efficient control and design of manufacturing
systems? To investigate the impact of possible dependency in the inter-event
times on the optimal control and performance measures of the system, a
manufacturing system that is controlled by using a single-threshold production
control policy is analyzed. It is shown that ignoring autocorrelation in
inter-arrival or service times leads to overestimation (underestimation) of the
optimal threshold level for negatively (positively) correlated processes. Then,
it is shown that the optimal control policy of a production/inventory system
with correlated inter-arrival and service times modeled as Markovian Arrival
Processes is a state-dependent threshold policy. In the second part of the
thesis, an exploratory data analysis is conducted by using a large industrial
dataset related to the flow of the products at a semiconductor wafer
fabrication. Then, the cycle times of the products are predicted using Machine
Learning methods, and the performance of different prediction algorithms is
assessed. The analytical and empirical results presented in this dissertation
show that effective use of collected data from a manufacturing system enables
controlling the system effectively and predicting its main performance measures
accurately.
