## 5 Results and Discussion
### Managerial Implications  
In the past, the new-product portfolio formulation and management fully relied on specific professionals who had extensive experience working on NPD [26]. Upper management only believed the decisions made by professionals, and thus the stage-gate model was applied to regularly monitor and adjust NPD projects so as to assign appropriate resources and talent. Such a human-driven approach involves a certain extent of bias and subjectivity, while professional knowledge cannot be effectively transferred to and inherited by other staff members [27]. Consequently, the quality and reliability of NPD projects were highly subjective for the experts and, therefore, the NPD process--in particular NPPM--was not sustainable for business development. With the aid of the proposed INPPCS, the overall procedures to manage product portfolios were standardized as the essential tool for NPD activities. New products, including improved products and brand-new products, could be developed according to the historical portfolios so as to allocate adequate budgets, resources, and talents. By doing so, the reliability and consistency of the entire NPD process could be improved, resulting in an increase in the likelihood of new-product success. In addition, based on historical practices, the NPPM process was almost entirely expert-driven, while customers had no stake in the project portfolio formulation. It was difficult to involve customers' perspectives in the traditional NPPM process, and thus customer requirements might not have been fully satisfied [28; 29]. According to the mechanism of the proposed system, customer feedback on relevant products can be analyzed to understand up-to-date customer wants and needs in order to fine-tune the strategy for the setup of NPD projects. It is commonly understood that building a robust machine-learning model for NLP applications is relatively difficult, particularly in the collection of sample-rich datasets for model training. Therefore, the transfer-learning scheme proposed in this study is an effective approach in understanding customer feedback based on limited datasets, resulting in effective model training and validation [30]. The entire new-product portfolio configuration process can become dynamic and sensitive to  
customer feedback, so that a customer-driven NPPM can be built in the era of intelligent manufacturing. Under the paradigm of Industry 4.0, an increasing number of intelligent technologies and algorithms can be applied to revamp design, production, and logistics operations to improve productivity, service levels, and sustainability [31; 32]. The power of machine learning should be leveraged to enhance the supply-chain capability in a dynamic and fluctuating business environment.  
Furthermore, with the findings of the case study, the proposed system has a positive impact on NPD activities with regard to the design and development of new industrial printing equipment. Manufacturing companies involving NPD activities can, therefore, obtain similar benefits by using the proposed approach to standardize and automate new-product portfolio configuration and management. By referring to past successful NPD projects, future NPD projects can be formulated in a suitable manner to enhance the success of new products in the market.