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A hybrid approach to resolve ambiguity in new product development.

Authors :
Liu, Feng-Lang
Chien, Li-Chih
Chang, Ting-Yu
Ku, Cooper Cheng-Yuan
Chang, Ching-Ter
Source :
Journal of Intelligent & Fuzzy Systems; 2024, Vol. 46 Issue 1, p1359-1378, 20p
Publication Year :
2024

Abstract

Improving technological innovation (TI) capabilities is an integral component of government policies aimed at improving the competitiveness of small and medium enterprises (SMEs). This study aims to address implementation challenges arising from the use of Qualitative Forecasting Method (QFM) in new product development programs and proposes a novel method to aid decision makers (DMs) in their decision-making process. To tackle this issue, a hybrid method is proposed, incorporating Fuzzy Delphi method (FDM), Fuzzy Analytic Hierarchy Process (FAHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Multi-Choice Goal Programming with utility function (MCGP-U), while introducing prospect theory as a novel approach. is proposed. The proposed method offers several advantages, including effective early planning, accurate identification of key success factors (KSFs), selection of the most suitable project leader, and estimation of the most reasonable resource investment, all of which are critical factors for success in TI for enterprises. The research results show that (1) the proposed method reduces project execution time by 20% compared to the original manual planning, (2) it facilitates the acquisition of KSFs using a rational approach to ensure project success, and (3) it increases the financial returns of the company by 17% compared to the company's forecast. In summary, this paper makes a significant contribution to practical applications and additionally contributes to decision-making field by introducing prospect theory into the proposed hybrid method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10641246
Volume :
46
Issue :
1
Database :
Complementary Index
Journal :
Journal of Intelligent & Fuzzy Systems
Publication Type :
Academic Journal
Accession number :
175159967
Full Text :
https://doi.org/10.3233/JIFS-234327