近來扣件業的發展提高興趣於資料探勘與短期預測的關係。先前的研究已經指出台灣扣件業的庫存管理問題，然而卻因資料過少，使其預測準確度與可靠度成為尚待解決的問題。灰預測模型，藉由累加生成方式，提供一個求解方向，而其中影響該模式預測準確度最重要的因素為背景值參數 值。本研究旨於從初期導入少樣數據改善灰色預測模型的準確性與可靠性，因此本研究提出一個基於蟻群演算法(ant colony system, ACS)的改良灰預測模型，找出各期 求出最佳解，亦從加州大學的知識發現資料庫所取得之公開測試資料進行效果驗證並與其他多種改良式灰預測模式進行比較。實驗結果顯示，本研究所提出的ACSGM (1, 1)在預測準確度比較上確實較其他灰預測模式為佳，並能有效增進傳統灰預測模式之預測效能。本研究發現祈能提供個案公司與扣件業高層主管在需求預測的管理決策中作出策略性規劃之參考。

Recent developments in the fastener industry has heightened interest in the relationship between data mining and short-term sales forecasting. Previous studies have reported inventory management problems in Taiwan’s fastener industry. However, important questions remain to be resolved including how to improve the accuracy and reliability of sales forecasting when dealing with short-term limited data. The grey model is proposed to overcome this problem through accumulated generating operations (AGO). The background values represented as coefficient  play a critical role that determines and affects the precision of the GM model. The purpose of this research is to improve the accuracy and reliability of the grey prediction model GM (1, 1) with limited data in the earlier part of the prediction process. Therefore, this study proposes a modified GM (1, 1) based ant colony system (ACS) to obtain the optimal  values. Meanwhile, two kinds of data are employed: the Synthetic Control Chart Time Series dataset (SCCTS) from the Knowledge Discovery Database (KDD) and the fastener industry statistics from Taiwan Customs Import and Export data in order to verify the effectiveness of the ACSGM (1, 1) model. It is compared with two other models, GM (1, 1) and AGM (1, 1). The results reveal that ACSGM (1, 1) significantly improves accuracy. The findings may serve as a guide in strategic planning for support management decisions related to forecasting demand for case companies and for the fastener industry.

In Chinese
馮世達（2007），台灣螺絲螺帽產業競爭力分析，碩士論文，國立成功大學資源管理研究所。
In English
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