Selection of the correct projects at the correct time is critical to the success of a Six Sigma program. Poor project selection can cause a host of problems for an organization such as resource waste, low stakeholder engagement, unsustainable results, and lost credibility. During the selection of a lean six sigma project, a mix of qualitative and quantitative data with high levels of uncertainty are utilized to study the effectiveness of projects and choose the best option that aligns with organization strategy and maximizes the impact and resource savings in the company. In this process, diverse range of conflicting and interdependent criteria are used to select the most appropriate options that match best with requirements and needs of the organization involved. This paper explores the possibilities of using analytic network process (ANP) and utilizing the collected data for selecting the most appropriate six sigma      project to reduce warranty costs. The proposed framework enables automated decision-making process, quantify subjective opinions of stakeholders, and enable efficient modeling of feedback loops in the system. It also shows promising results to decrease decision biases, increase system adaptability, and enhance transparency of the process.