Citation: Li, S.M.; Lee, C.K.M.
Transfer-Learning-Based Opinion
Mining for New-Product Portfolio
Configuration over the Case-Based
Reasoning Cycle. Appl. Sci. 2022, 12,
12477. https://doi.org/10.3390/
app122312477
Academic Editor: Antonella Petrillo
Received: 31 October 2022
Accepted: 2 December 2022
Published: 6 December 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Transfer-Learning-Based Opinion Mining for New-Product
Portfolio Configuration over the Case-Based Reasoning Cycle
Shui Ming Li * and Carman Ka Man Lee
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
* Correspondence: richard-sm.li@connect.polyu.hk
Abstract:
Due to the ever-changing business environment, enterprises are facing unprecedented
challenges in their new-product development (NPD) processes, while the success and survival of
NPD projects have become increasingly challenging in recent years. Thus, most enterprises are eager
to revamp existing NPD processes so as to enhance the likelihood of new products succeeding in
the market. In addition to the determination of sustainable new-product ideas and designs, new-
product portfolio management (NPPM) is an active research area for allocating adequate resources to
boost project development, while projects that perform poorly can be terminated. Since the existing
new-product portfolio configuration is manually decided, this study explores the possibility of stan-
dardising NPPM, particularly the configuration mechanism, in a systematic manner. Subsequently,
case-based reasoning can be applied to structure the entire NPPM process, in which past knowledge
and successful cases can be used to configure new projects. Furthermore, customer feedback was
analyzed using the transfer-learning-based text classification model in the case-retrieval process to
balance the values of enterprises and customers. A new-product portfolio was therefore configured
to facilitate NPPM under an agile–stage-gate model. To verify the effectiveness of the proposed
system, a case study in a printer manufacturing company was conducted, where positive feedback
and performances were found.
Keywords:
new product development; portfolio management; case-based reasoning; transfer learning;
text classification
1. Introduction
In recent years, due to the presence of several major supply chain disruptions lead-
ing to shortages of electronic components—for example, the COVID-19 pandemic and
Russia–Ukraine war—new-product development (NPD) in industry has become unprece-
dentedly 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 stud-
ies 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 prioritiza-
tion 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 portfo-
lios should be configured with the most reasonable settings at the beginning, rather than
Appl. Sci. 2022, 12, 12477. https://doi.org/10.3390/app122312477 https://www.mdpi.com/journal/applsci
