Estimating job cycle time is an important task for a semiconductor manufacturer as it helps to strengthen relationships with customers and is also conducive to the sustainable development of the manufacturer. The research trend in this field has moved toward the development of hybrid methods, especially those that are classification-based. Most existing methods use pre-classification; however, such methods have several drawbacks, such as incompatibility with the estimation method and unequal sizes of different job groups. In contrast, a post-classification approach has great potential, and therefore is used as a basis for the new approach in this study. In the proposed methodology, a systematic procedure is established to divide jobs into several groups according to their estimation errors. In this way, the classification and estimation stages can be combined seamlessly because they optimize the same objectives. A real case is used to evaluate the effectiveness of the proposed methodology and the experimental results support its superiority over several existing methods. The shortcomings of the existing methods based on pre-classification are also clearly illustrated. 
Estimating job cycle time in a wafer fabrication factory: A novel and effective approach based on post-classification