Fit sessions are essential in making well-fitted clothing. During these sessions, apparel fit is determined iteratively by a team of designers, technical designers, fit models, and merchandisers. With the advent of the digitalization of new product development processes in the fashion industry, fit sessions have been seen as a bottleneck for they are still held in person. However, the process is currently still irreplaceable, and what makes the feedback provided by fit models important is an area that has never been tapped. Therefore, the present study aimed to understand fit models' role in fit sessions as well as how they assess the fit of garments and deliver their feedback. On-site observation during fit sessions and individual interviews with fit models were conducted. It was found that fit models gave comfort, fit, and tactile comments by testing garments while standing and moving between several postures. They had knowledge that overlapped with those of technical designers and designers and this empowerment enabled them to take part in the decision-making process in fit sessions. It also was found that fit models' feedback on garments was essential as they were the first people to try on the garments and present the customers' points of view.

Apparel product development is an iterative problem-solving process that is heuristic in nature and involves turning 2D flat patterns into a 3D garment that would fit human anatomy. The digital transformation trend in the apparel industry will accelerate in the post-pandemic era. Therefore, it is crucial to better understand the dynamics as well as the types of information generated during apparel product development to translate this information into the digital realm to better support the apparel industry. Technical apparel designers acquire important knowledge on garment fit during their years in the workforce. Documenting their knowledge could assure that the company's know-how would be saved and used to train the future workforce. Technical designers' problem-solving strategies can be tracked, coded, and made a part of artificial intelligence (AI) technology for product development sessions. This would help strengthen the competitiveness of both existing and new design companies. However, the knowledge possessed by the technical designers to enable much-improved digitalization would require a thorough analysis of tacit or implicit knowledge, which is acquired through years of experience. Due to the nature of how apparel designers learn by directly manipulating materials with their hands, this knowledge type is challenging to document. Nonetheless, if a successful method can be developed to document this knowledge, it would be very rewarding, especially for organizational knowledge management, and allow companies to digitalize using AI.