A Meta-Model for Full-Load Production Function of Dynamic Machine Assignment

A supply chain is consisted of multiple nodes, so supply chain management will require two modeling tools: production behavior for each node and connecting method between nodes. In economics, a production function relates throughput to capital and labor. Similarly, a production function in supply chain management describes the relationship between performance measures and production decisions in a factory. There is a need to construct production functions for complex production units before designing a system for supply chain management.

If a node in supply chain encounters significant production variations during a full-load situation, its performance will become unpredictable. Remedial measures must be activated to bring the system back to steady states. Semiconductor foundry is a very complicated production system due to its large scale and the uncertainties in process yield, machines, product demand and product mixes. When dynamic events take place in a full-load situation, new bottlenecks are created and they must be mitigated by using dynamic machine assignment or other means. In this thesis, a semiconductor plant is treated as a production unit and the dynamic machine assignment is used to construct a full-load production function. Because there are lots of uncertainties in wafer fabrication and production performances are dependent on production scenarios, the meta-modeling is taken as an approach in this research work to construct the production function. The production function described in this thesis can be used as a tool to manage dynamic events for a factory in semiconductor supply chain.

Keywords: Semiconductor manufacturing, Full-load production function, Meta-model, Dynamic machine assignment.

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