A Hybrid Entropy-MARCOS Decision Support Framework for Optimising Sustainable Innovation in Complex Product Development Systems

Managing sustainable innovation within complex New Product Development (NPD) processes presents a significant challenge, requiring robust decision-making tools. This study proposes and evaluates a computational decision support framework designed to optimise strategic choices for sustainable product design, thereby helping to achieve overarching sustainability objectives. The framework employs a hybrid method, combining the entropy technique for weighting objective criteria and the MARCOS approach for systematic ranking, effectively addressing the complexities inherent in managing sustainable innovation. The entropy method minimises subjective bias when evaluating important sustainability criteria, while MARCOS offers a structured way of selecting the best design options within complex NPD. An empirical study conducted within the ceramic industry highlights the importance of factors such as high-quality design and carbon neutrality, providing practical insights for engineering and technology managers. The findings demonstrate the framework’s utility as a versatile tool for the early stages of sustainable product development, enabling informed strategic decisions and supporting the effective implementation of sustainability practices in complex systems. This research contributes to the theory and practice of computational decision support for technology management and sustainable innovation, particularly in manufacturing and other data-intensive contexts.