Nowadays manufacturing environment is highly volatile and rapidly changing as the need of products is increasing exponentially, due to the fact that Product Life Cycle is shortened and therefore more products are required to be manufactured or remanufactured. On top of that, these production requirements have "created" the need for more efficient, productive and eco-friendly planning and scheduling. For that purpose manufactures have started adopting the Zero Defect Manufacturing (ZDM) paradigm which has a goal of eliminating 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 focus on 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 purpose of creating this type of tool is twofold: a) provide to the manufacturers a scheduling tool that operates based on the ZDM objectives and b) use the tool for creating a map based on product manufacturing properties for choosing the right and most efficient ZDM strategies at the right production stage. Finally, in order for the tool to implement the ZDM concept, it will include algorithms that can predict when a defect will occur in order to prevent it.