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

Abstract

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.

Keywords:

• vendor-managed inventory (VMI)
• back-propagation (BP) neural network
• distributed lag
• customer order behavior
• semiconductor manufacturing

Acknowledgments

The authors wish to thank the Editors and two anonymous referees for their insightful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by Ministry of Science and Technology, Taiwan [grant number NSC100-2628-E-007-017-MY3]; [grant MOST 103-2221-E-155-029-MY2]; [grant MOST 104-2622-E-007-002].