The manufacturing industry is experiencing increasing complexity owing to advancements in digitalization, such as digital twins (D2), and a shift from mass production to high-mix, low-volume production systems. In the context of human-centric smart manufacturing, this study introduces a framework called digital triplet, proposed by our research group, with some case studies. We aimed to address these challenges by proposing the digital triplet concept. This framework integrates digital technologies with the intelligent activities of engineers, such as the continuous improvement of production systems, to support these activities rather than automating them. The digital triplet framework supports an engineer by recording, accumulating, and utilizing the knowledge of skilled experts. As part of this effort, we developed process modelling language for digital triplet (PD3), a language for describing the process knowledge of engineers. In addition, we constructed an integrated system to store this knowledge in relation to relevant D2 data. We proposed methods to utilize this knowledge to assist engineers and validated their effectiveness through case studies, including learning factories and quality improvements in the welding process. Through the case studies, the PD3 framework and methods enabled unskilled workers to perform at a level close to that of skilled engineers. Developing new methodologies to enhance the support for engineers presents significant chances for advancing manufacturing practices.