Modeling demand behavior in manufacturing supply chains

A fundamental element in the supply chain management problem is the behavior of demand, and in particular, the propagation of demand through the tiers of manufacturing facilities in the chain. Understanding this propagation phenomenon is essential to improving manufacturing performance in a supply network. In this research, two distinctly different modeling frameworks are developed to provide insight to demand behavior in manufacturing supply chains, and to study policies that provide opportunity for reducing operating costs and improving delivery performance.;The first of these describes the production-based decision process that drives the translation of demand (i.e. orders), and so drives supply chain operations. This modeling framework provides a tool for analyzing manufacturing supply chains that are driven by structured order processing and production planning systems. This part of the research focuses on providing insight to the planning and operation of manufacturing supply chains confronted with complex interactions and dependencies, as is typically observed in the automotive industry. Specifically, we find conditions under which order batching, multiple schedule releases, product structure and component sharing, and capacity levels in manufacturing facilities influence demand amplification and therefore manufacturing performance.;We then examine demand behaviors in technology-driven markets where volatile market demand exists without an integrated order processing and planning system, as in the microelectronics industry. Here, we find that the demand characteristics of the larger set of scenarios can be captured using only a small fraction of the actual demand scenarios. This makes computationally-intensive stochastic programming models a viable tool in this decision analysis arena.