## 6 Conclusions  
In summary, this study was motivated by existing challenges in the current manual NPPM process, which cannot satisfy the increasing customer and market requirements. Moreover, the reliability and consistency of NPPM are always questioned by the top management in manufacturing companies. Due to recent advances in artificial intelligence (AI), this study investigated the possibility of the integration of promising AI techniques to revamp the NPPM process, in which CBR was explored under the transfer-learning scheme for text classification. In the CBR mechanism, customer feedback is considered to enhance the case-retrieval process in order to avoid the dominance of product manufacturers in the NPD cycle. Therefore, a new-product portfolio can be built by referring to historical and successful cases, resulting in an enhanced quality of the new configurations. To verify the effectiveness of the proposed system, a case study in Gainscha (the case company) was conducted to revamp its NPD and NPPM processes in a systematic and standardized manner. Based on the empirical investigation of staff perspectives, it was found that the proposed system had a positive impact on NPPM, while relevant staff members were willing to work with the newly proposed system in order to build new project portfolios. In view of this research, this study contributes to both the academic and practical aspects of this topic. First, the main contribution of this study is its novel integration of CBR and transfer learning for text classification, which was proposed to determine attribute weights by considering customer reviews instead of mutual and intuitive approaches. Second, the entire NPPM process was standardized and computerized to achieve better portfolio quality and staff satisfaction.  
Regarding the major limitations of this study, the proposed system was validated solely by a single manufacturer in China, while customer feedback was related to a specific group of products. Moreover, customers can be influenced by other existing customer reviews and, therefore, the interaction between customers should be considered for opinion mining. In future research, customer reviews of various products and customer interaction should be considered for opinion mining, while the proposed system can be further implemented in other manufacturing companies to generalize research findings on the improvements in customer satisfaction and portfolio quality. Furthermore, more case studies in different manufacturing processes, not only industrial printing equipment, can be considered to further evaluate the benefits obtained from the proposed system. The value of this research study in the manufacturing industry can, thereby, be generalized.  
Conceptualization, S.M.L. and C.K.M.L.; methodology, S.M.L.; formal analysis, S.M.L.; writing--original draft preparation, S.M.L.; writing--review and editing, C.K.M.L.; supervision, C.K.M.L.; project administration, C.K.M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the Engineering Doctorate Programme, Faculty of Engineering of The Hong Kong Polytechnic University, and Pacific Business Machine Limited for inspiring the development of this research study.  
The authors declare no conflict of interest.