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Advances in Sustainable Smart Manufacturing and Industrial Intelligence

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1135

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Special Issue Editors

Dr. Xianyu Zhang

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Guest Editor

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

Prof. Dr. Xinguo Ming

E-Mail Website
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,

Smart manufacturing systems have become a key way of enhancing enterprise intelligence, greenness, and sustainability. Smart, green, and sustainable designs have become the focus of attention for enterprises and researchers. We invite scholars and enterprises of all disciplines to share their interests and successful cases here. Example topics include applications of AI in smart manufacturing, innovations in smart manufacturing models, intelligence of smart manufacturing businesses, sustainable development of enterprises, construction routes of smart manufacturing, etc.

Original research articles and reviews papers are welcome. Research areas may include (but are not limited to) the following:

Sustainable smart manufacturing;
Enterprise digital transformation;
Smart/green/sustainable manufacturing systems;
Smart/green/sustainable supply chains;
Smart production system designs;
Industrial artificial intelligence;
Industrial intelligence networking;
Service-oriented manufacturing;
Self-adaptive scheduling;
Smart lean operations;
Productive services;
Manufacturing service outsourcing;
Sustainable value;
Sustainable development.

This Special Issue aims to enable manufacturing industries to explore the mature technologies, methods, and models that are currently available. By sharing knowledge amongst experts and scholars, cutting-edge methods can be promoted and applied in order to promote the high-quality development of the manufacturing industry, achieve digitization, intelligence, greening, sustainability, and sustainable stakeholder value.

We look forward to receiving your contributions.

Dr. Xianyu Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart manufacturing
digital manufacturing
green manufacturing
sustainable manufacturing
sustainable value network
smart product severs systems
industrial artificial intelligence

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Research

22 pages, 1673 KB

Open AccessArticle

Boundary Recognition and Value Capture for Sustainable Intelligent Interconnected Ecosystem (SICE) Oriented Smart Product Service

by Haiqin Xie, Xinguo Ming, Maokuan Zheng and Xianyu Zhang

Sustainability 2026, 18(8), 4066; https://doi.org/10.3390/su18084066 - 20 Apr 2026

Viewed by 270

Abstract

With the profound transformation of service-oriented manufacturing worldwide since the 21st century, a new industrial model based on the combination of products and services has become a new profit and value growth point for manufacturing enterprises. Enterprises are shifting from simple product production to providing a comprehensive product-service system, further evolving into a smart product-service system, and ultimately expanding into Sustainable Intelligent Interconnected Ecosystems (SICE). The complexity and dynamic nature of SICE make its business and value boundaries unclear, and there is no effective theoretical framework and method for boundary identification and value capture, which hinders the sustainable development of SICE. To clarify the effective boundaries of the operation of the SICE, an innovative model and methods of the framework process, system boundaries for the SICE have been proposed. This study constructs a systematic framework for SICE business and value boundary research to optimize business boundary integration, and verifies the model through empirical research in the smart-home industry, providing a new method for SICE boundary identification and value capture. The system boundary research methods proposed in this paper can identify the business boundary and value boundary of the smart-home product-service ecosystem through a hierarchical approach, and the case illustration shows that the methods have certain applicability and practical guiding significance for the construction of smart-home product-service enterprise business platforms. Full article

(This article belongs to the Special Issue Advances in Sustainable Smart Manufacturing and Industrial Intelligence)

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13 pages, 375 KB

Open AccessArticle

The Influence of Communication Strategies of Intelligent Agents in Production Systems on the Shift of Sustainable Solutions

by Polina A. Sharko, Zhanna V. Burlutskaya, Aleksei M. Gintciak, Salbek M. Beketov and Karina A. Lundaeva

Sustainability 2025, 17(24), 11130; https://doi.org/10.3390/su172411130 - 12 Dec 2025

Cited by 1 | Viewed by 464

Abstract

Current decision support systems for recommending labor resource allocation and generating production schedules in the systems with decentralized technological process control often fail to account for the impact of participants’ communication strategies on the shifts in target performance indicators, which depend on the alignment between local goals of production units and the global objectives of the system. The goal of the present study is to develop an approach for determining optimal communication parameters among intelligent agents to achieve system-level performance targets using the previously developed multiagent systems (MAS) for optimizing technological processes. The research investigates how agent constraint systems influence both overall system welfare and the individual welfare of agents, considering the shifts in their objective functions driven by preferred communication strategies. A workflow is developed to identify effective constraints. Using this workflow, the study provides recommendations for assigning regional field development plans, accounting for participants’ tendencies toward cooperation. On data where the potential for increasing the region’s flow rate through optimization of labor resource allocation and scheduling of well intervention operations (GTO) does not exceed 6%, the presented solution enabled the development of field plans that result in an additional 1% increase in the predicted oil production region’s flow rate on top of the gain achieved through resource allocation optimization. Full article

(This article belongs to the Special Issue Advances in Sustainable Smart Manufacturing and Industrial Intelligence)

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