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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 3875

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Special Issue Editors

Dr. Xianyu Zhang

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Guest Editor

Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: smart manufacturing; green manufacturing; industrial intelligence; mass personalization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhitao Xu

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Guest Editor

College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: green/sustainable supply chain; service supply chain; intelligent scheduling; lean operation; productive services

Dr. Zhihua Chen

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Guest Editor

Department of Transportation Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: smart product service system; smart manufacturing; intelligent transportation system

Prof. Dr. Xinguo Ming

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Guest Editor

Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: industrial artificial intelligence; industrial intelligence networking; service-oriented manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of industrial technology, industry is gradually evolving towards smart manufacturing (Zhang and Ming 2022). While improving production efficiency and saving costs, the sustainability and greening of resources (Zhang et al. 2019), the environment and safety in the manufacturing process are important factors determining the long-term development of industry. Sustainable smart manufacturing (Abu-Bakr et al. 2020; Nar et al. 2020) aims to improve the sustainability of industrial ecology, economy and society through smart manufacturing technologies and means, such as industrial big data, industrial internet (Zhang and Ming 2022), industrial artificial intelligence, digital twins and industrial blockchain. Therefore, the planning, organization, configuration, optimization, application and implementation of data, networks, resources, system platforms and personnel in sustainable smart manufacturing are important topics to be studied (Zhang et al. 2022).

The rapid development of advanced smart technologies (e.g., cyber-physical systems (CPSs), Internet of Things (IoT) and artificial intelligence (AI)) has triggered a prospective smart connected product (SCP) market, thus, enabling the mainstream trend of manufacturing value proposition towards smart product service systems (PSSs) (Zhou et al. 2022). A smart PSS is defined as a bundle of SCPs and the generated smart services by leveraging SCPs as the media and tool, which are delivered to market for satisfying the personalized needs of customers, as well as providing more sustainable values (i.e., economic, environmental and social benefits) (Li et al. 2021). In the context of the Internet of Everything (IoX), large quantities of data are produced from SCPs’ operation lifecycles, ultimately being converted to smart data-driven value creation through various analytic techniques (Chen et al. 2020). It has attracted incremental attention from academia and practice due to its potential for enhancing manufacturers’ competitive strengths, improving customer satisfaction, promoting sustainable value, etc. However, there exist some issues requiring solutions, such as how these smart technologies impact the sustainability of PSSs, how to embed the sustainability concerns in smart PSS design and how smart PSSs improve the sustainability of industrial practice, e.g., sustainable manufacturing, sustainable transportation, sustainable city, etc.

The technologies of the fourth industrial revolution, also known as smart technologies, have the potential to reshape operations and supply chain management. The potential benefits of integrating smart technologies and supply chain management are diverse; nevertheless, one of the most promising domains is sustainability (Xu et al. 2022). With the blend of digital technologies, it is essential for organizations to achieve a high level of supply chain visibility, making the required information readily available to decision makers for the development of sustainable supply chain strategies (Chehbi-Gamoura et al. 2020; Jabbour et al. 2020). Using two streams of data, namely, real-time data and big data, organizations can cope with uncertainties and dynamics, both in supplies and customer demands, efficiently by reconfiguring their network and process at the interorganization level, incorporating new partners, innovating information sharing and collaboration mechanisms and optimizing logistics services to achieve sustainability. However, the challenge for both academia and industry is to determine how to embed data technologies into supply chain management to enhance sustainability, as well as to develop innovative tools, techniques and models that can leverage these technologies to unlock value.

This Special Issue aims to collect a selection of papers presenting original and innovative contributions to the study of sustainable smart manufacturing and services, focusing on sustainable smart manufacturing, sustainable smart product service systems and sustainable smart supply chain.

References

Abu-Bakr, M.; Abbas, A.T.; Tomaz, I.; Soliman, M.S.; Hegab, H.A. Sustainable and Smart Manufacturing: An Integrated Approach. Sustainability 2020, 12, 2280.

Chehbi-Gamoura, S.; Derrouiche, R.; Damand, D.; Barth, M. Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model. Prod. Plan. Control. 2020, 31, 355–382.

Chen, Z.; Lu, M.; Ming, X.; Zhang, X.; Zhou, T. Explore and evaluate innovative value propositions for smart product service system: A novel graphics-based rough-fuzzy DEMATEL method. J. Clean. Prod. 2020, 243, 118672. doi:https://doi.org/10.1016/j.jclepro.2019.118672.

Jabbour CJ, C.; Fiorini PD, C.; Ndubisi, N.O.; Queiroz, M.M.; Piato É,L. Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda. Sci. Total. Environ. 2020, 725, 138177.

Li, X.; Wang, Z.; Chen, C.-H.; Zheng, P. A data-driven reversible framework for achieving Sustainable Smart product-service systems. J. Clean. Prod. 2021, 279, 123618. doi:https://doi.org/10.1016/j.jclepro.2020.123618.

Nar, Z.M.; Nuhu, A.A.; Zeeshan, Q.; Korhan, O.; Asmael, M.; Safaei, B. Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability 2020, 12, 8211.

Xu, Z.; Elomri, A.; Al-Ansari, T.; Kerbache, L.; El Mekkawy, T. Decisions on design and planning of solar-assisted hydroponic farms under various subsidy schemes. Renew. Sustain. Energy Rev. 2022, 156, 111958. doi:https://doi.org/10.1016/j.rser.2021.111958.

Zhang, X.; Ming, X. Implementation path and reference framework for Industrial Internet Platform (IIP) in product service system using industrial practice investigation method. Adv. Eng. Inform. 2020, 51. doi:10.1016/j.aei.2021.101481.

Zhang, X.; Ming, X. A Smart system in Manufacturing with Mass Personalization (S-MMP) for blueprint and scenario driven by industrial model transformation. J. Intell. Manuf. 2022. doi:10.1007/s10845-021-01883-z.

Zhang, X.; Ming, X.; Bao, Y.; Liao, X. System construction for comprehensive industrial ecosystem oriented networked collaborative manufacturing platform (NCMP) based on three chains. Adv. Eng. Inform. 2022, 52. doi:10.1016/j.aei.2022.101538.

Zhang, X.; Ming, X.; Liu, Z.; Qu, Y.; Yin, D. General reference model and overall frameworks for green manufacturing. J. Clean. Prod. 2019, 237. doi:10.1016/j.jclepro.2019.117757.

Zhou, T.; Chen, Z.; Cao, Y.; Miao, R.; Ming, X. An integrated framework of user experience-oriented smart service requirement analysis for smart product service system development. Adv. Eng. Inform. 2022, 51, 101458. doi:https://doi.org/10.1016/j.aei.2021.101458.

Dr. Xianyu Zhang
Prof. Dr. Zhitao Xu
Dr. Zhihua Chen
Prof. Dr. Xinguo Ming
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

(1) Sustainable smart manufacturing

smart manufacturing
digital manufacturing
green manufacturing
industrial big data
industrial internet
industrial artificial intelligence
industrial blockchain
digital twin
data driven

(2) Sustainable smart product service system (PSS)

sustainable smart PSS framework
smart product service ecosystem
sustainability awareness smart PSS design
smart PSS for sustainable manufacturing
smart PSS for sustainable transportation
smart PSS for sustainable city

(3) Sustainable Smart supply chain

circular supply chain
supply chain collaboration
sustainable logistics service
smart delivery
Industry 4.0 in supply chain

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Research

21 pages, 2657 KiB

Open AccessArticle

Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization

by Mohammed Alkahtani, Mustufa Haider Abidi, Hamoud S. Bin Obaid and Osama Alotaik

Sustainability 2023, 15(18), 13815; https://doi.org/10.3390/su151813815 - 16 Sep 2023

Viewed by 808

Abstract

Due to the emergence of technology, electric motors (EMs), an essential part of electric vehicles (which basically act as engines), have become a pivotal component in modern industries. Monitoring the spare parts of EMs is critical for stabilizing and managing industrial parts. Generally, the engine or motor parts are delivered to factories using packing boxes (PBs). This is mainly achieved via a pooling center that manages the operation and transportation costs. Nevertheless, this process has some drawbacks, such as a high power train, bad press, and greater energy and time consumption, resulting in performance degradation. Suppliers generally take the parts from one place and deliver them to the other, which leads to more operation and transportation costs. Instead, it requires pooling centers to act as hubs, at which every supplier collects the material. This can mitigate the cost level. Moreover, choosing the placement of pooling centers is quite a challenging task. Different methods have been implemented; however, optimal results are still required to achieve better objectives. This paper introduces a novel concept for pooling management and transport optimization of engine parts to overcome the issues in traditional solution methodologies. The primary intention of this model is to deduce the total cost of the system operation and construction. Programming techniques for transporting the PBs, as well as for locating the pooling center, are determined with the aid of an objective function as a cost function. The location of the pooling center’s cost is optimized, and a Modified Gannet Optimization Algorithm (MGOA) is proposed. Using this method, the proposed model is validated over various matrices, and the results demonstrate its better efficiency rate. Full article

(This article belongs to the Special Issue Sustainable Smart Manufacturing and Service)

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33 pages, 7841 KiB

Open AccessArticle

Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

by Yuan Chang, Xinguo Ming, Xianyu Zhang and Yuguang Bao

Sustainability 2023, 15(11), 8858; https://doi.org/10.3390/su15118858 - 31 May 2023

Cited by 1 | Viewed by 1209

Abstract

Compared with the conventional industrial product–service system, the smart industrial service ecosystem (SISE) mentioned in this study contains more service activity according to the characteristics of the industrial context, participation of various stakeholders and smart interconnected technologies. This study proposes a detailed modularization design framework for SISE, which can be referenced in various industrial contexts. Firstly, the context-based smart industrial service identification blueprint (SISIB) is proposed to describe the operation model of SISE and identify the service components. The SISIB can ensure that the designers understand the service and work process of the system and improve or carry out the smart industrial service (SIS) component identification. In the case of this article, SIS components from different industrial levels can be systematically identified. Secondly, smart collaboration and sustainable development principles are proposed for measuring the correlation degree among the service components. Considering the complexity and multi-level distribution nature of service components, the hyperedge concept is presented to realize the correlation comparison among the service components, and the evaluation linguistics is applied to handle the decision uncertainties. With this method, the effective correlation comparison between service components can be formed with few hyperedges. Thirdly, the hypergraph clustering theory is applied to define the SISE service module partition. The triangular fuzzy number is first used in hyperedge strength evaluation to comply with the vague linguistics from service design experts. The normalized hypergraph cut principle is realized using the K nearest neighbors (kNN) algorithm, and with this method, the new unified hypergraph and related Laplace matrix can be obtained. Then, the relevant eigenvalue of that Laplace matrix is gained, and the component clustering visualization is realized using the k-means algorithm. After the clustering is performed, several modular design schemes can be gained. In order to select the best modularization scheme, we referenced the modularity concept and realized the quality measurement for the modular design using hypergraph modularity criteria. Regarding these three steps, a detailed modularization case study for a renewable electricity service ecosystem design is presented to verify the viability and feasibility of the study in service modular design. The result showed that the framework in this study can realize the visible and clearance service component identification in a smart connected multi-level industrial context. The modular design scheme based on hypergraph can also achieve high modularity with a more convenient correlation evaluation. Full article

(This article belongs to the Special Issue Sustainable Smart Manufacturing and Service)

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22 pages, 4116 KiB

Open AccessArticle

Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM

by Zheng Wang, Peng Gao and Xuening Chu

Sustainability 2022, 14(19), 12094; https://doi.org/10.3390/su141912094 - 24 Sep 2022

Cited by 1 | Viewed by 1384

Abstract

Accurately predicting the remaining useful life of wind turbine gearbox bearing online is essential for ensuring the safe operation of the whole machine in the long run. In recent years, quite a few data-driven approaches have been proposed that use the sensor-collected data to deal with this problem, achieving good results. However, their effects are heavily dependent on the massive degradation data due to the nature of data-driven methods. In practice, the complete data collection is expensive and time-consuming, especially for newly built or small-scale wind farms, which brings the problem of using limited data into sharp focus. To this end, in this paper, a novel idea of first using the prior knowledge of an empirical model for data augmentation based on the raw limited samples and then using the deep neural network to learn from the augmented data is proposed. This helps the neural network to safely approach the degradation characteristics, avoiding overfitting. In addition, a new neural network, namely, pre-interaction long short-term memory (PI-LSTM), is designed, which is able to better capture the sequential features of time-series samples, especially in the periods in which the continuous features are interrupted. Finally, a fine-tuning process is conducted using the limited real data for eliminating the introduced knowledge bias. Through a case study based on real sensor data, both the idea and the PI-LSTM are proved to be effective and superior to the state-of-art. Full article

(This article belongs to the Special Issue Sustainable Smart Manufacturing and Service)

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