Sustainable project choice : integrating Must-Be kano and fuzzy logic for optimal project selection

In today's competitive global markets, organizations increasingly recognize the necessity of prioritizing projects that improve financial performance and address resource conservation, environmental protection, and community well-being. This study introduces a novel approach to enhance the Sustainable Project Selection (SPS) process, which is crucial for meeting these multidimensional demands. We present a two-stage methodology that combines the Fuzzy Kano model with a modified Fuzzy Inference System (FIS) for a comprehensive evaluation of projects across the triple bottom line: economic, environmental, and social. The first stage utilizes the Fuzzy Kano model to systematically cluster project requirements into categories, ensuring a deep understanding of the needs of customers and stakeholders. The second stage uses a modified FIS, rigorously evaluating projects against these needs. This integration improves project assessment, aligns with sustainability standards, and enhances decision-making. Applying this methodology in a case study in the automotive industry demonstrates its effectiveness in the SPS process. The results indicate improved accuracy and reliability in selecting projects, ensuring alignment with the triple-bottom-line concept of sustainability. This study contributes a unique perspective to sustainable engineering management, presenting a structured and analytical framework to help organizations make informed and strategically sustainable project selections, thus balancing financial performance with social and environmental responsibilities.