A back-propagation neural network with a distributed lag model for semiconductor vendor-managed inventory

Variations in customer demands directly affect the total quantity of customer orders processed in semiconductor fabrication. This causes variability in wafer start input, affecting the work-in-process bubble and cycle time. The finished goods stored in fab warehouse are called semiconductor vendor-managed inventory. The increase of inventory leads to low inventory turnover ratio. In this study, a back-propagation neural network model with a distributed lag structure is developed to predict customer demands based on customer order behaviors, and to extract useful information for supporting the production plan for VMI based on uncertain demand and market fluctuation. An empirical study was conducted at a leading fab to validate the method. The results have shown the practical viability of the proposed approach. The derived empirical rules can assist decision-makers in making timely production decisions, given various order situations, ensuring that adequate fab utilization and cycle times are maintained.

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