Exploring robustness management of social internet of things for customization manufacturing
Introduction
In recent years, customization is gradually integrated with manufacturing industry, which brings out an innovative manufacturing paradigm, known as customization manufacturing (CM) [1]. Among the main reasons why CM enjoys a large amount of attention, we can note its ability to create enormous strategic benefits, including innovative products and services [[2], [3]], steady revenue streams [[4], [5]], high levels of customer satisfaction and customer loyalty [[6], [7]], and social sustainability [8 ]. The CM strategy highlights that no enterprise (not even the large and global) is able to complete all business processes from the initial acceptance of customized orders to the final provision of personalized products or services, therefore the traditional big-and-overall enterprise is replaced by many small-and-specialized enterprises which together constitute a CM network [[9], [10]]. The mission of such a network is to efficiently integrate the resources of decentralized enterprises. According to research by Mourtzis et al. [11], CM is and will continue to be a mainstream manufacturing mode in the 21st century, being boosted by emerging technologies like social internet of things (SIoT) [[12], [13]], wireless network [[14], [15]], big data [[16], [17]], cloud computing [[18], [19]], and mobile Internet [[20], [21]]. Of these technologies, efficient SIoT with a well-designed structure and advanced managerial level is a significant determinant for any CM network to fulfill its mission through making product-related data in the right context available for the right person or machine at the right time. In that case, SIoT, as a kind of inter-organizational systems, is created for the CM paradigm.
SIoT under the CM paradigm is driven by customized orders, and contains multiple heterogeneous enterprise systems (ESs) between which non-linear relationships can be incompatible, compatible or even integrated (which together form integration continuum) [[22], [23]]. As shown in Fig. 1 , these relationships are embodied in various aspects, such as organizational structures and culture, technology standards, syntaxes and semantics [24]. Once a pair of improper relationships emerges in one aspect, such relationships may induce local inefficient operations, followed by cascading failures and ending up as a global inefficient operation. In Fig. 1, if the technical standards between suppliers 1 and 2 are conflicting, the ESs of the two turn to operate inefficiently. Over time, the ESs of the manufacturer, collaborative manufacturer, stakeholder and retailer are impacted in turn, eventually leading to the failed fulfillment of customized orders in the SIoT. Such phenomena are common in the real world [25], and the resulting losses grow exponentially along CM networks. A robust operation is thus crucial for any SIoT under the CM paradigm, particularly when interactions between ESs play an important role.
Existing literature mainly concerns on the design and implementation of SIoT, and there has been little research done on its robustness. This paper proposes a methodology for the robustness management of SIoT based on complex systems thinking. The methodology divides the SIoT under the CM paradigm into several ESs and anatomizes the state variables of those ESs as well as the interactions between them, which enables us to establish nonlinear dynamic equations to model the SIoT. This methodology exposes the rich dynamics of SIoT in the CM environment along integration continuum, and discovers the threshold conditions enabling the SIoT to operate robustly when using the proposed algorithms.
The novel aspects of our research are as follows: (1) from a system modeling perspective, given the complexity and dynamics of SIoT under the CM paradigm, this article applies complexity science methods to construct nonlinear dynamic models to quantitatively assess the robustness of the SIoT in essence, which overcomes the current research limitations represented by the static structural modeling of the SIoT; (2) from a robustness management perspective, this paper attempts to address the robustness of the SIoT starting from the root causes, by selecting proper partners for ESs and determining favorable degree of compatibility or coupling between ESs. The proposed methodology synthesizes previous research to offer a practical framework for managing the robustness of SIoT in the CM environment, which facilitates the ex-ante robustness-based design and the ex-post robustness control of the SIoT.
The remainder of the paper is the following. In Section 2, a review of the relevant literature is presented. Section 3 discusses the operational models for SIoT in the CM environment, while Section 4 presents the system robustness analysis. Section 5 conducts numerical simulations, and analyzes the results. The application of the proposed methodology in manufacturing supply chains is described in Section 6. Section 7 concludes the paper, and suggests future research directions.
Section snippets
Literature review
Three streams of literature are related to this paper. The first one addresses the recent research on SIoT, the second one introduces the integration continuum, and the last one describes complex systems.
In the existing literature, there are several perspectives in addressing SIoT. Atzori et al. [26] identified proper strategies for establishing and managing SIoT, analyzed the features of SIoT and designed an architecture for SIoT. Li and Parlikad [12] emphasized that SIoT was valid in
Model setup
An ES is a complex technical–organizational system [[24], [53]], where the technical system and organizational system focus on information and communication technologies and management activities, respectively [45]. In non-linear systems theory, the properties or features of a system are represented by state variables whose changes over time reflect the operational laws of the system. The state variables of that technical system and organizational system are defined as technical level and
Robustness analysis
Robust systems can achieve stable and high performance against inherent and environmental disturbances. From a complex systems perspective, a stable performance is registered when the order parameters under disturbances temporarily deviate from the values that they finally return to, while a high performance means that the values of order parameters are no less than the targets. The case that the robustness of a system varies by the values of its order parameters is presented as follows: if the
Numerical simulations
This section performs a series of numerical simulations for the SIoT represented by Eq. (6). In Eq. (6), the parameters and () are free from human interference, and the technical level represents the order parameter of ES i. Therefore, parameters , and 2 are supposed to be constant. We consider them as , , , , , , , , and using Delphi technique. In the following, we set the targeted technical levels
Applications in manufacturing supply chains
The flexible delivery of materials between enterprises, the dynamic configuration of resources within enterprises as well as the timely provision of personalized products and services for customers compel manufacturing supply chains to be more adaptable. This adaptability depends on whether manufacturing supply chains can realize non-limited access to data in anytime and anywhere for anyone and anything. SIoT incorporates cyber and physical elements involved in all business processes from the
Conclusion and future avenues of research
In this paper, we develop a novel methodology for quantitative modeling of robustness management in SIoT. The definition of subsystems and state variables for SIoT provides essential elements for the mathematical formulation of our model. The model describes the robustness of SIoT along an integration continuum and determines the degree of compatibility or coupling that enables the robust operations of SIoT, being supported by the proposed algorithms.
Moreover, we demonstrate the utility of our
Acknowledgments
This work is supported by the Natural Science Foundation of China (Nos. 71571072, 71071057, 51208454, 61572220, and 61262013), the Department of Science and Technology of Guangdong Province (Nos. 2014B090901001 and 2015B010103002), the Natural Science Foundation of Guangdong Province, China (Nos. 2016A030313735 and 2016A030313734), and the Fundamental Research Funds for the Central Universities (No. 2015ZZ079).
Zhiting Song received the B.A. degree in industrial engineering from East China Jiaotong University, China, in 2013. She is currently a Ph.D. candidate in the School of Business Administration, South China University of Technology, China. Her research interests include smart manufacturing, cyber–physical systems, information systems, and complex systems.
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Zhiting Song received the B.A. degree in industrial engineering from East China Jiaotong University, China, in 2013. She is currently a Ph.D. candidate in the School of Business Administration, South China University of Technology, China. Her research interests include smart manufacturing, cyber–physical systems, information systems, and complex systems.
Yanming Sun is a Professor in the School of Business, Guangzhou University, China. He has directed multiple research projects, including the National Natural Science Foundation of China, and has authored/co-authored many scientific papers. His research interests are information systems, smart manufacturing, and big data.
Jiafu Wan has been a Professor in School of Mechanical & Automotive Engineering at South China University of Technology (SCUT) since Sep 2015. His research interests include cyber–physical systems, industry 4.0, smart factory, industrial big data, industrial robot and internet of vehicles. He has directed 13 research projects, including the National Natural Science Foundation of China, the High-level Talent Project of Guangdong Province, and the Natural Science Foundation of Guangdong Province. Thus far, he has published more than 120 scientific papers, including 70+ SCI-indexed papers, 20+ IEEE Trans./Journal papers, 9 ESI Highly Cited Papers and 3 ESI Hot Papers. His research results have been published in several famous journals, such as IEEE Transactions on Industrial Informatics, IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, IEEE Transactions on Intelligent Transportation Systems, IEEE Network, IEEE Wireless Communications, IEEE Systems Journal, IEEE Sensors Journal, and ACM Transactions on Embedded Computing Systems. According to Google Scholar, his published work has been cited more than 2800 times (H-index = 27). His SCI other citations (sum of times cited without self-citations) reached 648 times according to Web of Science Core Collection. He is an Associate Editor for IEEE Access (SCI), and he is a Managing Editor for IJAACS (Ei Compendex) and IJART (Ei Compendex). He is a Leading Guest Editor for several SCI-indexed journals, such as IEEE Systems Journal, IEEE Access, Elsevier Computer Networks, Mobile Networks & Applications, Computers and Electrical Engineering, and Microprocessors and Microsystems. He is General Chair for 2016 International Conference on Industrial IoT Technologies and Applications (IndustrialIoT 2016) and 7th EAI International Conference on Cloud Computing (CloudComp 2016). He is a senior member of both CMES and CCF, and a member of IEEE.
Lingli Huang is currently a lecturer in School of Mathematics, South China University of Technology, China. She got her M.S. degree in management science and engineering from South China University of Technology in 2016. Her research interests are nonlinear dynamics, applied mathematics, and dynamic systems.
Yan Xu obtained the B.A. degree in information management and information systems from Shanxi Normal University, Xian, China in 2015. She is now a Ph.D. candidate in the School of Business Administration, South China University of Technology, China. Her research interests include big data, fault diagnosis, and smart manufacturing.
Ching-Hsien Hsu is a professor and the chairman in the department of computer science and information engineering at Chung Hua University, Taiwan; He was a distinguished chair professor at Tianjin University of Technology, China, during 2012–2016. His research includes high performance computing, cloud computing, parallel and distributed systems, big data analytics, ubiquitous/pervasive computing and intelligence. He has published 200 papers in top journals such as IEEE TPDS, IEEE TSC, ACM TOMM, IEEE TCC, IEEE TETC, IEEE System, IEEE Network, top conference proceedings, and book chapters in these areas. Dr. Hsu is the editor-in-chief of International Journal of Grid and High Performance Computing, and International Journal of Big Data Intelligence; and serving as editorial board for a number of prestigious journals, including IEEE Transactions on Service Computing, IEEE Transactions on Cloud Computing, International Journal of Communication Systems, and International Journal of Computational Science. He has been acting as an author/co-author or an editor/co-editor of 10 books from Elsevier, Springer, IGI Global, World Scientific and McGraw-Hill. Dr. Hsu was awarded nine times distinguished award for excellence in research and annual outstanding research award through 2005 to 2016 from Chung Hua University. Since 2008, he has been serving as executive committee of IEEE Technical Committee of Scalable Computing; IEEE Special Technical Committee Cloud Computing; Taiwan Association of Cloud Computing. He is vice chair of IEEE TCCLD, IEEE TCSC and IEEE senior member.