The Architecture of Mass Customization-Social Internet of Things System

Current Research Profile

Dados Bibliográficos

ID22031258
AutoresZixin Dou (0000-0002-1234-3989, Guangzhou University), Yanming Sun (0000-0001-7416-767X, Guangzhou University, autor correspondente), Zhidong Wu (0000-0002-5126-284X, Algorithm Research Center, Joyy Inc., Guangzhou 510000, China), Tao Wang (0000-0002-8367-8946, University of Malaya), Shiqi Fan (0000-0003-3714-7201, University of Hong Kong), Yuxuan Zhang (0009-0002-9553-8032, University of Warwick)
Ano2021
Volume10
Fascículo10
Páginas653
Data de publicação2021-09-28
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoISPRS International Journal of Geo-Information (JOURNAL)
Identificadores do periódicoISSN: 2220-9964 • E-ISSN: 2220-9964
EditoraMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10100653
OpenAlexW3203028150
IdiomaEN
Citações recebidas1
Referências citadas174

In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems

Big data · Data science · Knowledge management · Mass Customization · Personalization · The Internet · World Wide Web · Computer Science · Digital Transformation in Industry · Manufacturing Process and Optimization · Product Development and Customization · Architecture