在競爭激烈的市場下，企業為求科技創新與經濟發展，實施產業轉型(Industrial transformation)策略，改變其產業結構與規模，提升產業競爭力與滿足顧客需求。在產品開發過程中，受限時間與成本壓力下，通常在未取得足夠數量的樣本資料量進行成本預測。如何在市場競爭與品質提升的環境下，從有限資料建構預測模型進行成本預測為企業管理重要的標的。樣本資料量不足的問題存在實務應用，如何從有少數樣本中，開發有意義的資訊是小樣本學習的研究議題。許多研究發現，小樣本研究為解決製造系統預測問題與克服資料量不足的學習效能，以虛擬樣本產生法增加樣本量，透過小樣本數據集增加樣本資料量，提升預測學習的準確性。本研究結合兩參數韋伯分佈與最大P值(Maximal P-value)決定分佈參數，生成虛擬樣本增加資料量，藉由Kolmogorov-Smirnov檢定過濾與篩選干擾因子，將訓練樣本資料匯入到預測學習工具內，比較在不同方法下預測的準確程度。本研究以台灣印刷電路板汽車板產品之小樣本資料為實驗案例，實驗結果顯示，本研究方法較原始資料之預測誤差有顯著改善，與最大P值相比有較佳的預測效果。

After the transition of consumer demand in the market, in order to create the technology and develop the finance, corporations adopt industrial transformation strategy to make an alteration of the corporation structure and scale and improve the competition and satisfy customer needs. Under stress of time and cost, the cost prediction in the product development process is often being conducted without acquiring sufficient sample data. The method of constructing prediction model to perform cost prediction analysis from limited data in the competitive and qualitative environment has become an important goal for corporation management. For the issue of insufficient sample data in the practice and application, the matter of how to develop the meaningful information from few samples is the research topic of small data set learning. Many studies noted that in order to address the predict issues of production system and break through the learning efficiency when data is inadequate, small data research increase the sample amount by virtual sample generation method to increase the sample data through small data sets and enhance the accuracy of prediction learning. The study adopted methods of two parameters Weibull distribution and Maximal P-value approach to generate virtual sample to increase data. The confounding factors were filtered and screened by Kolmogorov–Smirnov test and imported the training sample data into the prediction learning instruction to compare the accuracy of prediction in different methods. The study adopted small data sets of automotive printed circuit board in Taiwan as experiment case. The result indicated the method conducted in the study had a significant improvement on the prediction error than one in original data, and it received better effect of prediction comparing to maximum p-value.

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