Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Engineering | en_US |
| dc.contributor.advisor | Chan, T. S. Felix (ISE) | en_US |
| dc.contributor.advisor | Lee, K. M. Carman (ISE) | en_US |
| dc.creator | Li, Shui Ming | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12468 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | An intelligent system for facilitating fuzzy front-end of innovation in the new product development process | en_US |
| dcterms.abstract | New product development (NPD) is essential for most business organizations to create new values, protect existing values, and maintain high profitability and sustainability. However, the success of NPD projects can be challenging to achieve, due to high organizational complexity, uncertain business environment, and time-critical innovation. Under the smart manufacturing paradigm, NPD is an active research area to establish effective measures through the adoption of data analytics and artificial intelligence approaches that help formulate intelligent decision support systems. This is particularly important for the idea selection, demand forecasting, and production planning stages in the manufacturing industry. Therefore, appropriate new products can be developed to facilitate overall business development in the market. In this project, an intelligent product development support model (IPDSM) is proposed. It integrates multi-criterion decision-making with artificial intelligence techniques to facilitate and improve the smoothness of NPD activities. On the one hand, the fuzzy best-worst method (BWM) is adopted to select the most appropriate new product idea for mass production based on multiple product characteristics that meet customer requirements. | en_US |
| dcterms.abstract | On the other hand, new product demand forecasting is formulated using an adaptive neuro-fuzzy inference system (ANFIS) such that industrial practitioners can effectively estimate the time-series sales volume along the various stages of NPD. Therefore, the predicted product lifecycle of new products can be generated. Using IPDSM, the production planning can be concentrated on targeted new products with an accurate estimation of the demand in the market. Accordingly, the entire NPD process in the manufacturing industry can be beneficial under tight monitoring and evaluation. In this research project, the fuzzy front-end of innovation in the research related to NPD is intelligently revamped. Moreover, the capabilities of new product idea selection, new product demand forecasting, and project portfolio management are automated to better link the NPD in the recent era of smart manufacturing. The NPD processes are computerized to effectively store essential data for analysis, and business intelligence is adopted to enhance new product success in the market. Lastly, the proposed research methodology for supporting the fuzzy front-end of innovation is implemented in a printer manufacturing company to verify the methodological feasibility and effectiveness. It is found that the NPD team members in the case company are mostly satisfied with the aforementioned multi-model enhancements. | en_US |
| dcterms.extent | xvii, 173 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2023 | en_US |
| dcterms.educationalLevel | Eng.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | New products -- Management | en_US |
| dcterms.LCSH | Product management | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6917.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.23 MB | Adobe PDF | View/Open |
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