## 1 Introduction  
In recent years, due to the presence of several major supply chain disruptions leading to shortages of electronic components--for example, the COVID-19 pandemic and Russia-Ukraine war--new-product development (NPD) in industry has become unprecedentedly challenging regarding the development of successful new products for customers. Customer value and satisfaction are difficult to maintain, not to mention improvements in the current NPD process. To develop successful new products, some existing studies revealed that new-product ideas should be selected in a systematic and group-based manner, while product portfolios can be effectively formulated to address customer and technical needs [1, 2]. In the context of NPD research, the theory of new-product portfolio management (NPPM) has been widely discussed to measure, evaluate, and manage NPD projects [3]. In addition, decisions regarding continuation, termination, and prioritization can be made to strike a reasonable balance between manufacturers, customers, and other stakeholders. The effectiveness of NPPM can influence key performance aspects of new products launched in the market, such as production cycle time, human resources, and budget. Therefore, there is an urgent need for industrial practitioners to develop a systematic approach for the formulation of the most appropriate and reasonable product portfolios, enhancing the likelihood of new-product survival and success in the market. As the saying goes, \"prevention is always better than cure\", so the new-product portfolios should be configured with the most reasonable settings at the beginning, rather than making modifications and cancellations after NPD. However, the current configurations of NPPM suffer from two pitfalls. First, the current new-product portfolios are configured on a company-driven basis, such that the product manufacturers have the sole privilege of creating the product and project specifications, regardless of the customers' perspectives. Additionally, customer feedback is seldom considered in the portfolio configuration process. Moreover, the appropriate design of intelligent systems in assisting portfolio configuration is currently limited in industry. Although modern advances in artificial intelligence and machine learning in industrial design are significant [4], intelligent portfolio management is still a valuable but understudied area due to the lack of sample-rich datasets to train and validate machine-learning models. To address the above challenges in the context of NPPM, this study presents an intelligent new-product portfolio configuration system (INPPCS), where case-based reasoning (CBR) and transfer learning are hybridized to suggest the most appropriate portfolio configurations for the development of new products. More importantly, customers' reviews and feedback were extracted and analyzed to derive the weights of the portfolio management criteria. Thus, customers are involved in determining the most similar case retrievals in the CBR cycle, so the retrieved cases can serve as references to support the development of a new-product portfolio. In order to effectively understand customers' needs, the transfer-learning approach is adopted to perform a review classification through fine-tuning the pre-trained bidirectional encoder representations from transformers (BERT). Therefore, the classification performance can be maintained to a reasonable degree, even if the dataset is relatively small. Such a systematic design can effectively satisfy the needs of NPPM, when the size of customers' comments on specific new products are relatively small. To further verify the feasibility of the proposed system, a case study of a printer manufacturer located in China was undertaken, in which the manufacturer was eager to design and develop new printing equipment and services for their customers.