The competition in the semiconductor industry is becoming more and more fierce, which significantly distorts the learning process of semiconductor yield improvement. For example, if the yield of a product could not reach a certain level before a given deadline, then the competitiveness of the product will disappear and capacity will be re-allocated to other products. To prevent that from happening, some managerial actions, e.g. executing a quality engineering project, quickening the speed of mass production, etc. can be taken to accelerate yield learning. After such actions, the yield learning model has to be modified. In this study, how to incorporate the effects of such managerial actions on semiconductor yield learning is investigated. Subsequently, a new fuzzy yield learning model is developed. The proposed methodology has been applied to the data of four semiconductor products. Experimental results revealed the effectiveness of the proposed methodology.