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Background:
Systematic Review

Exploring the Interplay Between Green Practices, Resilience, and Viability in Supply Chains: A Systematic Literature Review

by
Hamza Chajae
1,
Moulay Ali El Oualidi
1,
Ali Hebaz
2,* and
Hasna Mharzi
2
1
Laboratory of Advanced Research in Industrial and Logistic Engineering (LARILE), École Nationale Supérieure d’Électricité et Mécanique, Hassan II University of Casablanca, Boulevard Abdellah Ibrahim (Ex. Route d’El Jadida), Casablanca 20000, Morocco
2
Multidisciplinary Research Laboratory in Social Engineering and Business Management (PRISME), École Nationale Supérieure d’Électricité et Mécanique, Hassan II University of Casablanca, Boulevard Abdellah Ibrahim (Ex. Route d’El Jadida), Casablanca 20000, Morocco
*
Author to whom correspondence should be addressed.
Logistics 2026, 10(1), 23; https://doi.org/10.3390/logistics10010023
Submission received: 30 October 2025 / Revised: 9 January 2026 / Accepted: 9 January 2026 / Published: 16 January 2026
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Abstract

Background: In this new era, marked by increasing environmental concerns, geopolitical crises, and global disruptions, traditional efficiency-focused supply chains have shown significant vulnerabilities. As a result, the shift toward new strategies to maintain sustainability has become more crucial. Meanwhile, to withstand disruptions, supply chains must develop robustness and resilience. More recently, attention has turned toward viability to enable sustainable supply chain operations over the long term under uncertainty. Methods: This study conducts a systematic literature review (SLR) to explore the links between green supply chain management (GSCM), supply chain resilience (SCRES), and supply chain viability (SCV), guided by the PRISMA framework and structured using the PICO approach as a high-level scoping tool. We reviewed 70 peer-reviewed journal articles published between 2010 and 2024. Result: The study identified widely adopted green practices and explored their impact on supply chain resilience and sustainable performance. Many studies address GSCM, SCRES, and SCV either separately or in pairs, but few integrate all three dimensions. GSCM fosters resilience, and when the three aspects are combined, they serve as the cornerstones of viable supply chains. However, their potential contribution to supply chain viability is still unexplored. Conclusions: These insights provide useful guidance for creating supply chains that balance long-term continuity, disruption-readiness, and environmental goals. They also suggest a future research agenda to better align these three priorities.

1. Introduction

Global supply chains are confronting an increasing number of obstacles. These disruptions have revealed the shortcomings and vulnerabilities of traditional supply chains that rely solely on cost minimization and efficiency optimization [1]. At the same time, with the growing concern for ecology, most supply chains are now reorienting toward a sustainable approach to remain competitive and resilient.
As a logical response, green supply chain practices (GSCPs) have emerged as a highly strategic priority. These practices include green purchasing, eco-design, recycling, reverse logistics, sustainable transportation, and others [2,3].
However, adopting green supply chain practices does not automatically lead to a resilient supply chain. Modern supply chains must be prepared to anticipate, absorb, adapt, and recover from disruptions [4]. Resilience remains oriented towards short-term response and recovery. Besides resilience, viability expands upon this viewpoint by emphasizing long-term adaptation, sustained performance, and the capacity of supply chains to function under protracted uncertainty. As a result, this analysis treats GSCM, resilience, and viability as interrelated aspects rather than as separate concerns. It focuses on how green practices strengthen resilience and how their combined impact promotes long-term supply chain viability. The concept of supply chain viability is further explained in the following section, along with how it fits into the larger performance framework.
Many studies have shown that certain green practices (e.g., dual sourcing and safety stock) could have multiple impacts—environmental, operational, and beyond [4,5]. However, only a few have integrated both GSCM and resilience simultaneously, and even fewer have included the concept of viability. There is a lack of systematic literature reviews that address how green supply chain practices contribute to resilience and how their integration supports the development of a long-term and viable supply chain [6,7,8]. This progression—from sustainability issues to the necessity of resilience to ultimately the imperative of long-term viability—provides the narrative foundation of this study and directly leads to the research gap addressed in the following section.

1.1. Conceptual Framework of the Study

This framework considers supply chain viability (SCV) as an emergent capability resulting from the simultaneous collaboration and co-evolution of green supply chain management (GSCM) and supply chain resilience (SCRES). It is conceptualized as a high-level systemic capability that is qualitatively and analytically different from robustness and resilience. Green practices such as clean production, eco-design, green purchasing, collaborative governance, and reverse logistics establish sustainable foundations by embedding agility, flexibility, redundancy, and learning, which enable resilience capabilities. In turn, resilience principles continuously reconfigure and tangibly reinforce green practices, encompassing both short-term recovery and a long-term vision for transformation.
The agility–resilience–sustainability (ARS) triad, introduced in Ivanov’s work from 2020 to 2023, conceptualizes the dynamic skills needed for supply chains to react, adapt, and change in the face of unpredictability. Where sustainability fosters long-term environmental and social performance, resilience ensures operational continuity, and agility enables quick responses. Viability arises at the nexus of agility, resilience, and sustainability. Here, the ARS provides the conceptual basis to relate GSCM to sustainability, SRES to resilience, and their joint contribution to viability. This framework is supplemented and reinforced by cross-cutting enablers, including digitalization, circular economy techniques, and modeling/optimization tools, which help to harmonize these interactions, as illustrated in Figure 1.

1.2. Scope of the Review

The study specifically focuses on the combination of green supply chain management (GSCM), supply chain resilience (SCRES), and supply chain viability (SCV) to define the parameters of this review. The review examines how green practices support resilience, how resilience facilitates the establishment of viability, and how these three characteristics combine to create adaptive and sustainable supply chains. Furthermore, to provide a quantitative perspective on the trade-offs and synergies between sustainability, resilience, and long-term viability, the paper looks at the optimization and modeling techniques used to operationalize this integration, including multi-objective, stochastic, and data-driven models.

1.3. Objective of the Systematic Literature Review

The objective of this systematic literature review is to assess the improvement of the supply chain in a resilient, sustainable, and long-term viable way. This is achieved by identifying key trade-offs, strategies, and measurable outcomes in scientific publications that follow PRISMA guidelines, aiming to synthesize existing knowledge and identify research gaps across GSCM, resilience, and viability.

1.4. Research Gap and Rationale

Despite the numerous studies conducted in this field, the literature still lacks many research perspectives. Limitations persist, motivating further investigation. Our review aims to address these gaps.
Given that green supply chain management (GSCM), supply chain resilience (SCRES), and supply chain viability (SCV) have largely been reviewed fragmentarily, further reviews should focus on studying the modeling and optimization approaches that operationalize these conceptual frameworks into quantitative and measurable forms. This would enable the evaluation of the trade-offs between cost optimization, gas emissions, and service quality while identifying synergies among circularity, efficiency, and resilience. Currently, studies that integrate all three dimensions in a combined optimization approach remain rare. Table 1 provides a concise summary of the most pertinent current review research on GSCM, SCRES, and SCV, which can prove this point of view rather than merely stating the gap. This comparison mapping demonstrates that prior evaluations have either only partially or separately addressed these ideas; none have integrated the three dimensions into a single framework or investigated their relationship through modeling or optimization. This makes it abundantly evident that an integrated tridimensional review is still lacking, which supports the uniqueness and importance of the current investigation.
This systematic literature review, therefore, fills this gap by exploring the interaction among these three dimensions and examining how their combined contribution supports sustainable and adaptive supply chains. It also provides an integrative synthesis of these pillars, offering a strong foundation for a future research agenda that can support empirical studies.

1.5. Research Questions

  • What are the most cited and widely adopted green supply chain (GSCM) practices identified in the literature?
  • To what extent do these green supply chain practices have an impact on SCRES?
  • Does the integration of GSCM and resilience contribute to supply chain viability?
  • What trade-offs or synergies are identified among resilience, viability, and green supply chains?
  • What optimization or modeling approaches are used to solve GSCM with resilience and viability problems?

2. Methodology

2.1. Review Approach

In this paper, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 framework to conduct the systematic literature review, ensuring high transparency and academic rigor in the selection and analysis of relevant articles [17]. The data extraction and coding were carried out using Excel for bibliometric recording and comparisons, and MAXQDA was used for qualitative thematic analysis. The study was also guided by the PICO (Population, Intervention, Comparison, Outcome) framework.

2.2. PICO Framework

Although the PICO framework was originally used in medical research, it has become widely adopted in management and operational reviews as a pertinent tool for defining the scope of systematic reviews. In this study, PICO was used to allow delimitations of the context (population), the practices studied (intervention), reference conditions (comparison), and finally the performance outcome (outcome). This structure, based on the analysis and comparison of current practices, provides a direct way to ensure a rigorous synthesis of the studies, thereby fulfilling the objectives of this review. Frameworks such as TCCM and SPIDER are better suited for mapping theories and exploratory qualitative research; they do not allow for the systematic extraction of comparative elements and measurable outcomes in the same way that PICO does. The application of the PICO framework to the scope and objectives of this review is detailed in Table 2.

2.3. Search Strategy and Data Sources

The literature search was conducted using three essential academic databases: Scopus, Web of Science, and ScienceDirect, using combinations of keywords linked by Boolean operators, and we applied filters to refine relevance. The choice of these databases was made based on their inclusion of the most high-impact journals in supply chain management, logistics, and sustainability, ensuring pertinent coverage of this review. The risk of missing relevant studies from databases such as Emerald, IEE, and SpringerLink remains minimal, as they largely overlap.
  • Keywords used
  • Scopus: (“green supply chain” OR “sustainable supply chain”) AND “resilience” AND (“viability” OR “sustainability”) AND (“optimization” OR “modeling”)
  • Web of Science: (“green supply chain” OR “sustainable supply chain”) AND resilience AND (optimization OR modelling OR “mathematical model” OR “decision model”)
  • ScienceDirect: “Green supply chain” AND “resilience” AND “viability” AND “optimization.”
  • Filters applied:
  • Publication date: 2010 to 2024
  • Languages: English and French
  • Document types: Peer-reviewed journal articles

2.3.1. Inclusion and Exclusion Criteria

  • Inclusion Criteria:
  • Articles that include at least one of the targeted keywords: GSCM, SCRES, SCV, or optimization;
  • Various types of studies: empirical, conceptual, or optimization-based;
  • Articles with structured methodology and clearly presented results;
  • Articles published in English or French between 2010 and 2024.
  • Exclusion Criteria:
  • Studies that address green practices, resilience, viability, or optimization but are unrelated to supply chain contexts;
  • Duplicate or inaccessible full texts;
  • Papers that are not within the selected time period;
  • Studies published in a language other than English or French.
Theses, gray literature, and conference papers were excluded to ensure methodological rigor and consistent peer-reviewed quality across the studied documents. Furthermore, to guarantee a reliable interpretation, the review was restricted to English and French publications, acknowledging that this may create a publication and linguistic bias.

2.3.2. Article Screening and Selection Process

After the identification phase, we collected a total of 700 records across three scientific databases: ScienceDirect (n = 376), Scopus (n = 88), and Web of Science (n = 236). Within these 700 papers, 56 were duplicates, and only 644 papers were retained for screening.
The screening was based on three main filters:
The first filter was applied to document type and source validation (200 papers excluded). Here, we performed an automatic exclusion on the database to remove papers from irrelevant disciplines (e.g., those not related to supply chain, such as energy, medicine, etc.) and non-academic ones, such as book chapters, notes, short surveys, etc.
The second filter was related to the thematic scope (345 papers excluded). Here, we removed all the papers that did not contain any of the main dimensions used for our research, either in the title or in the abstract, i.e., green supply chain (GSCM), resilience (SCRES), viability (SCV), or optimization/modeling.
We also removed papers that discussed these dimensions but in unrelated fields, such as energy or agriculture, without any direct relevance to supply chain management.
The third filter was related to paper accessibility. A total of 19 articles were excluded because of our inability to access the full text, even through institutional databases or direct links. Following a more refined screening, 10 additional articles were excluded due to insufficient focus and unavailable data. As a result, 70 articles were retained for full-text eligibility assessment and reviewed, as they met all the inclusion criteria.

2.4. Prisma Chart Flow

The study selection and screening process followed the PRISMA 2020 guidelines and is summarized in Figure 2.
Additional details related to the PRISMA selection process are provided in the Supplementary Materials.

2.5. Descriptive Analysis of Selected Articles

The 70 publications that were kept for full-text review underwent a descriptive bibliometric analysis as an adjunct to the PRISMA-based selection procedure. The primary publication sources, chronological distribution, and topic breadth of the chosen research are identified in this quantitative summary reported in Table 3.
The findings indicate that most of the research on supply chain viability (SCV), supply chain resilience (SCRES), and green supply chain management (GSCM) is published in high-impact (Q1) journals, particularly the Journal of Cleaner Production and Sustainability.
This shows a high level of academic maturity and awareness of the subject in the fields of operations management and sustainability. The publications’ temporal distribution (Table 4 and Figure 3) demonstrates a steady increase in academic interest starting in 2018 and reaching a high in 2024 with 15 articles (21% of the total). This pattern emphasizes how supply chain research is becoming more concerned with including sustainability, resilience, and viability, especially in the wake of the COVID-19 pandemic. A combination of modeling, optimization, and conceptual research is included in Table 5, which lists the evaluated articles along with their titles and publishing outlets to demonstrate the range and variety of contributions. A rigorous bibliometric analysis was performed regarding geographical, methodological, thematic, and journal quality dimensions. The methodological distribution shows 45% of the studies apply quantitative empirical methods (PLS/SEM, regression); 35% of the studies apply mathematical modeling, optimization, and simulation; and 20% are reviews, conceptual studies, and qualitative analyses. The geographical dispersion is as follows: around 50% of the studies were conducted in Asia (China, Pakistan, India, etc.), 24% in Europe (Germany, France, Denmark, Spain, etc.), 18% in America (USA, Canada), and 8% in Africa. Regarding the quality of journals, 70% of the studied journals are Q1, 20% are Q2, and 10% are Q3 and Q4.
The variety of publications and subjects illustrates the field’s multidisciplinary structure and provides a strong basis for the sections that follow, which address in detail the green supply chain management techniques.

2.6. Qualitative Analysis Approach

After the PRISMA-based selection procedure was completed, the results from 70 full-text articles were compiled using a qualitative thematic content analysis. Each paper was examined according to the identified categories—green supply chain management (GSCM) practices, supply chain resilience (SCRES) capabilities, and supply chain viability (SCV) enablers—using a hybrid approach that combined deductive and inductive coding. Essential concepts, connections, and processes were manually coded and then iteratively improved by comparing different studies. The synthesis was then organized by grouping the codes into higher-order themes. To guarantee stability and minimize subjectivity, a random subset of articles was reviewed to confirm coding consistency. An outside supply chain management specialist examined the coding framework and thematic categories to guarantee dependability. The expert evaluated the consistency between themes and the retrieved data, the coherence of the codes, and the clarity of the definitions. The legitimacy and robustness of the qualitative synthesis were strengthened by this process of expert evaluation. Each article’s methodology, main conclusions, conceptual axis (GSCM, SCRES, and SCV), and thematic focus were all recorded in a comprehensive coding matrix; the core research themes are summarized in Table 6. The double coding check provided a high observed agreement (P0 = 0.82) and Cohen’s kappa indicator K = 0.71, which indicates substantial inter-rater agreement and proves the robustness of the coding scheme used in this systematic review.

3. Green Supply Chain Management (GSCM) Practices

3.1. Introduction

With the increasing regulatory, societal, and institutional pressures to reduce environmental impacts while maintaining competitiveness, green supply chain management has become an important research field. GSCM is not only about eco-friendly manufacturing; it regroups a system of practices that integrates sustainability throughout the supply chain, from upstream suppliers to end customers, including reverse flows [30,61]. Moreover, the GSCM is considered a strategic lever that enhances operational efficiency and resilience and leads to long-term viability [37,41]. The literature has revealed many green practices that will be detailed in the coming section.

3.2. Core Green Practices

3.2.1. Internal Environmental Management (IEM)

Internal environmental management (IEM) encompasses environmental goals, audits, certifications (e.g., ISO 14001), and employee training. It serves as the basis of GSCM. IEM helps firms enhance their permanent integration of sustainability, making it a key factor that pushes green practices to occur. IEM is enabled by the top management commitment, which provides the necessary initiatives and promotes tangible actions such as investing in an environmental management system and training employees [30,41]. Studies show that enterprises with a structured IEM reach a high environmental and operational performance by aligning their organizational framework with sustainability [30].

3.2.2. Green Purchasing (GP)

Green purchasing (GP) is the first filter for firms to opt for sustainability. It considers environmental concerns starting from supplier selection and procurement methods. This selection is conditioned by criteria such as eco-certification, non-toxic inputs, and recycled items. GP obliges suppliers to provide sustainable raw materials; otherwise, only providers fulfilling this fundamental condition can be accepted. Studies indicate that GP greatly improves environmental performance; however, economic performance remains unpredictable in the short and medium term and mostly depends on influencing factors such as institutional pressure, customer requirements, and the mediation of innovation [37,61]. In some cases, GP realizes considerable cost savings, while in others, there could be increasing procurement costs, especially when sustainable procurement is not widely available [62].

3.2.3. Eco-Design (ED)

Eco-design (ED), also called design for the environment, aims to reduce the environmental impact during the product life cycle by enhancing reuse and recycling, eco-friendly conception of products (starting from material sourcing through manufacturing, use, and end-of-life), optimizing resource use, and lengthening products’ lifetime [30]. Some of the most common principles are the 6Rs by Delaney E: reduce, reuse, recycle, recover, redesign, and remanufacture [48].
ED has shown strong environmental performance development, in contrast to its economic performance; due to the high redesign and investment costs, economic outcomes are often delayed and cannot be reached in the short term [55]. Furthermore, the operational flexibility promoted by eco-design allows firms to adapt to regulatory and customer requirements in terms of sustainable products.

3.2.4. Green Manufacturing (GM)

Green manufacturing (GM) refers to a sustainable production system that integrates clean technologies, waste minimization, and energy efficiency programs. Although GM is often associated with lean manufacturing, it does not represent the same concept. When combined, lean and green approaches significantly enhance cost minimization, efficiency, quality, and environmental performance [14]. GM reduces emissions and resource use costs, thereby improving sustainable operational outcomes. However, the economic performance can be observed mainly in the medium and long term due to the high investment costs required [61].

3.2.5. Green Logistics and Warehousing

Green logistics aims to adopt practices such as route optimization, eco-friendly packaging, waste minimization, and shipment consolidation. In contrast, green warehousing is a managerial concept that seeks to be more energy-efficient and environmentally friendly in the long term, as warehousing is considered a major emission contributor (lighting, material handling, etc.) [68]. Implementing green logistics/warehousing reduces emissions and resource consumption and supports sustainability policies. Conversely, economic performance can suffer in the short term and improve later; to achieve cost-effectiveness, firms should opt for economies of scale and supportive policies [68]. RL reduces waste in the perishable product supply chain by creating a closed-loop flow [69].

3.2.6. Reverse Logistics (RL) and Investment Recovery (IR)

Reverse logistics (RL) is widely acknowledged as a fundamental enabler of green supply chain management (GSCM), as it facilitates the return, reuse, and recycling of products and mitigates environmental degradation. Empirical evidence in emerging economies demonstrates that regulatory pressure and customer requirements drive the adoption of RL rather than voluntary corporate strategies [55]. This suggests that RL is more often considered a proactive regulatory strategy than a forward-thinking component of corporate sustainability. Investment recovery (IR), by contrast, aims to recapture value from surplus assets, recycle, create new revenue by reselling used products, or reuse them internally. In GSCM, investment recovery practices are among the strongest economic and environmental boosters. Evidence from large-scale studies shows that IR enhances sustainable performance across economic, environmental, and social dimensions [41].

3.2.7. Collaboration, Digitalization, and Human Capital as Enablers of GSCM

Green supply chain management (GSCM) requires a high level of cooperation and relational governance. Transparency, reciprocity, and shared norms facilitate information sharing, cooperative problem solving, and co-innovation [36,50]. Empirical evidence by W. Ahmed et al. demonstrates that both environmental and economic outcomes are significantly improved when firms engage in collaborative practices with suppliers, customers, and regulators, particularly through the accelerated diffusion of green technologies [50]. According to a different viewpoint, digitalization has emerged as a key facilitator of GSCM, with the development of technologies like artificial intelligence (AI), big data, and the internet of things improving monitoring and decision-making while encouraging eco-innovation [26,29]. Business competitiveness and environmental practices are linked through innovation, and GSCM adoption is transformed into measurable sustainability outcomes through the use of digital tools and green information exchange [7,61]. In addition to technological and relational techniques, green human resource management (GHRM) integrates environmental principles into organizational routines through training, incentives, and recruitment processes that are in line with sustainability goals [67]. According to additional studies, the long-term resilience and viability of supply chains are strengthened by supportive organizational cultures and sustainable leadership, which also increase employee involvement in green projects [67].

3.3. Performance Outcomes

The literature review reveals that green supply chain management techniques influence four major performance parameters, which are summarized in Table 7. Internal environmental management, eco-design, green production, and reverse logistics have a persistent positive impact on the environment by lowering and reducing emissions and waste [3,30,35,41]. Economic performance is more nuanced: practices such as reverse logistics and packaging optimization offer clear cost savings, whereas eco-design and training necessitate long-term investments with delayed returns [3,37,61].
Operational outcomes are significantly improved as a result of increased quality, system flexibility, and reliability, which is further enhanced by collaboration and digitalization [29,59,61].
Green HRM practices, the IEM, and stakeholder collaboration improve employee engagement, build trust, and foster shared purpose, organizational identity, and societal value [23,62,66,68]. Overall, the evidence indicates that GSCM outcomes are heterogeneous and diverse. Next, we investigate how sustainable practices help supply chain resilience, moving beyond performance effects (Section 4).

3.3.1. Impact of Green Supply Chain Management (GSCM) Practices on Performance

Based on the synthesis of the reviewed studies, recurring patterns and key contingencies associated with GSCM practices are summarized in Table 8.

3.3.2. Critical Integration of GSCM Results

The review highlights three main discrepancies in the research, despite GSCM methods being consistently linked to improvements in operational and environmental performance. First, most studies focus solely on GSCM, neglecting how it interacts with adaptive capacities. As a result, green practices are often seen as static operational choices rather than dynamic enablers of viability or resilience. Second, empirical findings remain inconsistent. While there is clear evidence of environmental benefits from eco-design, green purchasing, and reverse logistics, the economic outcomes are highly dependent on context. Cost trade-offs, operational constraints, and the time delays needed to realize benefits are often underreported. Third, the literature often overlooks reverse causality, institutional pressures, or capability mediators such as agility, digitization, or collaboration, instead assuming straightforward cause-and-effect relationships (e.g., GSCM → performance). Overall, the GSCM literature provides a solid descriptive foundation but lacks comprehensive explanations of how green practices enhance broader adaptive capacities like viability and resilience. This underscores the need for more in-depth exploration in Section 4 and Section 5.

4. Supply Chain Resilience (SCRES)

4.1. Conceptualization and Positioning

Supply chain resilience (SCRES) is seen as a critical paradigm in response to the current global environment turbulence. SCRES measures a supply chain’s ability to anticipate, absorb, adapt, and recover from shocks while constantly learning and reconfiguring to maintain sustainable operations. Contrary to robustness, which promotes static resistance, resilience enables dynamic reconfiguration [10,11,27]. Rather than being seen as opposite concepts, they are complementary: robustness provides stability under unexpected disruptions, although resilience ensures adaptive reconfiguration once the robustness threshold is passed. Supply chain resilience is broadly defined within a dynamic framework of four phases: proactive (anticipation), absorptive (reducing shocks and losses), adaptive (adjusting to new situations), and reactive (recovery). However, these four-phase cycles remain analytically criticized by many authors, as they provide a linear progression, whereas supply chain disruptions expand in a recursive and cascading way. In complex supply chain networks, the disruption’s propagation rarely follows in a linear, ordered way, which makes classical resilience incapable of explaining nonlinear dynamics such as the ripple effect or feedback loops.
Furthermore, the core resilience capabilities include agility, adaptability, collaboration, visibility, redundancy, flexibility, and recovery. On the other hand, the vulnerabilities of resilience include resource limitations, external shocks, and network complexity [10]. The traditional resilience framework has been widely criticized for focusing on short-term reactions, which may be insufficient in cases where the disruption persists or requires deeper structural adjustments. Recent literature shows that resilience encompasses a socio-ecological vision, enabling not only bouncing back but also bouncing forward through long-term reconfiguration and transformation rather than prioritizing a short-term recovery [1,33]. As this perspective closely links resilience with viability and sustainability, positioning it as a cornerstone of the agility–resilience–sustainability (ARS) triad [10]. This conceptual framework demonstrates that resilience alone cannot ensure long-term adaptability, highlighting the need for a more future-oriented extension of the concept.
However, the main issues with resilience are short-term reactions, recovery, and operational continuity, as illustrated through the following example. When a disruption occurs in the upstream of the supply chain, its impact can be covered by safety stock and backup sourcing, which may allow the supply chain to restore its usual performance level within a short-term perspective. However, where disruption persists for the long term, the resilience process becomes unsustainable, and in this case, the supply chain must reconfigure and redesign its network. This shift from short-term recovery to long-term adaptation involves the supply chain’s viability rather than resilience.
Viability builds on this perspective by focusing on long-term flexibility, sustained performance, and the ability of the supply chain to function under extended uncertainty. It extends resilience by emphasizing continuous reconfiguration, sustainable performance boundaries, and ensuring the ability to function under prolonged uncertainty. Therefore, viability can be thought of as a more thorough, future-focused development of resilience, as further integrated in Figure 4.

4.2. Intra- and Inter-Organizational Antecedents of Resilience

Resilience is influenced by both intra-organizational antecedents, which are leadership commitment, risk management, and human capital, and inter-organizational antecedents, which are visibility, relational governance, bargaining power, and network design. Resilience requires a proactive balance between both intra-/inter-organizational antecedents [27]. For example, holding strong bargaining power could create relational vulnerability and increase dependence between partners. While relational governance mechanisms—such as collaborative problem solving, trust, transparency, and information sharing—can moderate this discrepancy, they do so by mitigating opportunistic behavior and fostering collaborative adoption during the disruption periods, thereby ensuring a stabilized relationship between partners.
Beyond operational antecedents, financial evidence shows the strategic role of resilience, as many studies prove that supply chain disruption negatively impacts the stock market, highlighting that resilience is more than an organizational concern.
At the strategic level, resilience extends beyond operational efficiency to serve as a cornerstone for long-term competitiveness. SCRES is more than just a protective perspective; it is a long-term enabler throughout the supply chain network. It integrates risk management, enhances learning capabilities, and enables firms to transform disruptive environments into value creation. These antecedents do not work separately; they complement each other or, in some cases, exhibit conflicting interactions. In other words, their influence on resilience does not emerge from individual impact but from a combined effect. Thus, achieving resilience requires a clear proactive balance and a good alignment of internal capabilities with inter-organizational processes, ensuring that governance structure, flexibility, and visibility work mutually rather than operating in silos.

4.3. Dynamic Capabilities and Organizational Enablers

Unlike ordinary capabilities, dynamic capabilities remain essential for resilience. By balancing alignment and adaptability with agility acting as mediator, they establish organizational ambidexterity that enhances resilience [27]. These dynamic capabilities act like a fundamental mechanism promoting the use of sustainable leadership, CSR, and institutional pressure in collaboration and in an integrative way to improve learning, adaptation, and adaptive reconfiguration during disruption. Corporate social responsibility (CSR) is considered a key factor for resilience. Berger et al. [58] showed CSR enhanced SMEs’ rapid adaptation during COVID-19; in parallel, leadership alongside CSR has been widely confirmed as a strong enabler of resilience. Sustainable leadership and CSR are not dynamic capabilities themselves; instead, they shape the organizational culture and relationships that strengthen and activate dynamic capabilities. Suriyankietkaew et al. [67] identified a set of sustainable leadership competencies that strengthen resilience in community-based enterprises: people orientation, sustainable vision, innovation, and ethical values. SCRES is also shaped by institutional pressure and relational ecosystems. Studies show that customer collaboration significantly reinforces the performance outcomes, while supplier collaboration has a more limited impact but still enhances resilience [50]. The way CSR and leadership are expressed within an organization is influenced by institutional pressure, which is caused by defining the expectations and restrictions that govern the activation of dynamic capabilities. In addition, institutional pressures—coercive, normative, and mimetic—lead firms to adopt practices that integrate both resilience and sustainability [62].

4.4. Strategies for Resilience Across Disruption Phases

To understand the resilience strategies well, we must explore clearly the different phases of disruption [4,32]. As summarized in Figure 5:
-
Pre-disruption (Proactive): firms implement preventive measures to avoid disruptions and reduce vulnerability. Strategic actions that can enhance resilience include multiple sourcing, safety stock, redundancy, proactive risk management, and postponement [4,27,39].
-
During disruption (Concurrent): a dynamic readjustment mechanism is mandatory to maintain operation, including broad and real-time data sharing, resource reallocation, and the possibility of use of temporary facilities [9].
-
Post-disruption (Reactive and Learning): recovery mechanisms are coupled with organizational learning and reconfiguration, anchoring the lessons learned from the disruption and making them the first barriers to anticipate new potential disruptions [32].

4.5. Measurement and Instrumentation

The measurement of resilience remains a challenging concern. Empirical evidence suggests that resilience increases only when vulnerabilities are reduced and capabilities strengthened [33]. Others argue that resilience encompasses the sustainable operation implementation, as it requires anticipatory planning after the post-recovery phase. More recent studies emphasize the importance of embedding a resilience assessment through many key performance indicators (KPIs), which allows firms to quantify their resilience. These metrics are grouped in the following table.
These KPIs were retained because they represent three core dimensions of resilience: operational resilience is measured by TTR, TTS, and service-level continuity; environmental resilience is measured by the carbon intensity of recovery; and financial resilience is measured by liquidity and financial buffers. There are many other indicators, but these remain the most cited metrics, and robust indicators assessing resilience and sustainability in the literature are summarized in Table 9.
Together, these resilience metrics establish a coherent measurement framework; while TTR and TTS highlight the adaptive and absorptive capacity, carbon intensity refers to the ability to be environmentally sustainable, and liquidity measures the financial resilience within the recovery process. Even though these indicators present a divergent aspect, their complementarity provides an integrated assessment system of resilience. No weights were attributed to these indicators, as we suggest that the interest of each indicator depends on many factors, such as the industrial context, the type of disruption, and the strategic priorities and goals. Instead, this framework highlights the main indicators and their contribution to different aspects of resilience, allowing practitioners to adapt the weighting of their own strategies.

4.6. Trade-Offs, Green Practices, and the Viability Nexus

The trade-offs involved in resilience measures shape supply chain viability. These trade-offs can be categorized into three main groups: (1) structural trade-off, where redundancy improves readiness but generates unsustainable costs, while network dispersion may lower interruption risks but increase CO2 emissions [4,29]; (2) operational trade-offs, where recovery actions could create deviations from the sustainable goals; (3) strategic trade-offs, where sustainability and green investment foster viability despite reducing flexibility and responsiveness provisionally.
Whether due to resource scarcity in emerging economies or industry priorities—such as agility in pharmaceuticals, modularity in automotive, or sustainability in agri-food supply chains—these tensions underscore the need to design resilience within contextual settings [46,55,67]. Reverse logistics, eco-design, and digital traceability are examples of green techniques that simultaneously improve adaptive capability and lessen environmental impact [30,41,61]. Viability expands on this logic by incorporating structural, strategic, and sustainability-oriented aspects that ensure continuing operation under extended uncertainty. Sustainability defines the long-term constraints related to environmental, social, and economic factors under which the supply chain must function; thus, the SCV emerges from these long-term sustainability requirements and the short- and medium-term adaptive capacity provided by resilience. These two dimensions interact continuously, enabling operational continuity during shocks and ensuring that long-term environmental, resource, and social requirements are fulfilled properly within the adaptive period.
Therefore, mature resilience capabilities that have been strengthened, institutionalized, and integrated with sustainability and agility principles can be seen as the evolutionary basis from which supply chain viability develops. In this way, SCRES provides the fundamental processes upon which sustainable supply chains are constructed and becomes a necessary but insufficient prerequisite for SCV.
Three crucial deficiencies appear despite the resilience literature’s extensive typologies (agility, adaptability, redundancy, and collaboration).
First, recovery speed (TTR and TTS) is overemphasized, and long-term structural transformation is underemphasized because resilience is primarily examined as a short-term reaction capability. This makes it more difficult to connect SCRES and SCV. Second, the majority of research takes a “capacity accumulation” perspective without addressing the underlying conflicts between resilience methods, such as redundancy versus cost, agility versus stability, and flexibility versus efficiency. Although these conflicts are recognized descriptively, they are rarely modeled or empirically investigated. Third, resilience enablers—such as visibility, collaboration, and digitalization—are frequently studied separately, neglecting their combined impacts. Few studies suggest integrated capacity configurations or examine interdependencies.
In general, the literature on SCRES is analytically dispersed but conceptually rich. It describes the available resilience capabilities, but it provides little information about how resilience interacts with green practices and develops into long-term viability. It is this gap that the following section on supply chain viability (SCV) seeks to address.

5. Supply Chain Viability

5.1. Conceptualization and Consensus

Despite their similarities, resilience (SCRES) and viability (SCV) represent distinct levels of adaptive potential. The ability to foresee, absorb, adapt, and recover from disturbances within a short- or medium-term horizon is the core focus of supply chain resilience. However, viability is a higher-order systemic competence that refers to the supply chain’s capacity to continue operating, reorganize, and develop sustainably in the face of protracted uncertainty. Therefore, resilience should be viewed as one operational element that facilitates viability rather than as a replacement for it.
SRES is conceptualized as a low-level adaptive capacity, which is necessary but cannot ensure the continuous viability of a supply chain. When disruptions last for a long time or when cumulative sustainability, societal, and environmental constraints come into play, resilience reaches its analytical limits. SCV addresses this gap by focusing on the supply chain’s ability to maintain expected performance during disruptions and to undergo deeper structural reconfigurations amid prolonged uncertainty.
Resilience, adaptability, sustainability, and structural redesign are all integrated into viability. By making this distinction obvious, conceptual overlap is avoided, and the SCV framework’s theoretical underpinnings are made clearer. Within this vision, Ruel et al. proposed a widely recognized framework, structured around five core dimensions: system development, structural mechanisms, redesign, feedback systems, and processes that embed viability through continuous learning and reconfiguration [15]. This aligns with social and ecological transformative perspectives, which are motivated by structural changes rather than a simple return to equilibrium status. To further explain the distinction between resilience and viability, the Table 10 illustrates the main differences.

5.2. Strategic Enablers and Mechanisms

Ecosystems and governance: SCV is no longer a firm’s internal property but rather a property of a broader ecosystem supported by interconnected networks and public–private partnerships [31,56,71]. Relational governance fosters trust, reciprocity, and co-financing, thereby enhancing environmental and economic outcomes through GSCM. Furthermore, institutional pressures (coercive, normative, and mimetic) and top management support increase the adoption of green practices and promote green innovation as part of a viability-oriented vision [36,37,62]. Furthermore, an integrated white SCM encompassing ethical, social, and environmental factors provides viability under broader society expectations [23].
Leadership and organizational capabilities: Organizational ambidexterity, combined with agility as a mediator, generates concrete, feasible outcomes by maintaining a balance of alignment and adaptation [40]. Sustainable leadership sets long-term vision, ethics, and innovation as the core enablers of viability [40].
Digitalization: IoT, digital twins, AI management, and big data analytics enhance anticipation, decision-making, and reconfiguration [26,29]. Empirical evidence demonstrates that digitalization improves performance, especially through response and recovery capabilities; in contrast, over-preparedness decreases efficiency [29]. From the customer side, digitalization helps reduce implicit costs (e.g., long lead times, manual processing time, and errors) and enhances skills, thereby improving suppliers’ green total factor productivity (GTFP) [29].

5.3. Financial and Circular Economy Enablers of Supply Chain Viability

Circular economy and financial resilience: Circular practices and robust financial models are the core pillars of the supply chain viability. Circular practices rest on measures such as reverse logistics, green transport, and recycling, which promote sustainability, while Industry 5.0 extends the digital supply chain with an environmental focus and human-centric approach [22,31]. The revenue diversification and economies of scale are the main enablers of a viable supply chain, as limited sources of revenue, firms, and subsidy-only models often fail [55]. From a financial standpoint, a viable supply chain refers to an efficient business model ensuring financial continuity and sustainable cost coverage through several factors, such as economies of scale, revenue diversification, and supportive arrangements (public–private partnership or subsidies).
Evidence shows that cycle logistics achieve a rapid payback with environmental benefits [70]. The buyer–supplier collaboration reinforces survivability under disruption and stabilizes continuity with liquidity-aware approaches [70].
People, knowledge, and culture: Green management, technological innovation, knowledge sharing, and green HRM act as the main mediators for translating GSCM into long-term/viable performance, particularly in developing countries and the SME context [7]. Recent work by Ghazvinian introduces lean, agility, resilience, green, and sustainability (LARGS) as the key factors for reconciliation of efficiency with sustainability and resilience [14].

5.4. Trade-Offs, Boundary Conditions, and Sectoral Contexts

Supply chain viability rests on explicit trade-offs and adaptation to specific contexts. These are synthesized and summarized in Table 11.

5.5. Measurement and Managerial Instrumentation

For academic rigor and managerial relevance, this section provides a measurement framework that helps to transform ambition into a tangible instrument.

5.5.1. Consensus Framing Through ARS and KPIs

Since it is a widely accepted framework, the ARS triad (agility, resilience, and sustainability) is converted into operational KPIs such as recovery time, lead time, responsiveness, and carbon intensity. This enhances empirical comparability and supports managerial decision-making with clear benchmarks.

5.5.2. Organizational Diagnostics via Ruel’s Scale

To ensure the firm’s performance, Ruel’s scale proposes the measurement of organizational capabilities by shedding light on five core dimensions—structural design, workforce development, process redesign, feedback mechanisms, and operational processes—providing an efficient diagnostic for managers and research validity for scientists.

5.5.3. Decision Dashboards Within a Viability Envelope

To remain competitive and sustainable, defining a viability envelope is becoming primordial, with dashboards tracking key indicators such as CO2 emission intensity, time to survive or recover, freshness measures, service level, and cash-to-cash cycle.
By combining ARS framing, Ruel’s diagnostic, and managerial follow-up, this approach provides theoretically robust and organizational support.

5.5.4. Evidence Summary

When combined, these studies show that supply chain viability results from the interplay of structural, technological, financial, and relational factors rather than being determined by a single skill. The main cornerstones underlying supply chain viability are summarized in Table 12. Viability is frequently described in the literature as a longer-term, higher-order adaptive ability that goes beyond immediate recovery and resilience. The gap noted in this research is further reinforced by the fact that, despite the growing maturity of conceptual and measurement frameworks for SCV, their empirical and quantitative operationalization is still limited.

5.5.5. Critical Integration of SCV Results

Theoretically, viability is an intriguing concept, but there is still little and scattered empirical support for it. All the examined studies reveal three significant limitations.
SCV is first operationalized ambiguously. Beyond the work of Ruel et al. (2024) [15], the majority of articles define the viability conceptually (ecosystems, ARS triad, and long-term adaptation), but they offer few quantifiable indicators or validated measures.
Second, studies seldom describe the processes by which short-term adaptability transforms into long-term continuity, instead seeing viability as an extension of resilience. Often, viability is discussed conceptually rather than being empirically examined.
Third, there is a lack of development in the integration of GSCM, SCRES, and SCV. Empirical research on the combined impact of the three dimensions is virtually nonexistent, despite evidence that green practices promote resilience and that resilience may improve viability. As a result, although SCV offers a unifying perspective for sustainable and adaptable supply chains, the literature is deficient in multi-level modeling, thorough empirical validation, and integrated frameworks connecting GSCM, SCRES, and SCV.
This SLR’s primary theoretical finding is that viability results from the interaction of adaptive mechanisms (SCRES) and green foundations (GSCM) rather than from separate capabilities. Integrated frameworks that clearly account for trade-offs, simulate these interconnections, and provide empirical validation are required.

5.6. Positioning in the Literature Review

This review moves from green practices (foundational sustainability capabilities) to resilience (the near-term operational manifestation of viable design) to viability (the ARS-based, ecosystem-enabled, financially sustainable, digitally supported capacity to endure and evolve). This sequencing ensures that recovery speed does not compromise long-term environmental and economic survivability [31].

6. Modeling and Optimization Approaches in Green, Resilient, Viable Supply Chains

Following the existing literature, in this section, we will review the modeling and optimization of green, resilience, and viability mechanisms and assess how these have been operationalized to support decision-making under uncertainty. Green supply chain management (GSCM), supply chain resilience (SCRES), and supply chain viability (SCV) are translated from the theoretical model into quantitative and measurable frameworks via mathematical models.
Using optimization and simulation techniques, researchers quantify trade-offs among economic performance, environmental outcomes, resilience capabilities, and sustainable operations while embedding principles of the agility–resilience–sustainability (ARS) triad into supply chain management. Accordingly, the provided classification framework shows how the modeling and optimization approaches operationalize the integrated treatment of GSCM objectives alongside resilience mechanisms and the long-term supply chain viability concepts.

6.1. Overview of Modeling Paradigms

Several optimization models integrating GSCM, SCRES, and SCV have been identified in the literature and can be categorized as summarized in the following table. This study uses a more rigorous classification approach for optimization-based studies in order to prevent overlaps between aims, solution methods, and modeling paradigms. The examined papers are arranged along two complementary dimensions. Supply chain network design, inventory and sourcing management, production planning, transportation, or closed-loop systems are examples of the first dimension, which refers to the main decision problem addressed (problem focus). The second dimension pertains to the modeling and management of uncertainty in the optimization framework, differentiating between data-driven or adaptive, stochastic, resilient, and deterministic techniques. This two-dimensional classification recognizes that a single model may incorporate several techniques, avoid artificially separating methods, and offer a more conceptually coherent and understandable framework for synthesizing the literature. In this synthesis, deterministic, stochastic, robust, and data-driven approaches are treated as parallel modeling paradigms, rather than hierarchical categories.
These modeling paradigms show how supply chains have shifted from static models to data-driven, adaptive ones that reflect the changing balance between resilience, sustainability, and performance amid uncertainty. While multi-objective and stochastic models make up a large portion of the cited studies, they often depend on predefined disruption scenarios and fixed parameters. Unlike data-driven models, which can adapt and learn in real time, these rely on static assumptions and face challenges like data availability, data quality, and complexity in implementation. Nonetheless, most models focus on resilience and efficiency as key priorities and tend to implicitly include viability. However, viability is rarely modeled as a distinct, forward-looking capability in current research. Instead, many studies aim to optimize resilience aspects (e.g., robustness and recovery), which are critical but not sufficient for ensuring long-term supply chain viability.

6.2. Key Findings from Optimization Studies

The design of sustainable, low-carbon, high-performance, disruption-tolerant, and economically efficient networks constitutes the primary goal of optimization research. The primary contributions and empirical findings from several sample GRV modeling frameworks are shown in Table 13.
Representative optimization frameworks and their main outcomes identified in the literature are summarized in Table 14.
When considered as a whole, these optimization models demonstrate how quantitative tools transform the viability paradigm into helpful decision-making frameworks. They provide practical solution spaces that improve flexibility and long-term system performance while addressing trade-offs between resilience and sustainability goals, such as redundancy vs. cost, or facility dispersion against emissions. Methodologically, there is observable diversity, whereas several common patterns are revealed. Firstly, many models emphasize a trade-off between strategies enhancing resilience, like redundancy or dual sourcing, and economic or environmental costs. Secondly, closed-loop models are usually associated with long-term optimization and performance by fostering operational continuity, although they require a higher initial investment, and the benefits may depend on context (type of industry and return volume). Thirdly, most of the models are oriented toward short- and medium-term optimization, which may hinder deeper and long-term structural adaptation, which represents a core characteristic of viability.

6.3. Implications and Research Outlook

Both modeling and optimization methodologies have translated the conceptual understanding of green supply chain management (GSCM), supply chain resilience (SCRES), and supply chain visibility (SCV) into actionable frameworks.
Their contributions include the following aspects:
  • The operationalization of the ARS triad into measurable goals that could guarantee profitability, sustainability, and resilience;
  • The enablement of trade-off and synergy analyses between green supply chain management and adaptive capabilities;
  • The identification of high-impact levers, such as process flexibility, dual sourcing, and circular flows.
Despite these advances, most optimization models still lack the integration of social, governance, and financial viability dimensions and focus more on economic, environmental, and resilience aspects.

7. Research Gaps and Future Research Agenda

7.1. Answers to the Research Questions

  • RQ1: Which green supply chain practices are the most commonly used?
  • According to the review, the most widely used GSCM methods across industries include internal environmental management, green purchasing, eco-design, reverse logistics, and cooperation.
  • RQ2: How much do these practices affect the resilience of the supply chain?
  • Although their impacts are frequently context-dependent and linked to short-term cost trade-offs, the evidence demonstrates that green practices improve resilience by enhancing flexibility, visibility, and collaboration.
  • RQ3: Does supply chain viability benefit from the integration of GSCM and resilience?
  • Results show that resilience serves as a prerequisite for viability but is not sufficient. Resilience- and sustainability-focused practices both interact to produce long-term viability, which is bolstered by structural adaptation mechanisms.
  • RQ4: What synergies and trade-offs are found?
  • Cost versus redundancy and emissions versus network dispersion are common trade-offs throughout research, but circularity, digitalization, and cooperative governance show synergies.
  • RQ5: Which modeling and optimization techniques are applied?
  • Supply chain viability is frequently addressed implicitly or through proxy indicators in most of the quantitative research that uses resilience-oriented optimization models. There is still little explicit modeling of long-term structural adaptation.

7.2. Research Gaps

The review’s findings point to a few significant, specific, and interrelated gaps that have not yet been adequately addressed in the literature and offer interesting directions for further investigation. Mechanisms, measurement, temporal evolution, enabling systems, and contextual limitations that control the relationship between resilience capacities, long-term viability, and green practices are among these gaps. First, most of the research prioritizes efficiency, robustness, or recovery speed over long-term viability by adopting a short- to medium-term analytical horizon, which naturally coincides with resilience. Secondly, supply chain viability as a dynamic, systemic, and long-term capability is more challenging to conceptualize and operationalize, since prevailing empirical and modeling paradigms in supply chain management emphasize event-driven and performance-based outcomes. Lastly, the fragmented and non-prioritized placement of SCV throughout the evaluated research is a result of the lack of a comprehensive theoretical framework integrating resilience mechanisms, green practices, and long-term adaptability.

7.2.1. Discovering the Mechanisms Connecting Resilience, Viability, and Green Practices

Although many studies show that GSCM improves performance, little is understood about the mechanisms by which green practices enhance adaptive capacities and how these capacities ultimately support survivability. Future studies should look into how certain practices (such as eco-design, circularity, digital traceability, and reverse logistics) affect resilience characteristics like learning, flexibility, and visibility and how resilience becomes long-term structural viability.
In particular, it remains unclear whether the resilience capacities mediate, moderate, or condition the impact of green perspectives on long-term viability.
By elucidating these mechanisms, causal, theory-driven explanations would replace association-based findings. Across the studies examined, green practices are principally linked to performance outcomes, although the mechanism through which they reinforce adaptive capacity and viability remains largely implicit.

7.2.2. Improving Viability Measurement and Operationalization

Viability is becoming more conceptually mature, yet it is still difficult to measure empirically. Future research should concentrate on developing quantifiable viability thresholds, such as minimum buffers or adaptability requirements; defining operational indicators of adaptability, continuity, ecosystem integration, and structural reconfiguration; and validating these indicators across industries and supply chain configurations. To turn the idea into a useful and comparable managerial tool, viability measurement must be strengthened.

7.2.3. Capturing Adaptive Capabilities Temporal Dynamics

Current studies frequently rely on cross-sectional or static data, which do not capture how supply chains change in the face of frequent or protracted disruptions. Future research should focus more on longitudinal study designs that track the development or deterioration of green and adaptive skills across time and make greater use of dynamic simulation techniques (agent-based modeling, system dynamics) to investigate long-term reconfiguration under uncertainty. Designing supply chains that can adjust over time requires an understanding of these temporal dynamics.

7.2.4. Underexplored Technological, Human, and Institutional Enablers

Although human capital and digital technologies are acknowledged facilitators, little is known about how they work to create sustainable supply chains. Future studies could look at how digital technologies (IoT, AI, digital twins, and predictive analytics) facilitate coordination, anticipation, and redesign empirically. Furthermore, they should shed light on the importance of the interplay between human and technical enablers within intricate supply chain ecosystems and how leadership, green competencies, organizational culture, and learning capacities support long-term continuity. Realistic implementation requires placing viability within a socio-technical viewpoint, which can be achieved by integrating these elements.

7.2.5. Taking Context-Specific Limitations and Vulnerabilities into Account

Lastly, the GSCM–SCRES–SCV nexus functions differently in circumstances when structural restrictions are present. Future studies ought to investigate situations with limited resources, such as rising economies and SMEs; industries with high environmental sensitivity or disruption exposure (food, pharmaceuticals, energy, and mining); and operational circumstances where resilience, circularity, and visibility struggle with financial, regulatory, or infrastructure constraints. Boundary conditions, alternate routes, and context-specific tactics that increase the application of workable supply chain models can be identified with such investigations. Future research can greatly improve theoretical clarity and offer practical insights for creating supply chains that are not only resilient and green but also structurally viable in the long term. This can be achieved by filling in these fundamental gaps: mechanisms, measurement, temporal development, socio-technical enablers, and contextual constraints.

7.3. Future Research Agenda

Future studies should consider both theoretical and empirical perspectives, with the following recommendations.

7.3.1. Develop an Integrated Theoretical Framework and Standardized Measurement Systems

Future research should combine the Dynamic Capabilities Theory, the Complex Adaptive Systems Theory, and the Triple Bottom Line to create a conceptual framework for assessing how firms sense, seize, and reconfigure green and resilient capabilities to maintain long-term viability [1,33,34]. This framework would better highlight the connection between green innovation, resilience capacity, and systemic viability.
Researchers should create standardized performance measures that consider environmental, economic, operational, and social factors [30,61,68]. Establishing quantitative criteria to measure supply chain resilience, viability, and green operations would allow cross-firm and cross-industry comparisons, hence improving managerial adoption.

7.3.2. Combine Empirical Validation with Digital and Technological Enablers

While previous research used modeling and optimization techniques, future studies should combine these methods with empirical validation to improve the practical relevance and external validity of their results. Furthermore, emerging technologies such as artificial intelligence, blockchain, the internet of things, and big data may provide practical benefits by increasing real-time monitoring and predictability. Therefore, integrating these tools would be valuable, as they represent key levers for future managerial decision-making.

7.3.3. Advance Integrative, Data-Driven, and Multi-Level Modeling Approaches

Future studies should go beyond isolated simulations by developing integrative frameworks that encompass models, optimization, stochastic simulation, and empirical grounding. Although they address resilience and viability, current hybrid models are still context-specific and methodologically fragmented [31,69]. Future studies should provide frameworks that integrate data-driven learning, system dynamics, and optimization in a way that is both adaptable and interoperable. Even though there is currently little integration of digital twin simulations with behavioral and policy situations, this might help to shed light on how actions taken in real time impact long-term viability. Furthermore, to create unified viability and sustainable measures and generalize results, cross-sector and cross-country validations are required [8,59].

7.3.4. Embedding Human, Institutional, and Governance Mechanisms

Nowadays, the integration of technology is becoming increasingly vital, embedding sustainable supply chain viability, which closely depends on human, governance, and organizational factors. Therefore, future studies should analyze how these factors promote adaptive and regenerative supply chain networks [28,56]. Recent studies on Industry 5.0 and viability emphasize the significance of combining strategic strategies, ethical considerations, stakeholder collaboration with environmental sustainability, and digital transformation [59,63]. Recent empirical research on leadership competencies, collective creativity, and regulatory incentives may show how socio-institutional systems promote resilience, sustainability, and systemic viability [22,65].

8. Theoretical Contribution and Conclusions

This systematic literature review presents an integrated framework that incorporates green supply chain management (GSCM), supply chain resilience (SCRES), and supply chain viability (SCV) to improve theoretical knowledge of sustainable, resilient, and viable supply chains. Based on seventy peer-reviewed studies published between 2010 and 2024, it synthesizes how adaptive, environmental, and systemic capabilities collaborate and interact to achieve a sustainable, resilient, and viable performance in uncertain environments.
From a theoretical perspective, this review maps and integrates current concepts and views. It emphasizes that the majority of research investigates GSCM, SCRES, and SCV separately, with only a few studies looking at their interactions. As a result, this study presents a unified conceptual underpinning, demonstrating that sustainable operations serve as a driver of resilience and that both sustainability and resilience work together to enhance long-term viability in disrupted situations. Furthermore, this analysis categorizes the most common modeling and optimization methodologies that investigate the trade-offs between environmental, adaptive, viability, and performance objectives. This allows readers to understand how theoretical concepts are translated into empirical and quantitative modeling and optimization frameworks. In terms of methodology, this study offers value by employing a transparent and repeatable synthesis approach based on Sauer and Seuring’s six-step framework and the PRISMA 2020 procedure. From a managerial approach, these insights help practitioners create supply chains that strike a balance between environmental responsibility and adaptability, transforming sustainability into a powerful tool for long-term survival and resilience.
Overall, this study lays the groundwork for future research aimed at incorporating GSCM, SCRES, and SCV into unified models. To construct flexible and sustainable supply chains that can withstand upheavals and generate long-term sustainable value, future research should focus on integrated frameworks, empirical validation, and digital or optimization-based tools.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/logistics10010023/s1.

Author Contributions

Methodology, H.C.; formal analysis, H.C.; investigation, H.C.; data curation, H.C.; writing—original draft preparation, H.C.; visualization, H.C.; validation, M.A.E.O., A.H., and H.M., M.A.E.O. with contributions from H.C.; writing—review and editing, M.A.E.O., A.H., and H.M.; supervision and academic guidance, M.A.E.O., A.H., and H.M.; project administration, M.A.E.O., A.H., and H.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wieland, A. Dancing the Supply Chain: Toward Transformative Supply Chain Management. J. Supply Chain Manag. 2021, 57, 58–73. [Google Scholar] [CrossRef]
  2. Green, K.W., Jr.; Zelbst, P.J.; Meacham, J.; Bhadauria, V.S. Green supply chain management practices: Impact on performance. Supply Chain Manag. Int. J. 2012, 17, 290–305. [Google Scholar] [CrossRef]
  3. Rahman, H.U.; Zahid, M.; Ullah, M.; Al-Faryan, M.A.S. Green supply chain management and firm sustainable performance: The awareness of China Pakistan Economic Corridor. J. Clean. Prod. 2023, 414, 137502. [Google Scholar] [CrossRef]
  4. Hasani, A.; Mokhtari, H.; Fattahi, M. A multi-objective optimization approach for green and resilient supply chain network design: A real-life case study. J. Clean. Prod. 2021, 278, 123199. [Google Scholar] [CrossRef]
  5. Mirzaee, H.; Samarghandi, H.; Willoughby, K. Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model. J. Clean. Prod. 2024, 471, 143165. [Google Scholar] [CrossRef]
  6. Asif, M.S.; Lau, H.; Nakandala, D.; Fan, Y.; Hurriyet, H. Adoption of green supply chain management practices through collaboration approach in developing countries—From literature review to conceptual framework. J. Clean. Prod. 2020, 276, 124191. [Google Scholar] [CrossRef]
  7. Junejo, I.; Sohu, J.M.; Alwadi, B.M.; Ejaz, F.; Nasir, A.; Hossain, B. Green supply chain management and SMEs sustainable performance in developing country: Role of green knowledge sharing, green innovation and big data-driven supply chain. Discov. Sustain. 2025, 6, 342. [Google Scholar] [CrossRef]
  8. Sawik, T. A stochastic optimisation approach to maintain supply chain viability under the ripple effect. Int. J. Prod. Res. 2023, 61, 2452–2469. [Google Scholar] [CrossRef]
  9. Ali, A.; Mahfouz, A.; Arisha, A. Analysing supply chain resilience: Integrating the constructs in a concept mapping framework via a systematic literature review. SCM 2017, 22, 16–39. [Google Scholar] [CrossRef]
  10. Kochan, C.G.; Nowicki, D.R. Supply chain resilience: A systematic literature review and typological framework. Int. J. Phys. Distrib. Logist. Manag. 2018, 48, 842–865. [Google Scholar] [CrossRef]
  11. Conz, E.; Magnani, G. A dynamic perspective on the resilience of firms: A systematic literature review and a framework for future research. Eur. Manag. J. 2020, 38, 400–412. [Google Scholar] [CrossRef]
  12. Fahimnia, B.; Sarkis, J.; Talluri, S. Editorial: Design and Management of Sustainable and Resilient Supply Chains. IEEE Trans. Eng. Manag. 2019, 66, 2–7. [Google Scholar] [CrossRef]
  13. Negri, M.; Cagno, E.; Colicchia, C.; Sarkis, J. Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda. Bus. Strat. Environ. 2021, 30, 2858–2886. [Google Scholar] [CrossRef]
  14. Kosasih, W.; Pujawan, I.N.; Karningsih, P.D.; Shee, H. Integrated lean-green practices and supply chain sustainability framework. Clean. Responsible Consum. 2023, 11, 100143. [Google Scholar] [CrossRef]
  15. Ruel, S.; El Baz, J.; Ivanov, D.; Das, A. Supply chain viability: Conceptualization, measurement, and nomological validation. Ann. Oper. Res. 2024, 335, 1107–1136. [Google Scholar] [CrossRef] [PubMed]
  16. Homayouni, Z.; Pishvaee, M.S.; Jahani, H.; Ivanov, D. A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty. Ann. Oper. Res. 2023, 324, 395–435. [Google Scholar] [CrossRef]
  17. Sauer, P.C.; Seuring, S. How to conduct systematic literature reviews in management research: A guide in 6 steps and 14 decisions. Rev. Manag. Sci. 2023, 17, 1899–1933. [Google Scholar] [CrossRef]
  18. Ahmadini, A.A.H.; Modibbo, U.M.; Shaikh, A.A.; Ali, I. Multi-objective optimization modelling of sustainable green supply chain in inventory and production management. Alex. Eng. J. 2021, 60, 5129–5146. [Google Scholar] [CrossRef]
  19. Santos, J.A.M.; Martins, M.S.E.; Pinto, R.M.; Vieira, S.M. Towards Sustainable Inventory Management: A Many-Objective Approach to Stock Optimization in Multi-Storage Supply Chains. Algorithms 2024, 17, 271. [Google Scholar] [CrossRef]
  20. Hussaini, Z.; Nemati, A.; Paydar, M.M. A multi-period multi-season multi-objective mathematical model for guaranteeing the viability of supply chains under fluctuations: A healthcare closed-loop supply chain application. Ann. Oper. Res. 2025, 346, 1063–1108. [Google Scholar] [CrossRef]
  21. Ali, E.; Gossaye, W. The effects of supply chain viability on supply chain performance and marketing performance in case of large manufacturing firm in Ethiopia. Braz. J. Oper. Prod. Manag. 2023, 20, 1535. [Google Scholar] [CrossRef]
  22. Jayarathna, C.P.; Agdas, D.; Dawes, L. Viability of sustainable logistics practices enabling circular economy: A system dynamics approach. Bus. Strat. Environ. 2024, 33, 3422–3439. [Google Scholar] [CrossRef]
  23. Suksanchananun, W.; Kot, S.; Chaiyasoonthorn, W.; Chaveesuk, S. The model of White Supply Chain Management for sustainable performance in the food industry. Equilibrium 2024, 19, 1405–1448. [Google Scholar] [CrossRef]
  24. Wang, H. Green Supply Chain Optimization Based on BP Neural Network. Front. Neurorobot. 2022, 16, 865693. [Google Scholar] [CrossRef]
  25. Latpate, R.; Bhosale, M.; Kurade, S. Green Reverse Supply Chain Models with Fuzzy Stochastic Re-manufacturing Capacity. Int. J. Fuzzy Syst. 2024, 26, 403–417. [Google Scholar] [CrossRef]
  26. Alabdali, M.A.; Yaqub, M.Z.; Windsperger, J. Green digital leadership and algorithmic management for sustainable supply chains: A serial mediation model. Int. J. Inf. Manag. Data Insights 2025, 5, 100343. [Google Scholar] [CrossRef]
  27. Durach, C.F.; Wieland, A.; Machuca, J.A.D. Antecedents and dimensions of supply chain robustness: A systematic literature review. Int. J. Phys. Distrib. Logist. Manag. 2015, 45, 118–137. [Google Scholar] [CrossRef]
  28. Zhang, C.Y.; Ji, J.; Zhang, T. Can sustainable supply chain sustain supplier’s operational efficiency? Int. J. Prod. Econ. 2025, 285, 109640. [Google Scholar] [CrossRef]
  29. Zhao, N.; Hong, J.; Lau, K.H. Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model. Int. J. Prod. Econ. 2023, 259, 108817. [Google Scholar] [CrossRef]
  30. Zhu, Q.; Sarkis, J.; Lai, K.-H. Examining the effects of green supply chain management practices and their mediations on performance improvements. Int. J. Prod. Res. 2012, 50, 1377–1394. [Google Scholar] [CrossRef]
  31. Ivanov, D.; Dolgui, A.; Blackhurst, J.V.; Choi, T.-M. Toward supply chain viability theory: From lessons learned through COVID-19 pandemic to viable ecosystems. Int. J. Prod. Res. 2023, 61, 2402–2415. [Google Scholar] [CrossRef]
  32. Liu, J.; Sarkar, S.; Kumar, S.; Jin, Z. An analysis of stock market impact from supply chain disruptions in Japan. Int. J. Prod. Perform. Manag. 2018, 67, 192–206. [Google Scholar] [CrossRef]
  33. Pettit, T.J.; Fiksel, J.; Croxton, K.L. ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK. J. Bus. Logist. 2010, 31, 1–21. [Google Scholar] [CrossRef]
  34. Wieland, A.; Durach, C.F. Two perspectives on supply chain resilience. J. Bus. Logist. 2021, 42, 315–322. [Google Scholar] [CrossRef]
  35. Li, F.; Zhang, H. The impact of green supply chain management on corporate performance under the full process model: A MASEM analysis based on heterogeneous moderation. J. Clean. Prod. 2024, 481, 144099. [Google Scholar] [CrossRef]
  36. Zhu, Q.; Feng, Y.; Choi, S.-B. The role of customer relational governance in environmental and economic performance improvement through green supply chain management. J. Clean. Prod. 2017, 155, 46–53. [Google Scholar] [CrossRef]
  37. Wen, X.; Cheah, J.-H.; Lim, X.-J.; Ramachandran, S. Why does “green” matter in supply chain management? Exploring institutional pressures, green practices, green innovation, and economic performance in the Chinese chemical sector. J. Clean. Prod. 2023, 427, 139182. [Google Scholar] [CrossRef]
  38. Zhao, X.; Li, S. Environmental uncertainty, supply chain, and stability of sustainable green innovation:Based on micro evidence from energy-intensive enterprises. J. Environ. Manag. 2024, 372, 123398. [Google Scholar] [CrossRef] [PubMed]
  39. Bahizire, G.M.; Sun, H.; Chen, T. Exploring the role of managerial green commitment in enhancing sustainability in Congo’s cobalt supply chain. J. Environ. Manag. 2024, 372, 122997. [Google Scholar] [CrossRef]
  40. Aslam, H.; Khan, A.Q.; Rashid, K.; Rehman, S.-U. Achieving supply chain resilience: The role of supply chain ambidexterity and supply chain agility. J. Manuf. Technol. Manag. 2020, 31, 1185–1204. [Google Scholar] [CrossRef]
  41. Çankaya, S.Y.; Sezen, B. Effects of green supply chain management practices on sustainability performance. J. Manuf. Technol. Manag. 2019, 30, 98–121. [Google Scholar] [CrossRef]
  42. Rasi, R.E.; Sohanian, M. A multi-objective optimization model for sustainable supply chain network with using genetic algorithm. J. Model. Manag. 2021, 16, 714–727. [Google Scholar] [CrossRef]
  43. Asha, L.N.; Aragon, L.G.; Dey, A.; Yodo, N. Location Optimization Strategies for Corn Production and Distribution towards Sustainable Green Supply Chain. Logistics 2024, 8, 78. [Google Scholar] [CrossRef]
  44. Abbasi, S.; Vlachos, I.; Samadzadeh, A.; Etemadifar, S.; Afshar, M.; Amra, M. Modelling a Logistics and Financial Supply Chain Network during the COVID-19 Era. Logistics 2024, 8, 32. [Google Scholar] [CrossRef]
  45. Moyo, E.H.; Carstens, S.; Walters, J. Simulation Model for a Sustainable Food Supply Chain in a Developing Country: A Case Study of the Banana Supply Chain in Malawi. Logistics 2024, 8, 85. [Google Scholar] [CrossRef]
  46. Shetty, S.K.; Bhat, K.S. Green supply chain management practices implementation and sustainability –A review. Mater. Today: Proc. 2022, 52, 735–740. [Google Scholar] [CrossRef]
  47. Yun, Y.; Chuluunsukh, A.; Gen, M. Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach. Mathematics 2020, 8, 84. [Google Scholar] [CrossRef]
  48. Khot, S.B.; Thiagarajan, S. Unveiling the relationships between enablers in resiliently sustainable supply chains using an integrated TISM-Fuzzy MICMAC approach: Study of Indian automobile sector post-COVID-19. Multidiscip. Sci. J. 2024, 6, 2024256. [Google Scholar] [CrossRef]
  49. Jin, H.; Jiang, N.; Su, W.; Dalia, S. How does customer enterprise digitalization improve the green total factor productivity of state-owned suppliers: From the supply chain perspective. Omega 2025, 133, 103248. [Google Scholar] [CrossRef]
  50. Ahmed, W.; Ashraf, M.S.; Khan, S.A.; Kusi-Sarpong, S.; Arhin, F.K.; Kusi-Sarpong, H.; Najmi, A. Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance. Oper. Manag. Res. 2020, 13, 4–21. [Google Scholar] [CrossRef]
  51. Imran, T.I.; Karmaker, C.L.; Karim, R.; Misbauddin, S.M.; Bari, A.B.M.M.; Raihan, A. Modeling the supply chain sustainability imperatives in the fashion retail industry: Implications for sustainable development. PLoS ONE 2024, 19, e0312671. [Google Scholar] [CrossRef]
  52. Stich, V.; Groten, M. Design and Simulation of a Logistics Distribution Network Applying the Viable System Model (VSM). Procedia Manuf. 2015, 3, 534–541. [Google Scholar] [CrossRef]
  53. Liu, F.; Song, J.; Tong, J.D. Building Supply Chain Resilience through Virtual Stockpile Pooling. Prod. Oper. Manag. 2016, 25, 1745–1762. [Google Scholar] [CrossRef]
  54. Simchi-Levi, D.; Wang, H.; Wei, Y. Increasing Supply Chain Robustness through Process Flexibility and Inventory. Prod. Oper. Manag. 2018, 27, 1476–1491. [Google Scholar] [CrossRef]
  55. Mohanty, R.; Prakash, A. Green supply chain management practices in India: An empirical study. Prod. Plan. Control 2014, 25, 1322–1337. [Google Scholar] [CrossRef]
  56. Katsela, K.; Pålsson, H. Viable business models for city logistics: Exploring the cost structure and the economy of scale in a Swedish initiative. Res. Transp. Econ. 2021, 90, 100857. [Google Scholar] [CrossRef]
  57. Kayani, S.A.; Warsi, S.S. Exploring the synergy between sustainability and resilience in supply chains under stochastic demand conditions and network disruptions. Results Eng. 2025, 26, 104954. [Google Scholar] [CrossRef]
  58. Berger-Douce, S. Capacité dynamique de résilience et RSE, l’alchimie gagnante face à la Covid-19 ? Rev. Int. P.M.E. 2021, 34, 100–120. [Google Scholar] [CrossRef]
  59. Feng, T.; Qamruzzaman, M.; Sharmin, S.S.; Karim, S. Bridging Environmental Sustainability and Organizational Performance: The Role of Green Supply Chain Management in the Manufacturing Industry. Sustainability 2024, 16, 5918. [Google Scholar] [CrossRef]
  60. Boutmir, Y.; Bannari, R.; Touil, A.; Fri, M.; Benmoussa, O. Dependent-Chance Goal Programming for Sustainable Supply Chain Design: A Reinforcement Learning-Enhanced Salp Swarm Approach. Sustainability 2025, 17, 6079. [Google Scholar] [CrossRef]
  61. Khan, M.T.; Idrees, M.D.; Rauf, M.; Sami, A.; Ansari, A.; Jamil, A. Green Supply Chain Management Practices’ Impact on Operational Performance with the Mediation of Technological Innovation. Sustainability 2022, 14, 3362. [Google Scholar] [CrossRef]
  62. Saeed, A.; Jun, Y.; Nubuor, S.A.; Priyankara, H.P.R.; Jayasuriya, M.P.F. Institutional Pressures, Green Supply Chain Management Practices on Environmental and Economic Performance: A Two Theory View. Sustainability 2018, 10, 1517. [Google Scholar] [CrossRef]
  63. Zarreh, M.; Khandan, M.; Goli, A.; Aazami, A.; Kummer, S. Integrating Perishables into Closed-Loop Supply Chains: A Comprehensive Review. Sustainability 2024, 16, 6705. [Google Scholar] [CrossRef]
  64. Ghazvinian, A.; Feng, B.; Feng, J.; Talebzadeh, H.; Dzikuć, M. Lean, Agile, Resilient, Green, and Sustainable (LARGS) Supplier Selection Using Multi-Criteria Structural Equation Modeling under Fuzzy Environments. Sustainability 2024, 16, 1594. [Google Scholar] [CrossRef]
  65. Guerra, J.D.; Palomino, G.; Yildiz, O.; Macassi, I.A.; Alvarez, J.C. Re-Designing Business Process Models for Enhancing Sustainability in Spinach Production Through Lean Tools with Digital Transformation. Sustainability 2025, 17, 5673. [Google Scholar] [CrossRef]
  66. Mahar, A.S.; Zhang, Y.; Sadiq, B.; Gul, R.F. Sustainability Transformation Through Green Supply Chain Management Practices and Green Innovations in Pakistan’s Manufacturing and Service Industries. Sustainability 2025, 17, 2204. [Google Scholar] [CrossRef]
  67. Suriyankietkaew, S.; Krittayaruangroj, K.; Iamsawan, N. Sustainable Leadership Practices and Competencies of SMEs for Sustainability and Resilience: A Community-Based Social Enterprise Study. Sustainability 2022, 14, 5762. [Google Scholar] [CrossRef]
  68. Agyabeng-Mensah, Y.; Ahenkorah, E.; Afum, E.; Dacosta, E.; Tian, Z. Green warehousing, logistics optimization, social values and ethics and economic performance: The role of supply chain sustainability. Int. J. Logist. Manag. 2020, 31, 549–574. [Google Scholar] [CrossRef]
  69. Liu, A.; Zhu, Q.; Xu, L.; Lu, Q.; Fan, Y. Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model. Transp. Res. Part E Logist. Transp. Rev. 2021, 150, 102319. [Google Scholar] [CrossRef]
  70. Nascimento, C.d.O.L.; Rigatto, I.B.; de Oliveira, L.K. Characterization and analysis of the economic viability of cycle logistics transport in Brazil. Transp. Res. Procedia 2020, 46, 189–196. [Google Scholar] [CrossRef]
  71. Katsela, K. Viable business models for city logistics: Exploring the cost structure and revenue streams of fourteen European cases. Transp. Res. Procedia 2025, 82, 3889–3899. [Google Scholar] [CrossRef]
  72. Alashhab, M.S. DEVELOPING A ROBUST GREEN SUPPLY CHAIN PLANNING OPTIMIZATION MODEL CONSIDERING POTENTIAL RISKS. Int. J. GEOMATE 2020, 19, 208–215. [Google Scholar] [CrossRef]
  73. Laosirihongthong, T.; Adebanjo, D.; Tan, K.C. Green supply chain management practices and performance. Ind. Manag. Data Syst. 2013, 113, 1088–1109. [Google Scholar] [CrossRef]
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Figure 1. Conceptual model: integrating green supply chain management and resilience toward a dynamic framework of supply chain viability.
Figure 1. Conceptual model: integrating green supply chain management and resilience toward a dynamic framework of supply chain viability.
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Figure 2. PRISMA 2020 flow diagram of the literature selection process.
Figure 2. PRISMA 2020 flow diagram of the literature selection process.
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Figure 3. Graphic evolution of the studied paper in this review per year.
Figure 3. Graphic evolution of the studied paper in this review per year.
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Figure 4. Integration of green supply chain management and resilience toward a dynamic framework of supply chain viability.
Figure 4. Integration of green supply chain management and resilience toward a dynamic framework of supply chain viability.
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Figure 5. Stages of Building Supply Chain Resilience.
Figure 5. Stages of Building Supply Chain Resilience.
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Table 1. Overview of prior contributions.
Table 1. Overview of prior contributions.
ReferenceGSCM DimensionSCRES DimensionViability SCV Dimension
Ali, Mahfouz & Arisha (2017): Analyzing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review [9]Not exploredExplicitly incorporatedNot explored
Kochan & Nowicki (2018): Supply chain resilience: a systematic literature review and typological framework [10]Not exploredExplicitly incorporatedNot explored
Conz & Magnani (2020): A dynamic perspective on the resilience of firms: A systematic literature review [11]Not exploredExplicitly addressedIndirectly incorporated
Fahimnia, B., Sarkis, J., & Talluri, S. (2019): Design and Management of Sustainable and Resilient Supply Chains [12]Partially integratedExplicitly addressedNot explored
Negri et al. (2021): Sustainability and resilience in supply chains: a literature review and research agenda [13]Explicitly addressedExplicitly addressedNot explored
Asif et al. (2020): Adoption of green supply chain management practices: literature review to conceptual framework [6]Explicitly addressedOmittedNot addressed
Kosasih et al. (2023)—Integrated lean-green practices and supply chain sustainability framework [14]Explicitly addressedIndirectly integratedNot explored
Ruel et al. (2024)—Supply chain viability: conceptualization, measurement, and nomological validation [15]Not exploredIndirectly integratedExplicitly addressed
Ivanov (2022, 2023)—Viable supply chain viability ecosystem frameworks [16]Not exploredExplicitly addressedExplicitly addressed
Table 2. PICO framework: description and application in this systematic literature review.
Table 2. PICO framework: description and application in this systematic literature review.
PICO ElementsDescriptionApplication in This Review
Population (P)Target context Supply chains currently lack sustainability and operate under unpredictable disruptions across sectors such as manufacturing, logistics, and distribution, as evidenced by measurable indicators (e.g., frequency of disruption, waste rate, and carbon emissions)
Intervention (I)What are/the Main strategiesGreen supply chain management (GSCM), green practices (GPs), supply chain resilience (SCRES), and viability approaches (SCVs)
Comparison (C)Reference point/benchmarkTraditional supply chains vs. modern green, resilient, and viable supply chains
Outcomes (O)Expected resultsQuantifiable outcomes such as operational metrics (cost to serve, lead time, and service level), environmental indicators (carbon emission, waste reduction), resilience indicators (disruption cost, TTR, TTS), and viability indicators (adaptability rate, continuity, and circularity rate)
Table 3. Main journals and the number of articles included in the review.
Table 3. Main journals and the number of articles included in the review.
JournalNumber of Articles
Sustainability9
Journal of Cleaner Production7
Annals of Operations Research3
International Journal of Production Research3
Logistics3
Journal of Environmental Management2
Journal of Business Logistics2
International Journal of Production Economics2
Journal of Manufacturing Technology Management (JMTM)2
Production and Operations Management2
Transportation Research Procedia2
Alexandria Engineering Journal1
Algorithms 1
Brazilian Journal of Operations & Production Management (BJO&PM)1
Business Strategy and the Environment (Bus Strat Env)1
Cleaner and Responsible Consumption1
Supply Chain Management: An International Journal (SCM)1
Equilibrium Quarterly Journal of Economics and Economic Policy (eq)1
European Management Journal1
Transportation Research Part E: Logistics and Transportation Review1
Industrial Management & Data Systems1
International Journal of Fuzzy Systems1
International Journal of Information Management Data Insights1
International Journal of Physical Distribution & Logistics Management1
International Journal of Physical Distribution & Logistics Management (IJPDLM)1
International Journal of Productivity and Performance Management (IJPPM)1
Journal of Modelling in Management (JM2)1
Journal of Supply Chain Management1
Materials Today: Proceedings1
Supply Chain Management: An International Journal1
Multidisciplinary Science Journal1
The International Journal of Logistics Management (IJLM)1
Operations Management Research1
PLoS ONE1
Procedia Manufacturing1
Production Planning & Control1
Research in Transportation Economics1
Results in Engineering1
Revue internationale P.M.E.1
Mathematics1
Discover Sustainability1
Omega1
Frontiers in Neurorobotics1
International Journal of GEOMATE1
Table 4. Number of papers studied per year.
Table 4. Number of papers studied per year.
YearNumber of Articles
20101
20122
20131
20141
20152
20161
20172
20184
20191
20208
20219
20226
20238
202415
20249
Table 5. List of included studies by journal.
Table 5. List of included studies by journal.
JournalPaper Title
Alexandria Engineering JournalMulti-objective optimization modelling of sustainable green supply chain in inventory and production management [18]
Algorithms Towards Sustainable Inventory Management: A Many-Objective Approach to Stock Optimization in Multi-Storage Supply Chains [19]
Annals of Operations ResearchA robust heuristic approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty [16]
Supply chain viability: conceptualization, measurement, and nomological validation [15]
A multi-period multi-season multi-objective mathematical model for guaranteeing the viability of supply chains under fluctuations: a healthcare closed-loop supply chain application [20]
Brazilian Journal of Operations & Production Management (BJO&PM)The effects of supply chain viability on supply chain performance and marketing performance in case of large manufacturing firm in Ethiopia [21]
Business Strategy and the Environment (Bus Strat Env)Viability of sustainable logistics practices enabling circular economy: A system dynamics approach [22]
Cleaner and Responsible ConsumptionIntegrated lean-green practices and supply chain sustainability framework [14]
Discover SustainabilityGreen supply chain management and SMEs sustainable performance in developing countries: role of green knowledge sharing, green innovation and big data-driven supply chain [7]
Equilibrium Quarterly Journal of Economics and Economic Policy (eq)The model of White Supply Chain Management for sustainable performance in the food industry [23]
European Management JournalA dynamic perspective on the resilience of firms: A systematic literature review and a framework for future research [11]
Frontiers in NeuroroboticsGreen Supply Chain Optimization Based on BP Neural Network [24]
Industrial Management & Data SystemsGreen supply chain management practices and performance [2]
International Journal of Fuzzy SystemsGreen Reverse Supply Chain Models with Fuzzy Stochastic Re-manufacturing Capacity [25]
International Journal of Information Management Data InsightsGreen digital leadership and algorithmic management for sustainable supply chains: A serial mediation model [26]
International Journal of Physical Distribution & Logistics ManagementAntecedents and dimensions of supply chain robustness: a systematic literature review [27]
International Journal of Physical Distribution & Logistics Management (IJPDLM)Supply chain resilience: a systematic literature review and typological framework [10]
International Journal of Production EconomicsCan sustainable supply chain sustain supplier’s operational efficiency? [28]
Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model [29]
A stochastic optimization approach to maintain supply chain viability under the ripple effect [8]
Examining the effects of green supply chain management practices and their mediations on performance improvements [30]
Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems [31]
International Journal of Productivity and Performance Management (IJPPM)An analysis of stock market impact from supply chain disruptions in Japan [32]
Journal of Business LogisticsENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK [33]
Two perspectives on supply chain resilience [34]
Journal of Cleaner ProductionA multi-objective optimization approach for green and resilient supply chain network design: A real-life case study [4]
Adoption of green supply chain management practices through a collaboration approach in developing countries—From literature review to conceptual framework [6]
Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model [5]
Green supply chain management and firm sustainable performance: The awareness of China Pakistan Economic Corridor [3]
The impact of green supply chain management on corporate performance under the full process model: A MASEM analysis based on heterogeneous moderation [35]
The role of customer relational governance in environmental and economic performance improvement through green supply chain management [36]
Why does “green” matter in supply chain management? Exploring institutional pressures, green practices, green innovation, and economic performance in the Chinese chemical sector [37]
Journal of Environmental ManagementEnvironmental uncertainty, supply chain, and stability of sustainable green innovation: Based on micro evidence from energy-intensive enterprises [38]
Exploring the role of managerial green commitment in enhancing sustainability in Congo’s cobalt supply chain [39]
Journal of Manufacturing Technology Management (JMTM)Achieving supply chain resilience: the role of supply chain ambidexterity and supply chain agility [40]
Effects of green supply chain management practices on sustainability performance [41]
Journal of Modelling in Management (JM2) A multi-objective optimization model for a sustainable supply chain network with using genetic algorithm [42]
Journal of Supply Chain Management Dancing the Supply Chain: Toward Transformative Supply Chain Management [1]
Logistics Location Optimization Strategies for Corn Production and Distribution towards Sustainable Green Supply Chain [43]
Modelling a Logistics and Financial Supply Chain Network during the COVID-19 Era [44]
Simulation Model for a Sustainable Food Supply Chain in a Developing Country: A Case Study of the Banana Supply Chain in Malawi [45]
Materials Today: Proceedings Green supply chain management practices implementation and sustainability—A review [46]
Mathematics Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach [47]
Multidisciplinary Science Journal Unveiling the relationships between enablers in resiliently sustainable supply chains using an integrated TISM-Fuzzy MICMAC approach: Study of Indian automobile sector post-COVID-19 [48]
Omega How does customer enterprise digitalization improve the green total factor productivity of state-owned suppliers: From the supply chain perspective [49]
Operations Management Research Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance [50]
PLoS ONE Modeling the supply chain sustainability imperatives in the fashion retail industry: Implications for sustainable development [51]
Procedia Manufacturing Design and Simulation of a Logistics Distribution Network Applying the Viable System Model (VSM) [52]
Production and Operations Management Building Supply Chain Resilience through Virtual Stockpile Pooling [53]
Increasing Supply Chain Robustness through Process Flexibility and Inventory [54]
Production Planning & Control Green supply chain management practices in India: an empirical study [55]
Research in Transportation Economics Viable business models for city logistics: Exploring the cost structure and the economy of scale in a Swedish initiative [56]
Results in Engineering Exploring the synergy between sustainability and resilience in supply chains under stochastic demand conditions and network disruptions [57]
Revue internationale P.M.E. Capacité dynamique de résilience et RSE, l’alchimie gagnante face à la COVID-19? [58]
Supply Chain Management: An International Journal Green supply chain management practices: impact on performance [2]
Supply Chain Management: An International Journal (SCM) Analyzing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review [9]
Sustainability Bridging Environmental Sustainability and Organizational Performance: The Role of Green Supply Chain Management in the Manufacturing Industry [59]
Dependent-Chance Goal Programming for Sustainable Supply Chain Design: A Reinforcement Learning-Enhanced Slap Swarm Approach [60]
Green Supply Chain Management Practices’ Impact on Operational Performance with the Mediation of Technological Innovation [61]
Institutional Pressures, Green Supply Chain Management Practices on Environmental and Economic Performance: A Two Theory View [62]
Integrating Perishables into Closed-Loop Supply Chains: A Comprehensive Review [63]
Lean, Agile, Resilient, Green, and Sustainable (LARGS) Supplier Selection Using Multi-Criteria Structural Equation Modeling under Fuzzy Environments [64]
Re-Designing Business Process Models for Enhancing Sustainability in Spinach Production Through Lean Tools with Digital Transformation [65]
Sustainability Transformation Through Green Supply Chain Management Practices and Green Innovations in Pakistan’s Manufacturing and Service Industries [66]
Sustainable Leadership Practices and Competencies of SMEs for Sustainability and Resilience: A Community-Based Social Enterprise Study [67]
The International Journal of Logistics Management (IJLM) Green warehousing, logistics optimization, social values and ethics, and economic performance: the role of supply chain sustainability [68]
Transportation Research Part E: Logistics and Transportation Review Sustainable supply chain management for perishable products in emerging markets: An integrated location–inventory–routing model [69]
Transportation Research Procedia Characterization and analysis of the economic viability of cycle logistics transport in Brazil [70]
Viable business models for city logistics: exploring the cost structure and revenue streams of fourteen European cases [71]
International Journal of GEOMATE DEVELOPING A ROBUST GREEN SUPPLY CHAIN PLANNING OPTIMIZATION MODEL CONSIDERING POTENTIAL RISKS [72]
Table 6. Core research themes and key insights supported by the literature.
Table 6. Core research themes and key insights supported by the literature.
Main ThemeSub-Themes/CodesKey Insight
GSCM PracticesIEM, Eco-Design, Reverse Logistics, Collaboration, Green Purchasing, and Production.enhance operational and environmental performance, variable short-term costs [3,30,61].
SCRES CapabilitiesRedundancy, Flexibility, Agility, VisibilityProvide for adaptive continuity in the face of disruption [9,10,40].
SCV EnablersDigitalization, Ecosystem Governance, Circular Economy, LeadershipMaintain performance and flexibility over a long period [31,66].
Modeling/OptimizationMulti-objective, Stochastic, Metaheuristic, AI-drivenMeasure and operationalize the costs, emissions, and resilience trade-offs [4,8].
Table 7. Impact of green supply chain management (GSCM) practices on environmental, economic, operational, and social performance.
Table 7. Impact of green supply chain management (GSCM) practices on environmental, economic, operational, and social performance.
GSCM PracticesEnvironmental PerformanceEconomic PerformanceOperational PerformanceSocial Performance
Internal Environmental Management (IEM)Great improvements in emission reduction and regulatory compliance [39]Enhances efficiency and cost savings in the long term [41], but requires high initial implementation costsImproves flexibility and quality [59]Strengthens CSR and employee engagement, building trust and societal value [66]
Green Purchasing (GP)Positive, although very dependent on institutional pressure [37] Mixed: potential long-term cost reductions, but significant initial investment burden [3]Improves supplier coordination [73], though supplier reliability remains variableMostly indirect social effects
Eco-Design (ED)Reduces waste and ensures regulatory compliance [41] High upfront costs, with long-term gains through innovation [3] Stimulates product and process innovation [61,66], though it may slow time-to-marketEnhances corporate image [61]
Green Manufacturing (GM)Reduces resource usage and pollution [46]Improves financial performance when sustained [3], but initial efficiency losses may occurEnhances productivity and quality [61]—
Green Distribution and PackagingReduces logistics-related carbon impact and footprint [41] Supports cost efficiency in transportation [61], but sustainable packaging often increases costsShorter lead times and reduced waste—
Reverse Logistics (RL)Promotes circularity and recycling [47]Generates financial value recovery [61], though collection and transport costs can be highImproves return management [55] Reduces waste and creates recycling-related jobs [23]
Collaboration (Customers and Suppliers)Customer collaboration yields environmental and financial benefits [36] Supplier collaboration shows weaker environmental but stronger financial effects [3]Improves flexibility and resilience [5], though coordination can be resource-intensiveBuilds trust, partnerships, and societal value [67]
Green Information Systems/DigitalizationEnables traceability and emission monitoring [29,49] Reduces hidden costs and enhances GTFP [49], but overinvestment in digitalization may increase costsImproves resilience and visibility [29]—
Green HRM and Knowledge SharingPromotes a pro-environmental culture [7,66]Reinforces sustainability and innovation [66], though training programs are costly and slow to yield returnsStimulates green innovation [7] Enhances employee engagement and ethical standards [67]
Table 8. GSCM practices: patterns and critical contingencies.
Table 8. GSCM practices: patterns and critical contingencies.
GSCM PracticeKey Synthesized PatternContingency
Internal Environmental Management (IEM)The IEM is considered a foundational enabler for advanced GSCM adoption.Its effectiveness relies on top management’s commitment and guidance.
Green Purchasing (GP) Effectiveness depends on different contexts, and positive outcomes could be achievable but not guaranteed. To succeed in adopting GP, this is merely tied to external institutional pressure and supplier readiness.
Eco-Design (ED)Eco-design is the main driver for green products and process innovation. Eco-designing can improve brand image and promote long-term environmental benefits, but this will consequently slow time-to-market.
Collaboration with CustomersThe value is created mutually and by collaboration, leading to shared environmental and economic benefits. The effectiveness requires collaborative decisions, while the company may interfere at certain points (for example, when cost constraints do not allow it).
Collaboration with SuppliersResilience-building patterns improve supply chain robustness and flexibility. Supplier collaboration becomes more difficult in complex or dispersed supply chains.
Reverse Logistics (RL)Reverse logistics is considered a cornerstone for circular economy integration. While it aims to value and reduce waste, it is often challenged by high reverse network costs.
Green DigitalizationIt improves traceability, monitoring, and efficiency. If not well aligned with the core processes, it risks overinvestment and increased system complexity.
Table 9. Key indicators for supply chain resilience measurement.
Table 9. Key indicators for supply chain resilience measurement.
IndicatorPurpose
TTR (Time to Recover)The total period of time needed after an interruption of a supply chain node to regain normal operations [33]
TTS (Time to Survive)The longest duration for which the supply chain can continue to satisfy demand without replenishing after a disruption [54]
Service-level continuityCapacity to sustain desired service levels both during and following interruptions [15]
Carbon intensity of recoveryEnvironmental cost of restoring operations [29]
Liquidity and financial buffersCapacity to provide working capital and cash flow in times of crisis [32]
Table 10. Differentiation between resilience and viability.
Table 10. Differentiation between resilience and viability.
AspectResilienceViability
Time scopeShort-term to medium-termLong-term/continuous
PurposeAbsorb, adapt, and recoverEvolve under prolonged uncertainty
NatureOperational capabilityStrategic capability
OutcomeStability and recoveryLong-term survivability
Table 11. Main trade-offs shaping the viability of green and resilient supply chains.
Table 11. Main trade-offs shaping the viability of green and resilient supply chains.
DomainTrade-OffImplication for Viability
Dispersion vs. emissions [4]The more we increase nodes, the more we ensure the continuity and reduce disruption risks.A high number of nodes considerably raises the CO2 emissions, and this requires a balance between efficiency and sustainability.
Buffers vs. Performance [19,29]Redundancy and safety stocks enhance preparedness and enable functioning under disrupted conditions.High cost and energy burdens exceed benefits, pushing firms outside the viability zone.
Perishables (food/pharma) [45,69]Balancing freshness, CO2 emissions, and delivery cost by implementing an efficient integrated location–inventory–routing (LIR) strategy.Smallholders face infrastructure constraints (e.g., limited warehouses and lack of refrigerated transport), given that location–inventory–routing (LIR) is core to viable operations in perishable sectors.
Energy-intensive industries [38]The green innovation adoption is restricted by regulatory uncertainty and high energy use.The innovation pathways are increasingly destabilized with unclear policies and environmental uncertainty.
Emerging/developing markets [45,67]SMEs rely on frugal digitalization and use community networks and decisive leadership.Under resource constraints, viability is disproportionately shaped by these characteristics.
Table 12. Cornerstones of supply chain viability.
Table 12. Cornerstones of supply chain viability.
Key SCV Dimension Core Insight from the Literature
Viability as a higher-order capabilityViability is no longer a stand-alone operational characteristic but rather a systemic competence that integrates sustainability, resilience, and agility [31].
Structural and ecosystem reconfigurationEcosystem-level adaptation strategies, such as substitution, repurposing, and scalable network designs, are essential to long-term sustainability [15].
Measurement and instrumentation of SCVMulti-dimensional structures that capture structure, learning, redesign, and feedback mechanisms can be used to evaluate viability [15].
Digitalization as a viability enablerDigital technologies emphasize trade-offs in viability-oriented design by improving reaction and recovery but potentially decreasing efficiency when abused [29].
Financial and relational foundations of viabilityOperating under extended stress requires both financial consistency and relational stability, which are bolstered by income diversification, PPPs, and enduring buyer–supplier relationships [35,71].
Table 13. Main modeling paradigms for green–resilient–viable (GRV) supply chains.
Table 13. Main modeling paradigms for green–resilient–viable (GRV) supply chains.
Problem FocusDeterministic ModelsStochastic ModelsRobust OptimizationData-Driven/Adaptive Models
Supply Chain Network DesignFixed-parameter facility placement and capacity planningTwo-stage network design and scenario-based disruption modelingNetwork configuration at its worst, with limited disruptionsAdaptive network reconfiguration made possible by digital twins
Inventory and Sourcing ManagementStatic safety stock and EOQ-based modelsInventory models driven by supply and demand uncertaintiesStrong sourcing and safety stock practicesAI and learning algorithms for real-time inventory management
Production and PlanningProduction and scheduling models that are deterministicPlanning for capacity and stochastic demandSturdy production schedules in the face of parameter uncertaintyPredictive analytics-based adaptive production planning
Transportation and LogisticsPlanning for distribution and fixed routingUncertainty in transit duration or demand while using stochastic routingSturdy routing and distribution of transport capacityDynamic routing with digital platforms and real-time data
Closed-Loop and Circular Supply ChainsDeterministic models for recycling and reverse logisticsModeling stochastic return flowSturdy circular network architectureData-driven lifetime analytics and circular optimization
Table 14. Representative optimization frameworks and main outcomes.
Table 14. Representative optimization frameworks and main outcomes.
StudyModel Type/TechniqueDecision FocusMain Findings and Insights
Hasani et al. (2021) [4]Multi-objective MINLP (SPEA2–ALNS hybrid)Dual sourcing and facility location Dual sourcing and facility dispersion greatly enhance resilience while only slightly increasing CO2 emissions.
Mirzaee et al. (2024) [5]Two-stage stochastic MILPbackup suppliers, safety stock, temporary facilitiesThe optimum cost–resilience trade-offs need combining safety stock and backup providers to achieve an effective balance in a disruptive environment.
Homayouni et al. (2023) [16]Robust heuristic optimizationGreen transport, carbon emission policiesRobust design reduces cost penalties while being viable across multiple regulatory scenarios.
Ahmadini et al. (2021) [18]Fuzzy multi-objective MILP and goal programmingproduction planning and inventory managementFuzzy goal programming enables practical solutions while maintaining constant environmental performance in times of demand unpredictability.
Yun et al. (2020) [47]Hybrid genetic algorithm (closed-loop network)Recycling and reverse logistics Resource recovery and circular flow improve long-term viability via closed loops.
Wang (2022) [24]Neural-network-based optimizationReal-time inventory and transportAI-driven optimization improves decision-making and dynamic recovery during disruption phases.
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Chajae, H.; El Oualidi, M.A.; Hebaz, A.; Mharzi, H. Exploring the Interplay Between Green Practices, Resilience, and Viability in Supply Chains: A Systematic Literature Review. Logistics 2026, 10, 23. https://doi.org/10.3390/logistics10010023

AMA Style

Chajae H, El Oualidi MA, Hebaz A, Mharzi H. Exploring the Interplay Between Green Practices, Resilience, and Viability in Supply Chains: A Systematic Literature Review. Logistics. 2026; 10(1):23. https://doi.org/10.3390/logistics10010023

Chicago/Turabian Style

Chajae, Hamza, Moulay Ali El Oualidi, Ali Hebaz, and Hasna Mharzi. 2026. "Exploring the Interplay Between Green Practices, Resilience, and Viability in Supply Chains: A Systematic Literature Review" Logistics 10, no. 1: 23. https://doi.org/10.3390/logistics10010023

APA Style

Chajae, H., El Oualidi, M. A., Hebaz, A., & Mharzi, H. (2026). Exploring the Interplay Between Green Practices, Resilience, and Viability in Supply Chains: A Systematic Literature Review. Logistics, 10(1), 23. https://doi.org/10.3390/logistics10010023

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