偵測識別批量生產系統中的低良率機器對於最大限度地減少缺陷輸出至關重要。 然而，解決這個複雜問題的最新方法需要大量的專業知識、昂貴的資源或兩者的結合。 為了解決這個挑戰，我們提出了一種使用「Maximum Likelihood Estimation」和「Bootstrap Confidence Intervals」的簡單且經濟高效成本效益高的方法。此方法可以有效估計每台機器的良率，從而能夠精確定位低良率機器並產生優先順序清單以進行進一步調查。 擁有 50-500 台機器的製造商可以透過建立包含約 6-20 倍生產機器批次的資料集來實施此方法。 當滿足此條件時，系統可有效偵測識別最多 5 台低良率機器。 此外，估計的每台機器良率可用於預測各個生產批次的良率，為生產計劃和優化提供有價值的見解。;Identifying low-yield machines in batch production systems is crucial to minimize defective outputs. However, recent methods of addressing this complex issue require considerable expertise, expensive resources, or a combination of both. To solve this challenge, we propose a straightforward and cost-effective method using maximum likelihood estimation and bootstrap confidence intervals. This method efficiently estimates per-machine yield, enabling the pinpointing of low-yield machines and the generation of a prioritized list for further investigation. Manufacturers with 50–500 machines can implement this method by building a dataset containing approximately 6-20 times as many batches as production machines. When this condition is met, the system effectively identifies up to 5 low-yield machines. Additionally, the estimated per-machine yield can be used to predict the yield of individual production batches, providing valuable insights for production planning and optimization.
