Capacity dynamics of feed-forward, flow-matching networks exposed to random disruptions

While lean manufacturing has greatly improved the efficiency of production operations,it has left US enterprises in an increasingly risky environment. Causes ofmanufacturing disruptions continue to multiply, and today, seemingly minor disruptionscan cause cascading sequences of capacity losses. Historically, enterprises havelacked viable tools for addressing operational volatility. As a result, each year UScompanies forfeit billions of dollars to unpredictable capacity disruptions and insurancepremiums. In this dissertation we develop a number of stochastic models thatcapture the dynamics of capacity disruptions in complex multi-tier flow-matchingfeed-forward networks (FFN). In particular, we relax basic structural assumptionsof FFN, introduce random propagation times, study the impact of inventory bufferson propagation times, and make initial efforts to model random network topology.These stochastic models are central to future methodologies supporting strategic riskmanagement and enterprise network design.