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



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Texas A&M University


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.