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Sustainability
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Sustainable Supply Chain Management in Industry 4.0
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Sustainable Supply Chain Management in Industry 4.0

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

Deadline for manuscript submissions: 24 August 2024 | Viewed by 2020

Special Issue Editors

Dr. Zhitao Xu

E-Mail Website
Guest Editor
School of Management Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: intelligent scheduling; service manufacturing; digital twin; logistics and supply chain management; industrial policy
Dr. Zhenyong Wu

E-Mail Website
Guest Editor
School of Management Science and Engineering, Nanjing University of Information Engineering and Technology, Nanjing, China
Interests: logistics smulation and optimization; data-driven decision making; knowledge service
Prof. Dr. Yaping Fu

E-Mail Website
Guest Editor
School of Business, Qingdao University, Qingdao, China
Interests: production planning and scheduling; evolutionary multi-objective optimization; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the preceding decades, supply chain management has driven by the development of Industry 4.0  towards to a more efficient and greener system. Industry 4.0 embraces a set of future industrial developments regarding Internet of Things, cyber–physical systems, artificial intelligence, machine learning, data mining, cloud computing, blockchain, and big data analytics. Industry 4.0 represents the fourth industrial revolution and will offer wide prospects to gain competitive advantages and establish future sustainable supply chain practices.

Some of the changes that operations and their connected supply chains face are revolutionary, and this requires careful consideration from both practical and theoretical points of view. However, the implementation of I4.0 in SC remains in its infancy. Going forward, there is a need to explore how the Industry 4.0 helps organizations to monitor metrics on an ongoing basis, troubleshoot poor performance, and identify root cause, as well as enable the delivery of better business decisions and provide tremendous benefits through the improvement of business processes.

Thus, this Special Issue aims to explore the potential of Industry 4.0 opportunities available in supporting the data-driven revolution in sustainable supply chain space. In this Special Issue, we are looking for high-quality original research articles related (but not limited) to the following topics:

  1. Environmental, social and governance (ESG) in supply chains;
  2. Sustainable supply chain optimization in Industry 4.0;
  3. Resilience in supply chains and logistics;
  4. Product/service tracibility with blockchain in supply chain;
  5. Digital twin frameworks and methodologies for sustainable supply chains;
  6. Artificial intelligence in manufacturing and transport logistics;
  7. Use of simulation-based advanced data analytics for sustainable supply chain design;
  8. Artificial intelligence/digital twin-driven logistics optimization;
  9. Big data/artificial intelligence-driven e-commerce supply chains;
  10. Data analytics and machine learning in sustainable supply chains;
  11. Real-time optimization for green vehicel routing problem.

Dr. Zhitao Xu
Dr. Zhenyong Wu
Prof. Dr. Yaping Fu
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.

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

  • supply chain
  • Industrial 4.0
  • sustainability
  • data analytics
  • sustainable supply chain optimization
  • logistics

Published Papers (3 papers)

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Research

21 pages, 5007 KiB  
Article
Strategic Choices of General Contractors in the Context of China’s Industry Chain of Construction Industrialization
by Shengfei Li and Dalin Zeng
Sustainability 2024, 16(15), 6511; https://doi.org/10.3390/su16156511 - 30 Jul 2024
Viewed by 408
Abstract
Amidst the challenges of economic downturn and construction industrialization, the profits obtained by general contractors through comparative advantage strategies are slowly decreasing, and thus, new strategic choices are required. The collaborative division of labor effect in the industry chain can improve profits and [...] Read more.
Amidst the challenges of economic downturn and construction industrialization, the profits obtained by general contractors through comparative advantage strategies are slowly decreasing, and thus, new strategic choices are required. The collaborative division of labor effect in the industry chain can improve profits and labor productivity, which is an important driving force for enterprise transformation and development. Therefore, a need arises to improve the profits of general contractors in the industrial chain system composed of prefabricated component suppliers, general contractors, and building development enterprises. Accordingly, this paper constructs a backward integration, forward integration, and bidirectional integration Stackelberg game model based on the proportion of resource investments, with general contractors as the main decision-making body. It then compares and analyzes the optimal decision-making values in different situations to study the optimal strategic decision-making problem of general contractors. Research results indicate the following. (1) All three integrated strategies can improve the profits of general contractors. When the proportion of resource investment meets certain conditions, the profits of general contractors under the bidirectional integration strategy are the highest, while the sustainable performance of the industrial chain and prefabricated buildings can be increased and the coordination of the industrial chain can be achieved. Thus, it is the best choice for a general contractor. (2) As a prefabricated component supplier needs to carry out continuous technological innovation activity to obtain a cumulative effect, the return on investment of forward integration is less than that of backward integration. (3) General contractors may consider choosing to carry out bidirectional integration strategies of forward integration followed by backward integration. Full article
(This article belongs to the Special Issue Sustainable Supply Chain Management in Industry 4.0)
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23 pages, 1085 KiB  
Article
Exploring the Impacts of Green Supply Chain Integration and Ambidextrous Green Innovation on the Financial Performance of China’s Pharmaceutical Manufacturing Enterprises
by Guimei Yang, Feng Liu and Putthiwat Singhdong
Sustainability 2024, 16(15), 6501; https://doi.org/10.3390/su16156501 - 30 Jul 2024
Viewed by 396
Abstract
This study focuses on the factors affecting the financial performance of pharmaceutical manufacturing enterprises. Based on dynamic capability theory and ambidextrous innovation theory, this study adopts the Resource–Behavior–Performance framework to investigate how green supply chain integration (GSCI) and ambidextrous green innovation affect financial [...] Read more.
This study focuses on the factors affecting the financial performance of pharmaceutical manufacturing enterprises. Based on dynamic capability theory and ambidextrous innovation theory, this study adopts the Resource–Behavior–Performance framework to investigate how green supply chain integration (GSCI) and ambidextrous green innovation affect financial performance. The proposed hypotheses were tested through structural equation modeling using data from 400 China’s pharmaceutical manufacturing enterprises. The results indicate that the various dimensions of GSCI and ambidextrous green innovation yield distinct outcomes. Among the three dimensions of GSCI, only green supplier integration and green customer integration significantly impact financial performance directly, unlike green internal integration. Among the two dimensions of ambidextrous green innovation, only exploitative green innovation significantly influences financial performance, acting as a mediator between the GSCI dimensions and financial performance, while exploratory green innovation does not. The main advantages of this study include considering the connotation and value of GSCI from a green dynamic capability perspective, as well as the effects of exploratory and exploitative green innovation as intermediary behaviors while simultaneously considering the performance effects of GSCI and ambidextrous green innovation. This study offers novel academic insights and practical guidelines for pharmaceutical manufacturing enterprises to integrate GSCI and ambidextrous green innovation, with the aim of achieving better financial performance in their sustainable development efforts. Full article
(This article belongs to the Special Issue Sustainable Supply Chain Management in Industry 4.0)
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18 pages, 1073 KiB  
Article
An Improved Q-Learning Algorithm for Optimizing Sustainable Remanufacturing Systems
by Shujin Qin, Xiaofei Zhang, Jiacun Wang, Xiwang Guo, Liang Qi, Jinrui Cao and Yizhi Liu
Sustainability 2024, 16(10), 4180; https://doi.org/10.3390/su16104180 - 16 May 2024
Viewed by 598
Abstract
In our modern society, there has been a noticeable increase in pollution due to the trend of post-use handling of items. This necessitates the adoption of recycling and remanufacturing processes, advocating for sustainable resource management. This paper aims to address the issue of [...] Read more.
In our modern society, there has been a noticeable increase in pollution due to the trend of post-use handling of items. This necessitates the adoption of recycling and remanufacturing processes, advocating for sustainable resource management. This paper aims to address the issue of disassembly line balancing. Existing disassembly methods largely rely on manual labor, raising concerns regarding safety and sustainability. This paper proposes a human–machine collaborative disassembly approach to enhance safety and optimize resource utilization, aligning with sustainable development goals. A mixed-integer programming model is established, considering various disassembly techniques for hazardous and delicate parts, with the objective of minimizing the total disassembly time. The CPLEX solver is employed to enhance model accuracy. An improvement is made to the Q-learning algorithm in reinforcement learning to tackle the bilateral disassembly line balancing problem in human–machine collaboration. This approach outperforms CPLEX in both solution efficiency and quality, especially for large-scale problems. A comparative analysis with the original Q-learning algorithm and SARSA algorithm validates the superiority of the proposed algorithm in terms of convergence speed and solution quality. Full article
(This article belongs to the Special Issue Sustainable Supply Chain Management in Industry 4.0)
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