Navigating the new normal: Redefining N95 respirator design with an integrated text mining and quality function deployment-based optimization model

•Proposing a sequential decision-making model for mask design.•Text mining algorithms are used to extract critical features from online consumer reviews.•QFD is employed to evaluate manufacturing and consumer requirements.•Optimal N95 design is identified while considering technical and consumer needs.•The findings may assist organizations, offering guidance for product development. The global surge in demand during the pandemic led to a proliferation of substandard surgical N95 respirators, particularly in the e-commerce market, due to a lack of regulatory standards. In the post-pandemic era, the essential role of these respirators in protecting against airborne hazards and infectious agents persists. Addressing the need for standardized design and manufacturing processes, this research proposes a three-phased integrated decision-making framework. Firstly, text-mining algorithms analyze online consumer reviews to identify critical characteristics of surgical N95 respirators. Subsequently, a spherical-fuzzy-based quality function deployment assesses manufacturing capabilities in alignment with consumer requirements. The final phase utilizes Mixed Integer Non-linear Programming to optimize utility and select the most effective design, considering both technical specifications and consumer needs. The proposed framework is validated through a case study, revealing technical characteristics crucial for organizations in product development. The findings not only offer insights into the technical aspects of N95 respirators but also recommend essential product characteristic tests during manufacturing and certification. This information is vital for educating consumers and guiding companies in the effective use and development of Surgical N95 respirators in the post-pandemic landscape, addressing the challenges posed by inferior and counterfeit products.