Integration of data science with product design towards data-driven design

被引:3
作者
Liu, Ang [1 ]
Lu, Stephen [2 ]
Tao, Fei [3 ]
Anwer, Nabil [4 ]
机构
[1] Univ New South Wales, Sydney, NSW 2052, Australia
[2] Univ Southern Calif, Los Angeles, CA 90089 USA
[3] Beihang Univ, Beijing 100191, Peoples R China
[4] Univ Paris Saclay, ENS Paris Saclay, LURPA, F-91190 Gif Sur Yvette, France
关键词
Product design; Data science; Data-driven design; SUPPLY CHAIN MANAGEMENT; BIG DATA ANALYTICS; DATA QUALITY; PREDICTIVE ANALYTICS; ENGINEERING DESIGN; SENTIMENT ANALYSIS; SYSTEMS; FEATURES; MODEL; CHALLENGES;
D O I
10.1016/j.cirp.2024.06.003
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper aims to investigate the scientific integration of data science with product design towards datadriven design (D3). Data science has potential to facilitate design decision-making through insight extraction, predictive analytics, and automatic decisions. A systematic scoping review is conduced to converge various D3 applications in four dimensions: the design dimension about design operations, the data dimension about popular data sources and common data-related challenges, the method dimension about the methodological foundations, and the social/ethical dimension about social/ethical considerations and implications. Based on the state-of-the-art, this paper also highlights potential future research avenues in this dynamic field. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
引用
收藏
页码:509 / 532
页数:24
相关论文
共 287 条
[1]  
Aalst Van Der W, 2016, Data Science in Action
[2]   Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting [J].
Aamer, Ammar Mohamed ;
Yani, Luh Putu Eka ;
Priyatna, I. Made Alan .
OPERATIONS AND SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL, 2021, 14 (01) :1-13
[3]   A survey on data-efficient algorithms in big data era [J].
Adadi, Amina .
JOURNAL OF BIG DATA, 2021, 8 (01)
[4]   Data-mining-based methodology for the design of product families [J].
Agard, B ;
Kusiak, A .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2004, 42 (15) :2955-2969
[5]   A survey on deep learning in medical image reconstruction [J].
Ahishakiye, Emmanuel ;
Van Gijzen, Martin Bastiaan ;
Tumwiine, Julius ;
Wario, Ruth ;
Obungoloch, Johnes .
INTELLIGENT MEDICINE, 2021, 1 (03) :118-127
[6]   Reading functional requirements using machine learning-based language processing [J].
Akay, Haluk ;
Kim, Sang-Gook .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2021, 70 (01) :139-142
[7]   Algorithmic bias in data-driven innovation in the age of AI [J].
Akter, Shahriar ;
McCarthy, Grace ;
Sajib, Shahriar ;
Michael, Katina ;
Dwivedi, Yogesh K. ;
D'Ambra, John ;
Shen, K. N. .
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2021, 60 (60)
[8]   Privacy in smart toys: Risks and proposed solutions [J].
Albuquerque, Otavio de Paula ;
Fantinato, Marcelo ;
Kelner, Judith ;
de Albuquerque, Anna Priscilla .
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2020, 39
[9]   A 3D shape generative method for aesthetic product design [J].
Alcaide-Marzal, Jorge ;
Antonio Diego-Mas, Jose ;
Acosta-Zazueta, Gonzalo .
DESIGN STUDIES, 2020, 66 :144-176
[10]   Contestable AI by Design: Towards a Framework [J].
Alfrink, Kars ;
Keller, Ianus ;
Kortuem, Gerd ;
Doorn, Neelke .
MINDS AND MACHINES, 2023, 33 (04) :613-639