## 1 Introduction  
The current wave of digitization has profoundly transformed the world, significantly impacting product design. Understanding customer needs and addressing their requirements are crucial factors influencing market success. Additionally, quality requirements and cost-efficient management remain critical for products to be competitive and sustainable [1; 2; 3]. These factors continue to be vital considerations [4; 5; 6; 7]. Recent trends show increasing product requirements and rapidly evolving markets demanding highly precise product developments in shorter cycles. Production dependencies and boundary conditions must be considered early in the product design phase to ensure functionality [8; 9]. This heightened complexity necessitates precise designs with numerous variables, making design decisions more challenging and extending development times [8; 9; 10; 11]. Advancements in information technologies offer significant opportunities to support the product engineering process through increased computing power, new simulation and analysis tools, and interconnected data [10]. Digital twins of products or production processes are now being modeled in various domains to derive optimal solutions [8]. Enhanced data availability and traceability facilitate the creation of data-driven models using artificial intelligence (AI), big data analytics, and machine learning (ML) methods [10; 12; 13]. These advancements enable the development of algorithms that support or even substitute human decision-making [14; 15]. Data mining techniques uncover patterns in big data, enabling the discovery of unexpected knowledge without prior theorization and domain expertise [16; 17]. Despite these benefits, incorporating digital technologies into product design presents notable challenges. Automation can erode tacit knowledge traditionally held by designers, complicating the effective use of human expertise [8; 10; 11; 18; 19]. Effective implementation requires collaboration among product designers, production planners, and application engineers, each with unique boundary conditions and optimization strategies that can unpredictably affect subsequent phases [8]. Clearly defining roles and scopes of action is crucial for achieving optimal solutions. While supplementary reviews from online sources provide valuable insights, their impact on prioritizing customer requirements has not been extensively examined [20]. In group decision-making, opinion inconsistencies often arise, making consensus essential [20]. Addressing these issues requires advanced technological tools and effective communication and collaboration among stakeholders. Traditional methods, such as multi-criteria decision-making (MCDM) techniques like quality function deployment (QFD) and analytic hierarchy process (AHP), continue to be studied and developed to assist in product design. These methods will be discussed further in the literature review section. As shown in Table 1, MCDM methods such as AHP and QFD are widely used in combination for decision-making in product design. Summarizing the key strengths and limitations of MCDM combined with QFD, such as AHP, analytical network process (ANP), and fuzzy logic [21; 22], it is evident that no single method can address all key challenges comprehensively.