Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.