پیش بینی موجودی تنزل بها یافته برای تولید نیمه رسانا با توجه به سن موجودی، اصول حسابداری، و ساختار محصول با تنظیمات واقعی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20844||2013||9 صفحه PDF||سفارش دهید||6790 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers & Industrial Engineering, Volume 65, Issue 1, May 2013, Pages 128–136
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 18 months–3 years. However, the average cycle time for producing flash memory is approximately 3 months. 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 10 s, 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.
The semiconductor industry is continuously growing with extensive applications in medical electronics, green energy, car electronics, computers, communication, and consumer electronics (MG + 4C). The Semiconductor Industry Association (SIA) reports a double-digit growth of all major semiconductor product categories in 2010 compared to 2009. The global semiconductor chip sales reached a record US$298.3 billion with a nearly 32% annual increase (SIA, 2011). However, numerous semiconductor memory companies have been exposed to the risk of excess inventory levels and frequent inventory write-downs (Chen, Ramnath, Rangan, & Rock, 2010). The reason can be ascribed to industry characteristics, including high capital intensity, rapid technology development, and severe supply chain competition (Aizcorbe, 2002, Chien, 2007, Chien et al., 2011 and Leachman et al., 2007). Specifically, high capital expenditure prompts semiconductor manufacturers to fully use capacity, which leads to high levels of inventory accumulation during low-demand periods. Following Moore’s law, the new generation product will dominate prior generations regarding the cost-per-function. This technology migration will accelerate the price decline and replacement of prior generation products, rendering the existing inventory obsolete. Additionally, the increasingly fierce competition has commodified chip sales. The continuous and significant price decline leads to a market value lower than the manufacturing costs, which is another cause of inventory write-downs. On January 1, 2005, the International Financial Reporting Standards (IFRS) Foundation Trustees declared the IFRS No. 2 (2010) the accounting principle for the reduction of inventory to market allowance. However, since its implementation, integrated device manufacturers have experienced even greater difficulty maintaining robust records for financial statements. Inventory write-downs are recorded as part of cost of goods sold that led to disadvantageous gross profit. Semiconductor manufacturing is complex and lengthy. For example, the average cycle time of flash memory is approximately 3 months, including the 50 days for wafer fabrication, 5 days for circuit probing, 7 days for chip assembly, and 22 days for final tests. The average cycle time of semiconductor manufacturing varies across products because of a number of manufacturing strategic decision settings, including demand planning (Chien, Chen, & Peng, 2010a), new product ramping schedule and allocation (Chien, Wu, & Wu, 2011), capacity planning (Chien & Zheng, 2011), wafer start plan, work-in-process (WIP) level, tool availability (Kuo, Chien, & Chen, 2011), outsourcing strategy and order allocations (Chien et al., 2010b and Wu and Chien, 2008a), and scheduling (Wu & Chien, 2008b). In addition, three months to 1 year of safety stocks are typically established depending on the product market and customer satisfaction level. In these cases, most of the WIP and end products become the amortization item to be written down. Additionally, variations in financial performance may further cause overreactions in the stock market when a significant amount of reduction of inventory to market is reported at once. This overreaction is typically nonreversible. That is, no compensation will be provided even after written-down inventories are sold thereafter. The timing and magnitude of inventory write-downs are crucial to earning management (Chen et al., 2010). However, knowing that prior research addressed inventory write-down estimation primarily based on economic factors and temporal history data, this study develops a multi-period inventory write-down prediction model that captures specific company features, including accounting principles, inventory ages, and product structures. An empirical study was conducted on a semiconductor manufacturer located in the Hsinchu Science Park in Taiwan to demonstrate the viability of the proposed model. The rest of this paper is organized as follows. Section 2 addresses the literature review of inventory models related to inventory write-downs; Section 3 elaborates features of inventory write-downs in semiconductor manufacturing and introduces the proposed model; Section 4 explains the proposed model; Section 5 presents the data collection and analytical results based on an empirical study; Section 6 addresses discussions on sensitivity analysis and applications of cost tornado diagrams; and lastly, Section 7 offers a conclusion.
نتیجه گیری انگلیسی
Because predicting inventory write-downs in the semiconductor manufacturing industry is vital but complex, a systematic model that captures critical features, including the accounting principles, inventory ages, and product structures must be developed. To provide an efficient and accurate solution, this study developed an integrated polynomial-time heuristic-based model. The empirical study shows the advantages of using the proposed model, including rapid computation less than 10 s and a MAPE less than 3.5% for a complete production line 3-month prediction. Using this efficient and effective model, management can perform comprehensive what-if analysis regarding various improvement alternatives and supply chain strategies, such as the decisions on push/pull boundaries. Discussions regarding the sensitivity analysis and cost tornado diagrams suggest the priority of affecting factors. To prevent unexpected inventory write-downs, management should focus on implementing the FIFO rules, reducing manufacturing costs, and monitoring market demand changes. Additionally, management should be aware that the delayed effects of the current inadequate strategy will significantly increase write-downs in the future. Though the proposed model assumes linearity and deterministic parameters and variables, more general models can be developed by introducing simulation and evolutionary algorithms. This extension will enable the evaluation of accounting rule changes and supply chain decision effects.