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Global Supply Chain Management for Sustainable Organizational Performance
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Global Supply Chain Management for Sustainable Organizational Performance

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 5151

Special Issue Editors

Prof. Dr. Ahmad K. Elshennawy

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management Systems, University of Central Florida (UCF), Orlando, FL 32816, USA
Interests: supply chain management; Industry 4.0; operations management; Quality 4.0; lean six sigma; reliability engineering
Prof. Dr. Elizabeth Cudney

E-Mail Website
Guest Editor
John E. Simon School of Business, Maryville University, St. Louis, MO 492010, USA
Interests: lean six sigma; quality; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global supply chain management aims to improve efficiency, achieve sustainable organizational performance, and enhance profitability for suppliers, manufacturers, logistics providers, and warehouses. Organizations in the industry are preparing for the challenges of leveraging advanced technologies and integrating industrial engineering tools and methods for sustainable performance, improving efficiency, maintaining high quality levels and improved customer satisfaction, and achieving desired value and optimized processes. Opportunities for research on the supply chain extend to the study of logistics, inventory management, and warehousing optimization, as well as the use of data analytics, artificial intelligence, and other advanced tools and technologies to optimize the supply chain process and improve its performance. 

This special edition of Sustainability seeks contributions from researchers and practitioners in global supply chains. Topics include, but are not limited to:

  • Critical success factors for implementing frameworks and methodologies for global supply chain management.
  • Integration of Industry 4.0 into global and sustainable supply chain processes.
  • Impact of the human factor on the sustainability of supply chain management.
  • Supply chain 4.0: challenges and applications.
  • Artificial intelligence and advanced technologies in supply chain management.
  • Smart warehouse management, logistics, and transportation.
  • Critical success factors for implementing AI and other digital technologies.
  • Organizational readiness for AI and smart supply chain adoption.
  • Case studies, practical applications, and best practices that allow readers to apply AI tools to solve actual supply chain problems and achieve sustainability in the organization.

In addition, we are seeking high-quality articles emphasizing strategic support, warehouse optimization, big data and data-driven strategic decision-making, and organizational readiness for global supply chain performance.

Prof. Dr. Ahmad K. Elshennawy
Prof. Dr. Elizabeth Cudney
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 management
  • artificial intelligence
  • sustainability
  • sustainable implementation
  • supply chain 4.0
  • best practices
  • smart warehousing
  • organizational performance

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Published Papers (6 papers)

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Research

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30 pages, 2543 KiB  
Article
Sustainable Supply Chain Strategies for Modular-Integrated Construction Using a Hybrid Multi-Agent–Deep Learning Approach
by Ali Attajer, Boubakeur Mecheri, Imane Hadbi, Solomon N. Amoo and Anass Bouchnita
Sustainability 2025, 17(12), 5434; https://doi.org/10.3390/su17125434 - 12 Jun 2025
Viewed by 352
Abstract
Modular integrated construction (MiC) is a cutting-edge approach to construction that significantly improves efficiency and reduces project timelines by prefabricating entire building modules off-site. Despite the operational benefits of MiC, the carbon footprint of its extensive supply chain remains understudied. This study develops [...] Read more.
Modular integrated construction (MiC) is a cutting-edge approach to construction that significantly improves efficiency and reduces project timelines by prefabricating entire building modules off-site. Despite the operational benefits of MiC, the carbon footprint of its extensive supply chain remains understudied. This study develops a hybrid approach that combines multi-agent simulation (MAS) with deep learning to provide scenario-based estimations of CO2 emissions, costs, and schedule performance for MiC supply chain. First, we build an MAS model of the MiC supply chain in AnyLogic, representing suppliers, the prefabrication plant, road transport fleets, and the destination site as autonomous agents. Each agent incorporates activity data and emission factors specific to the process. This enables us to translate each movement, including prefabricated components of construction deliveries, module transfers, and module assembly, into kilograms of CO2 equivalent. We generate 23,000 scenarios for vehicle allocations using the multi-agent model and estimate three key performance indicators (KPIs): cumulative carbon footprint, logistics cost, and project completion time. Then, we train artificial neural network and statistical regression machine learning algorithms to captures the non-linear interactions between fleet allocation decisions and project outcomes. Once trained, the models are used to determine optimal fleet allocation strategies that minimize the carbon footprint, the completion time, and the total cost. The approach can be readily adapted to different MiC configurations and can be extended to include supply chain, production, and assembly disruptions. Full article
(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)
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19 pages, 928 KiB  
Article
Enhancing Sustainable Global Supply Chain Performance: A Multi-Criteria Decision-Making-Based Approach to Industry 4.0 and AI Integration
by Dalia Štreimikienė, Ahmad Bathaei and Justas Streimikis
Sustainability 2025, 17(10), 4453; https://doi.org/10.3390/su17104453 - 14 May 2025
Cited by 1 | Viewed by 622
Abstract
The integration of Industry 4.0 and Artificial Intelligence (AI) technologies has redefined global supply chain operations, with increasing emphasis on sustainability as a strategic priority. Despite this evolution, there remains a significant gap in the literature regarding the structured prioritization of sustainability-related indicators [...] Read more.
The integration of Industry 4.0 and Artificial Intelligence (AI) technologies has redefined global supply chain operations, with increasing emphasis on sustainability as a strategic priority. Despite this evolution, there remains a significant gap in the literature regarding the structured prioritization of sustainability-related indicators influenced by digital transformation. This study addresses that gap by identifying and ranking key sustainability enablers across environmental, operational, strategic, and social dimensions using the Best–Worst Method (BWM), a robust multi-criteria decision-making (MCDM) technique. Based on expert input from 37 professionals in the fields of supply chain management, sustainability, and digital technologies, twenty indicators were evaluated within four separate thematic groups. Results reveal that Emissions Monitoring and Reduction and Energy Efficiency are the most critical in the environmental dimension, while Supply Chain Traceability and Smart Inventory Management dominate the operational category. Supply Chain Resilience is identified as the top strategic factor, and Ethical Sourcing is deemed most vital from a social sustainability standpoint. These findings provide actionable insights for policymakers and practitioners, supporting data-driven decision-making and strategic alignment of digital investments with sustainability goals. This research contributes to both academic discourse and practical frameworks by offering a replicable approach to prioritizing sustainability indicators in the context of digital transformation. This study also identifies limitations and proposes future research directions to enhance the integration of digital and sustainable development in global supply chains. Full article
(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)
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29 pages, 1902 KiB  
Article
Quality Models for Preventing the Impact of Supply Chain Disruptions in Future Crises
by Miroslav Drljača, Saša Petar, Grace D. Brannan and Igor Štimac
Sustainability 2025, 17(8), 3293; https://doi.org/10.3390/su17083293 - 8 Apr 2025
Viewed by 618
Abstract
Supply chains, which have numerous participants, are exposed and vulnerable. In recent years, this has been evident in disruptions caused by circumstances that have changed the context, such as (1) the COVID-19 pandemic, (2) the Suez Canal blockade, and (3) the war in [...] Read more.
Supply chains, which have numerous participants, are exposed and vulnerable. In recent years, this has been evident in disruptions caused by circumstances that have changed the context, such as (1) the COVID-19 pandemic, (2) the Suez Canal blockade, and (3) the war in Ukraine. These circumstances caused disruptions in supply chains and surprised numerous participants in the international market, individual organizations, as well as states and entities around the world. This caused confusion and large financial losses for numerous global market participants and for people all around the world. The purpose of this paper is to design three original models, the implementation of which should significantly reduce the damage caused by disruptions in supply chains in future crises: (1) a model for individual organizations, (2) a national economy model, and (3) a global model. The authors applied methods of scientific cognition and analyzed three case studies from the recent past. The key finding is that by applying the models with four components (methods, measures, quality tools, and indicators), the resilience of supply chains increases the damage from disruptions in supply chains during future crises can be significantly reduced, and the quality of life of everyone on the planet will be less threatened. Full article
(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)
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19 pages, 2149 KiB  
Article
Determinants of Design with Multilayer Perceptron Neural Networks: A Comparison with Logistic Regression
by Amirhossein Ostovar, Danial Davani Davari and Maciej Dzikuć
Sustainability 2025, 17(6), 2611; https://doi.org/10.3390/su17062611 - 16 Mar 2025
Cited by 2 | Viewed by 822
Abstract
This research focuses on harnessing artificial neural networks (ANNs) to enhance the design of steel structures. The design process encompasses various stages, including defining the building’s geometry, estimating loads, selecting an appropriate structural system, sizing components, and creating detailed plans. Optimizing the weight [...] Read more.
This research focuses on harnessing artificial neural networks (ANNs) to enhance the design of steel structures. The design process encompasses various stages, including defining the building’s geometry, estimating loads, selecting an appropriate structural system, sizing components, and creating detailed plans. Optimizing the weight of these structures is vital for reducing costs, improving efficiency, and minimizing environmental impact. This study specifically investigates multilayer perceptron (MLP) neural networks to optimize steel structure design. It evaluates different ANN configurations with varying numbers of hidden layers and neurons to find the most effective arrangement. Additionally, the performance of MLP networks is compared to that of logistic regression. The results demonstrate that MLP networks deliver superior accuracy in optimizing the design of steel structures compared to logistic regression. The process of designing steel structures at an early stage can reduce the consumption of energy and raw materials before the production of the structures themselves begins. This is important from an economic point of view because some costs can be reduced during the design process. When designing steel structures, it is also possible to take into account changing conditions, such as the growing share of renewable energy sources in the total energy balance in many countries. Full article
(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)
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20 pages, 292 KiB  
Article
Readiness for Industry 4.0 in a Medical Device Manufacturer as an Enabler for Sustainability, a Case Study
by Olivia McDermott, Dudley Luke Stam, Susana Duarte and Michael Sony
Sustainability 2025, 17(1), 357; https://doi.org/10.3390/su17010357 - 6 Jan 2025
Viewed by 1561
Abstract
This research aims to determine the state of Industry 4.0 readiness and to identify the best practices, challenges, and barriers to implementing Industry 4.0 technology in a medical device manufacturer, thus aiding in improving sustainability. Semi-structured interviews were completed with 12 senior executives [...] Read more.
This research aims to determine the state of Industry 4.0 readiness and to identify the best practices, challenges, and barriers to implementing Industry 4.0 technology in a medical device manufacturer, thus aiding in improving sustainability. Semi-structured interviews were completed with 12 senior executives representing a wide array of functions in a single large medical device manufacturer. Convenience sampling was used to analyse the interview transcripts to draw out themes that were then discussed and analysed with findings from the literature review. This research determined the state of Industry 4.0 readiness in the case study of medical device manufacturers. This research identified several best practices, challenges, and barriers to implementing Industry 4.0 technology. Currently, there are few case studies in the literature that have a medical device manufacturer as the case study for Industry 4.0 readiness. There are even fewer articles that tackle Industry 4.0 implementation across the entire medical device industry. There is currently no published literature that analyses the best practices for implementing Industry 4.0 in a medical device manufacturer. The best practices for Industry 4.0 implementation identified in this study can be beneficial to stakeholders in the medical device industry and within the healthcare sector, help them plan current and future Industry 4.0 programmes, improve sustainability in their companies, as well as optimise patient treatment and approaches. Full article
(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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18 pages, 2113 KiB  
Review
Digital Transformation of Healthcare Enterprises in the Era of Disruptions—A Structured Literature Review
by Gaganpreet Singh Hundal, Donna Rhodes and Chad Laux
Sustainability 2025, 17(13), 5690; https://doi.org/10.3390/su17135690 - 20 Jun 2025
Viewed by 207
Abstract
Digital transformation is the process of using digital technologies for creating or modifying existing business processes and customer experience, leveraging cutting-edge technology to meet changing market needs. Disruptions like the COVID-19 pandemic, regional wars, and climate-driven natural disasters create consequential scenarios, e.g., global [...] Read more.
Digital transformation is the process of using digital technologies for creating or modifying existing business processes and customer experience, leveraging cutting-edge technology to meet changing market needs. Disruptions like the COVID-19 pandemic, regional wars, and climate-driven natural disasters create consequential scenarios, e.g., global supply chain disruption creating further demand–supply mismatch for healthcare enterprises. According to KPMG’s 2021 Healthcare CEO Future Pulse, 97% of healthcare leaders reported that COVID-19 significantly accelerated the digital transformation agenda. Successful digital transformation initiatives, for example, digital twins for supply chains, augmented reality, the IoT, and cybersecurity technology initiatives implemented significantly enhanced resiliency in supply chain and manufacturing operations. However, according to another study conducted by Mckinsey & Company, 70% of digital transformation efforts for healthcare enterprises fail to meet their goals. Healthcare enterprises face unique challenges, such as complex regulatory environments, cultural resistance, workforce IT skills, and the need for data interoperability, which make digital transformation a challenging project. Therefore, this study explored potential barriers, enablers, disruption scenarios, and digital transformation use cases for healthcare enterprises. A structured literature review (SLR), followed by thematic content analysis, was conducted to inform the research objectives. A sample of sixty (n = 60) peer-reviewed journal articles were analyzed using research screening criteria and keywords aligned with research objectives. The key themes for digital transformation use cases identified in this study included information processing capability, workforce enablement, operational efficiency, and supply chain resilience. Collaborative leadership as a change agent, collaboration between information technology (IT) and operational technology (OT), and effective change management were identified as the key enablers for digital transformation of healthcare enterprises. This study will inform digital transformation leaders, researchers, and healthcare enterprises in the development of enterprise-level proactive strategies, business use cases, and roadmaps for digital transformation. Full article
(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)
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