Sustainability

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26 pages, 3830 KB

Open AccessArticle

Responsible Supply Chains Through ESG Factors and Transformative Trajectories

by Pietro De Giovanni

Sustainability 2026, 18(7), 3344; https://doi.org/10.3390/su18073344 - 30 Mar 2026

Cited by 1 | Viewed by 1066 | Correction

Abstract

This study examines the Environmental, Social, and Governance (ESG) factors to be used in guiding suppliers and creating responsible supply chains. By adopting an integrated mixed-method approach (Delphi in conjunction with the survey method), this research determines the ESG factors prioritized by companies [...] Read more.

This study examines the Environmental, Social, and Governance (ESG) factors to be used in guiding suppliers and creating responsible supply chains. By adopting an integrated mixed-method approach (Delphi in conjunction with the survey method), this research determines the ESG factors prioritized by companies in relation to the assessment, qualification, and tendering of suppliers. We discover that the adaptation of ESG factors follows an unorganized and heterogenous process, resulting in complex adoption. Finally, this study searches for an evolutionary set of trajectories that suppliers can undertake to evolve according to their ESG maturity and their importance in the supply chains, revealing the operational plans necessary for suppliers to become Champions. Full article

(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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27 pages, 6092 KB

Open AccessArticle

Optimization of the Urban Food-Energy-Water Nexus: A Micro-Supply Chain and Circular Economy Approach

by Marwen Elkamel and Luis Rabelo

Sustainability 2026, 18(6), 2751; https://doi.org/10.3390/su18062751 - 11 Mar 2026

Viewed by 627

Abstract

This paper presents a mathematical programming model to optimize the design and sustainability performance of the urban food–energy–water (FEW) nexus. The model incorporates a micro supply chain and addresses the supply-demand balance within existing and future FEW systems using performance indicators such as [...] Read more.

This paper presents a mathematical programming model to optimize the design and sustainability performance of the urban food–energy–water (FEW) nexus. The model incorporates a micro supply chain and addresses the supply-demand balance within existing and future FEW systems using performance indicators such as cost and carbon footprint. The problem allows for optimal discrete choices, such as investment in new assets, as well as continuous choices, including capacity of different units and produce exchange among urban farms. The model is applied to an urban agriculture network in South Florida that integrates renewable energy technologies (solar, wind, biomass), combined heat and power (CHP) units, reclaimed wastewater and stormwater for irrigation, and electric vehicles for produce transport. The optimization process identifies the most effective infrastructure investment decisions, resource allocation, and technology configurations to support circular economy practices and long-term sustainability objectives. The proposed framework enables reductions in carbon footprints, food waste, and improves food accessibility in food deserts and strengthens collaboration among urban farms. It supports the planning of resilient urban FEW systems by aligning resource use with social, economic and environmental sustainability objectives. The results provide a decision-support tool for urban planners and policymakers, offering practical insights to guide infrastructure investment and sustainability planning in other geographic regions. Full article

(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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31 pages, 3482 KB

Open AccessArticle

Toward Sustainable Supply Chains in Metal Additive Manufacturing: An Energy-Based Limited-Scope Global Warming Potential (GWP) Life Cycle Assessment of Oxygen-Free High Conductivity Copper Powder Production

by Michael Sherwin, John Barnes and Eric Rhodes

Sustainability 2026, 18(3), 1356; https://doi.org/10.3390/su18031356 - 29 Jan 2026

Cited by 2 | Viewed by 967

Abstract

Powder metallurgy processes manufacture products from metal powders, which can be produced using various methods. When customer requirements permit, powder metal processes can produce products in an additive rather than a subtractive fashion. Thus, this approach reduces the waste associated with traditional subtractive [...] Read more.

Powder metallurgy processes manufacture products from metal powders, which can be produced using various methods. When customer requirements permit, powder metal processes can produce products in an additive rather than a subtractive fashion. Thus, this approach reduces the waste associated with traditional subtractive metallurgical forming processes such as machining. In addition to lowering material waste, enhancing design flexibility, and improving process efficiency, additive manufacturing of powder metallurgy products can also reduce environmental impact by reducing energy consumption, raw material use, emissions, transportation, and waste generation. Furthermore, the use of alternative methods for manufacturing metal powders can further reduce environmental impact. In this study, an energy-based limited-scope global warming potential life cycle assessment is presented that compares the carbon intensities of manufacturing critical products made of oxygen-free high-conductivity copper powder via two different powder production routes: electrode induction melting gas atomization, and the DirectPowder^TM System, within additive manufacturing supply chains. Instead of relying on single-point estimates, this study uses a Monte Carlo simulation to account for uncertainty and variation in input data. Results indicated that the DirectPowder^TM manufacturing pathway had a 39.4% lower global warming potential per kg of usable powder when parts were manufactured via laser powder bed fusion. When only the powder manufacturing methods were included in the analysis, the DirectPowder^TM method demonstrated the potential to reduce global warming impact by 92.9% when compared to the electrode induction melting gas atomization process. In total, 11.44 kg CO₂-eq per kg of OFHC copper produced is saved when using the DirectPowder^TM process. This research provides new insights into the tradeoffs between the environmental impact and functional capabilities of these methods. It offers valuable guidance on process selection for product designers and supply chain professionals seeking to optimize product performance, energy use, and environmental footprint. Full article

(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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23 pages, 2798 KB

Open AccessArticle

Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production

by Zhe Wee Ng, Biswajit Debnath and Amit K Chattopadhyay

Sustainability 2025, 17(19), 8848; https://doi.org/10.3390/su17198848 - 2 Oct 2025

Cited by 2 | Viewed by 1585

Abstract

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) [...] Read more.

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management. Full article

(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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27 pages, 1448 KB

Open AccessArticle

Why Nobody Measures the Scope 4 (Avoided) Emissions? Let’s Get It Started!

by Pietro De Giovanni

Sustainability 2025, 17(18), 8317; https://doi.org/10.3390/su17188317 - 16 Sep 2025

Cited by 4 | Viewed by 2780

Abstract

As the urgency of climate action intensifies, organizations are increasingly required not only to reduce their instantaneous internal emissions (Scopes 1 and 2) and their value chain impacts (Scope 3), but also to demonstrate their overall contribution to climate change. Therefore, this paper [...] Read more.

As the urgency of climate action intensifies, organizations are increasingly required not only to reduce their instantaneous internal emissions (Scopes 1 and 2) and their value chain impacts (Scope 3), but also to demonstrate their overall contribution to climate change. Therefore, this paper introduces and formalizes the concept of Scope 4 emissions, defined as avoided emissions enabled by a company’s products, services, or business models, representing the fourth strategic pillar in corporate climate accounting. The paper proposes how to quantify the Scope 4 emissions through the decarbonization plan, using a stadium decarbonization plan as an illustrative example to show how Scope 1–3 reductions can be complemented by cumulative Scope 4 impacts, the advantages of undertaking proactive approaches toward sustainability and proposing the concept of relative carbon neutrality. Finally, the paper connects the Scope 4 emissions with ESG factors, highlights possible risks and challenges associated with its computation, and inviting regulators and policy makers to devise new Scope 4-based policies and incentives needed when considering the directives’ dynamics (e.g., the Omnibus Package). Full article

(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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30 pages, 2543 KB

Open AccessArticle

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

Cited by 5 | Viewed by 3353

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 KB

Open AccessArticle

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 12 | Viewed by 5281

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 KB

Open AccessArticle

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

Cited by 2 | Viewed by 4059

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 KB

Open AccessArticle

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 37 | Viewed by 3530

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 KB

Open AccessArticle

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

Cited by 4 | Viewed by 4953

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 KB

Open AccessReview

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

Cited by 8 | Viewed by 6700

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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Other

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28 pages, 1530 KB

Open AccessSystematic Review

Leveraging AI to Build Agile and Resilient Healthcare Supply Chains for Sustainable Performance: A Systematic Scoping Review and Future Directions

by Senthilkumar Thiyagarajan, Elizabeth A. Cudney, Pranay Chimmani, Lionel Henry D’silva and Chad M. Laux

Sustainability 2026, 18(3), 1434; https://doi.org/10.3390/su18031434 - 1 Feb 2026

Cited by 1 | Viewed by 2131

Abstract

Ongoing global disruptions, including pandemics, geopolitical tensions, and climate-driven events, have exposed vulnerabilities in healthcare supply chains (HSCs). This study examines how artificial intelligence (AI) is reshaping HSCs to improve agility, resilience, and sustainable performance. Using a systematic literature review with PRISMA-style screening [...] Read more.

Ongoing global disruptions, including pandemics, geopolitical tensions, and climate-driven events, have exposed vulnerabilities in healthcare supply chains (HSCs). This study examines how artificial intelligence (AI) is reshaping HSCs to improve agility, resilience, and sustainable performance. Using a systematic literature review with PRISMA-style screening across Scopus and Web of Science, the study is complemented by bibliometric analysis and latent Dirichlet allocation topic modeling to analyze peer-reviewed articles. The results indicate an exponential increase in AI-enabled HSC research, concentrated in a small number of journals and spanning a globally diverse author community. Three dominant thematic clusters emerged: (1) sustainability-oriented supply chain design, (2) disruption and resilience management, and (3) healthcare-focused digital transformation. Across these themes, AI, digital twins, Internet of Things, and simulation are evolving from efficiency tools to strategic enablers of decision intelligence, supporting real-time sensing, scenario analysis, and proactive risk mitigation. The study highlights a convergence of “triple transformation” in which digitalization, resilience, and sustainability are increasingly co-dependent capabilities in HSCs. However, persistent barriers exist, including data quality issues, legacy systems, workforce skill gaps, limited model interpretability, and incomplete governance frameworks, which constrain large-scale adoption. The findings indicate a need for longitudinal and multi-method studies on human–AI collaboration, trust calibration, and leadership in AI-enabled HSCs. This study provides practical guidance for healthcare organizations looking to leverage AI in developing agile, resilient, and sustainable supply chain ecosystems. Full article

(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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17 pages, 2373 KB

Open AccessSystematic Review

Sustainable Supply Chains in the Forest Bioeconomy: A Systematic Review

by Hamish van der Ven and Kodiak Bear

Sustainability 2025, 17(21), 9738; https://doi.org/10.3390/su17219738 - 31 Oct 2025

Cited by 2 | Viewed by 1481

Abstract

The forest bioeconomy is an emerging global sector that uses forest material to make value-added bioproducts that range from pharmaceuticals to biofuels. Notwithstanding their capacity to advance various United Nations Sustainable Development Goals, forest bioproducts face considerable sustainability challenges in global supply chains [...] Read more.

The forest bioeconomy is an emerging global sector that uses forest material to make value-added bioproducts that range from pharmaceuticals to biofuels. Notwithstanding their capacity to advance various United Nations Sustainable Development Goals, forest bioproducts face considerable sustainability challenges in global supply chains associated with harvesting, processing, and transportation. Using a systematic literature review focused on challenges and solutions to sustainability in forest bioeconomy supply chains, we analyze 81 peer-reviewed studies to identify the primary sustainability challenges and their attendant solutions. We find that economic barriers to scaling the forest bioeconomy are the most commonly studied challenge, while social and environmental challenges are often marginalized. Increasing stakeholder engagement is the most commonly mentioned solution, but the limitations of stakeholder engagement are largely absent from scholarly discourse. Lastly, we identify significant gaps in the literature related to coverage of non-European countries and analysis of key sectors like mass timber construction. The results gesture to the need for more research on under-represented regions and sectors, greater attention to social and environmental supply chain challenges, and deeper engagement with adjacent literatures on the intersection of public policy with sustainable supply chain governance. Full article

(This article belongs to the Special Issue Global Supply Chain Management for Sustainable Organizational Performance)

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