A voice of the customer real-time strategy: An integrated quality function deployment approach. (July 2022)
- Record Type:
- Journal Article
- Title:
- A voice of the customer real-time strategy: An integrated quality function deployment approach. (July 2022)
- Main Title:
- A voice of the customer real-time strategy: An integrated quality function deployment approach
- Authors:
- Shen, Yixuan
Zhou, Jian
Pantelous, Athanasios A.
Liu, Yanbao
Zhang, Ziying - Abstract:
- Highlights: A full-process product improvement solution driven by online reviews is raised. An online review screening model is constructed based on twenty-eight indicators. CRs are identified by text mining, and the weights are assigned via fuzzy inference. CRs' weights are mapped to ECs' priorities by machine-learning techniques. Abstract: In the era of Industry 4.0, the rapid development of information technology in the last decade has provided new challenges for product improvement by enabling users to give their feedback and sentiments in real time. In this paper, combining with genetic algorithm back propagation neutral network, fuzzy inference method, and entropy-based synthesis evaluation method, we raise a full-process product improvement solution driven by online reviews, from the initial online review collection to the final engineering characteristic prioritization. The proposed novel integrated quality function deployment-based approach adheres to the customer-oriented design principle, allowing manufacturers to strengthen the launched product based on the spontaneously-articulated voice of customer, rather than the traditional expertise. In this way, an off-the-shelf product improvement strategy is available for enterprises, and its special advantages like fast adaptation and real-time responsiveness, would significantly reduce management costs, shorten response time to market dynamics, and enhance customer satisfaction. In addition, a case study in smartphoneHighlights: A full-process product improvement solution driven by online reviews is raised. An online review screening model is constructed based on twenty-eight indicators. CRs are identified by text mining, and the weights are assigned via fuzzy inference. CRs' weights are mapped to ECs' priorities by machine-learning techniques. Abstract: In the era of Industry 4.0, the rapid development of information technology in the last decade has provided new challenges for product improvement by enabling users to give their feedback and sentiments in real time. In this paper, combining with genetic algorithm back propagation neutral network, fuzzy inference method, and entropy-based synthesis evaluation method, we raise a full-process product improvement solution driven by online reviews, from the initial online review collection to the final engineering characteristic prioritization. The proposed novel integrated quality function deployment-based approach adheres to the customer-oriented design principle, allowing manufacturers to strengthen the launched product based on the spontaneously-articulated voice of customer, rather than the traditional expertise. In this way, an off-the-shelf product improvement strategy is available for enterprises, and its special advantages like fast adaptation and real-time responsiveness, would significantly reduce management costs, shorten response time to market dynamics, and enhance customer satisfaction. In addition, a case study in smartphone industry is conducted for illustration, and the results clearly demonstrate the effectiveness and practicability of the treatment. … (more)
- Is Part Of:
- Computers & industrial engineering. Volume 169(2022)
- Journal:
- Computers & industrial engineering
- Issue:
- Volume 169(2022)
- Issue Display:
- Volume 169, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 169
- Issue:
- 2022
- Issue Sort Value:
- 2022-0169-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07
- Subjects:
- Quality function deployment (QFD) -- Online reviews -- Text mining -- Voice of customer (VoC) -- GA-BP neural network
Engineering -- Data processing -- Periodicals
Industrial engineering -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03608352 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cie.2022.108233 ↗
- Languages:
- English
- ISSNs:
- 0360-8352
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.713000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 22113.xml