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Inventory write-down prediction for semiconductor manufacturing considering inventory age, accounting principle, and product structure with real settings

Authors :
Jei-Zheng Wu
Source :
Computers & Industrial Engineering. 65:128-136
Publication Year :
2013
Publisher :
Elsevier BV, 2013.

Abstract

The International Financial Reporting Standards (IFRS) No. 2 has been the worldwide accounting principle for the reduction of inventory to market allowance since January 1, 2005. Using make-to-stock manufacturing strategies and inventory accounting for only approximately 14% of the total costs, integrated device manufacturers have found maintaining robust records for financial statements increasingly difficult. For example, one company in the case study conducted in this study must write-down losses of 2-100% of the total inventory costs for products with inventory ages of 18months-3years. However, the average cycle time for producing flash memory is approximately 3months. In other words, when the system variation and safety stock policy are considered, the company must write-down the reduction of inventory to market allowance for most of work-in-process inventory. However, little research has been done to addressing the practical management of operations according to inventory aging processes. This study develops a polynomial-time-based model to obtain significant features, including inventory ages, accounting principles, and product structures (bill of material), for the accurate prediction of inventory write-downs to reduce the impact of the carrying value fluctuation of inventory. An empirical study was conducted on a Taiwanese semiconductor manufacturer. The results show that predicting 3-month inventory write-downs of a complete flash memory production line comprising approximately 8500 product types can be conducted in less than 10s, with the mean absolute percentage error (MAPE) less than 3.5%. Discussions regarding the sensitivity analysis and cost tornado diagrams suggest the priority of affecting factors. The results show the viability of implementing the proposed model to predict inventory write-downs in the semiconductor manufacturing industry.

Details

ISSN :
03608352
Volume :
65
Database :
OpenAIRE
Journal :
Computers & Industrial Engineering
Accession number :
edsair.doi...........5b49b0d2b03cfc7a4860cd73c864d18f
Full Text :
https://doi.org/10.1016/j.cie.2011.11.020