| Summary: | While lean manufacturing has greatly improved the efficiency of production operations,
it has left US enterprises in an increasingly risky environment. Causes of
manufacturing disruptions continue to multiply, and today, seemingly minor disruptions
can cause cascading sequences of capacity losses. Historically, enterprises have
lacked viable tools for addressing operational volatility. As a result, each year US
companies forfeit billions of dollars to unpredictable capacity disruptions and insurance
premiums. In this dissertation we develop a number of stochastic models that
capture the dynamics of capacity disruptions in complex multi-tier flow-matching
feed-forward networks (FFN). In particular, we relax basic structural assumptions
of FFN, introduce random propagation times, study the impact of inventory buffers
on propagation times, and make initial efforts to model random network topology.
These stochastic models are central to future methodologies supporting strategic risk
management and enterprise network design.
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