Contemporary manufacturing landscape has changed and has become highly volatile and demanding. Product Life Cycle has significantly decreased because of the consumers’ need for personalized products in shorter period of time. This need forced the manufacturers to produce smaller batches of highly diversified products instead of producing huge batches of the same product. These newly imposed production requirements made the manufacturers struggle to optimize their productions in such short period of time and therefore, these production requirements created the need for more efficient, productive and eco-friendly planning and scheduling. The solution to this problem can be given partially by a concept named “Zero Defect Manufacturing” (ZDM). The goal of ZDM is to eliminate defected parts and therefore achieving higher efficiency, eco-friendliness and lower production costs. There are four ZDM strategies that are interconnected with each other: detection, repair, prediction and prevention. The current research work focuses on the Beginning of Life of the product and considers the development of a dynamic Scheduling tool combined with an intelligent Decision Support System (DSS) that takes into consideration the ZDM strategies for eliminating defected parts during production. The elimination of defected parts would be done mainly by creating specific algorithms for predicting when a defect may occur and for taking the right decisions in order to prevent the defect occurrence. Finally, an industrial example is presented in order to illustrate the potential benefits of such an approach.