Circular Supply Chain Management Assessment: A Systematic Literature Review

Abstract

In response to escalating global concerns about waste generation throughout the product life cycle, the Circular Economy (CE) has emerged as a central alternative to the dominant linear economic model. The integration of CE principles into supply chain management is manifested in Circular Supply Chain Management (CSCM), offering a novel perspective on supply chain sustainability. Despite the growing research interest in developing CSCM to enhance supply chain sustainability, assessment approaches of this concept are notably absent in the literature. This study addresses this gap by focusing on the assessment and performance measurement of circular practices in the context of supply chains. At first, the research presents a bibliometric analysis to delve into the performance and science mapping of CSCM assessment, providing a comprehensive view of the scientific landscape. Subsequently, a content analysis is then used to identify current assessment approaches, focusing on frameworks, methodologies, barriers, enablers, and CE strategies. The study proposes a conceptual model based on the SCOR framework, including core categories such as enablers (business model, technology, collaboration, design) and results (material, water, energy flows) represented by the Rs strategies. This model contributes to bridging theoretical gaps and guiding practitioners and policymakers in the design, evaluation, and implementation of circular supply chains.

1. Introduction

The accelerating environmental crisis, marked by climate change, biodiversity loss, resource scarcity, and increasing greenhouse gas (GHG) emissions, has intensified the call for sustainable production and consumption systems aligned with the United Nations Sustainable Development Goals (SDGs) [1]. In this context, the circular economy (CE) has emerged as a paradigm that contrasts with the traditional linear take–make–dispose model by promoting the reduction, reuse, recycling, and recovery of resources [2,3]. Its relevance lies not only in improving material efficiency but also in reducing environmental pressures, preventing ecosystem degradation, and enabling long-term resilience in global supply chains [4,5].

To achieve this goal, a cradle-to-cradle approach must be adopted [6], which involves minimizing the use of materials, water, and energy while avoiding the extraction of virgin materials. At the same time, it aims to be restorative and regenerative, maximizing the number of times a material or product can be recovered [7]. This paradigm shift aims not only to eliminate waste, but also to address the additional waste generated by underutilization of resources [8]. Consequently, CE is not only a production approach, but also a new business model that focuses on waste and resource management and is designed to be regenerative from the outset [9,10].

Most CE studies and measurements have focused primarily on the macro and meso levels, relating to regions, countries, and sectors [11]. These approaches are evident in publications by the World Council Business for Sustainable Development [12], such as “Vision 2050: Time to Transform”, which addresses the nine transformations needed to implement the CE. While most CE assessments focus on the macro and meso levels [2], it is worth noting that the literature on CE indicators tends to focus on the firm level rather than supply chain analysis [13], lacking of practical closed-loop solutions [14]. Some CE measurements have even focused on circularity at the product level, as seen in the Product Circularity Indicator and the Material Circularity Indicator [15]. However, the development of measurement systems at the micro level (companies and supply chains) is still in its infancy [16], lacks practical testing [17], and thus does not receive the attention needed to understand its potential and implications [18]. Existing metrics often do not include all material cycles or take a value chain perspective on material flows [19]. Therefore, circular economy requires assessment of the entire value chains [20], becoming relevant to investigate and propose CE frameworks that are oriented towards supply chain management, a concern of policymakers [21] and strategic management professionals in various companies and their supply chains [22].

In the linear supply chain model, companies did not prioritize waste elimination, making the model environmentally unfriendly [23]. As a result, waste generation, environmental degradation, and increased consumption of natural resources become critical issues in a circular supply chain (CSC) [24]. Thus, circular production and supply chains aim to eliminate waste and instead recover the value in their products and materials [25]. In this context, CSC is defined as introducing the concept of CE into supply chain management to improve sustainability and increase resource efficiency [17,26], requiring supply chain operations to merge with CE practices [6].

However, it is crucial to note that the performance indicators used to evaluate CSC are different from those used in traditional supply chains; while the latter focus more on cost and quality issues, CSC indicators focus on CE strategies, resource consumption and recovery throughout the supply chain [27,28]. Therefore, the performance measurement must ensure the transition to circular enterprises and circular supply chains and to improve sustainability and reduce costs [29]. Likewise, circular economy indicators must cover sustainability and technical aspects, including categories related to resources, waste, energy, water, emissions, use phase, transport and packaging, and hazardous substances [30].

Therefore, CSCM involves the integration of circular principles into supply chain management, encompassing both industrial and natural environments. It systematically aims to restore technical materials and regenerate biological resources to achieve a zero-waste goal by introducing comprehensive innovations in business strategies and supply chain operations, starting from product/service inception to disposal and waste management. This process requires the involvement of all stakeholders throughout the life cycle of a product or service, including manufacturers of components/products, service providers, semi-formal-informal recyclers, consumers, and end users [2,31].

Similarly, CSCM involves orchestrating and managing a network of various entities and consumers to achieve economic, environmental, and societal benefits by reducing, maintaining, and recovering resources through restorative and revitalizing cycles that optimize the use of resources until the end of their life [7,32]. This approach requires organizing and synchronizing the functional responsibilities of organizations, including production, marketing, information technology, finance, logistics, and customer service, across all practitioners and business units within a supply chain. The goal is to reduce waste and emissions by implementing circular resource and energy management strategies [1]. In addition to closing material loops, CSCM aims to eliminate the generation of solid, liquid, and gaseous waste, reduce the use of hazardous and toxic chemicals, and operate exclusively on renewable energy sources [27]. As a result, CSCM is considered an innovative approach to resource conservation and reductions in production waste, energy, and GHG emissions in various industrial supply chains [14,33].

In addition, it is crucial to distinguish CSCM from reverse supply chain management and closed loop management, which differ in that CSCM focuses on restorative and regenerative cycles, creating a web of connections between different performance goals [10]. For this reason, the assessment of CSCM needs to recognize the multiple linkages between different performance objectives, simultaneously encompassing circularity, economic, environmental and social aspects [7]. CSCM should also consider traditional supply chain material flows from basic production and reverse logistics and a restorative approach that includes a closed-loop approach for recovery flows and an open-loop approach for cascading flows [34,35].

Unlike linear supply chains, CSCs must be assessed not only in terms of efficiency and profitability but also through their environmental impacts on water, energy, soil, and air systems. Recent literature has highlighted the importance of integrating metrics such as carbon and water footprints, life cycle assessment (LCA), and performance indicators to better capture the trade-offs and synergies between circularity and sustainability [17,36]. Some of these methodologies are oriented towards measuring material circularity that have been developed and disseminated by the Ellen MacArthur Foundation (EMF) [9,37]. Similarly, frameworks have been developed in which CE strategies are linked to material flows [19]. These strategies often revolve around the so-called Rs of the CE. Some have focused on the 3Rs [38], 6Rs [8,32], and even 10Rs [39,40,41].

Despite these advances, significant gaps remain. For instance, Chhimwal et al. [8] introduced a Markovian model to measure material circularity; however, their approach is limited to recycling and assumes constant transition probabilities, restricting its applicability across industries. Calzolari et al. [13] critically reviewed CSCM indicators and found that academia and practitioners prioritize economic and environmental metrics while neglecting social and systemic dimensions. They often rely on reductionist assumptions. Sawe et al. [16] examined people-driven factors in SMEs, emphasizing the importance of leadership, training, and employee participation but not proposing a comprehensive assessment framework. Vegter et al. [17] examined performance measurement systems and showed that most are still in the conceptual stage, lack empirical validation, and fail to capture the interdependencies between circular, economic, environmental, and social objectives. Elia et al. [37] emphasized the role of supply chain integration in advancing circular economy (CE) adoption but did not provide an operational framework for assessing circularity across all flows. More recently, Carissimi et al. [42] pointed out the absence of appropriate KPIs for evaluating CE practices and questioned whether traditional sustainability indicators are adequate for CSC contexts. Taken together, these studies confirm the need for a comprehensive assessment framework that goes beyond fragmented approaches, captures enablers and outcomes, and aligns circular economy (CE) strategies with supply chain processes.

Therefore, there is a need to fill this gap in order to advance the state of the art in CSCM and sustainable development goals, reduce resource use, and positively impact ecological systems [19]. While prior reviews have examined the circular economy from perspectives such as barriers [43], conceptual definitions [2], performance measurement tools [44,45], multidimensional frameworks [46], interdependencies in performance objectives [7], or broader CE assessment tools [47], none have focused specifically on assessment frameworks for CSCM. This paper addresses that gap by systematically analyzing how CSCM has been assessed in the literature, combining a bibliometric mapping of the field with a qualitative synthesis of frameworks, enablers, outcomes, and CE strategies. Accordingly, the purpose of this study is to provide a comprehensive understanding of CSCM assessment and to advance the field by proposing a novel evaluation framework. The specific objectives are: (1) mapping the scientific landscape of CSCM assessment through bibliometric analysis, (2) identifying and categorizing existing frameworks, enablers, outcomes, and CE strategies through content analysis, and (3) integrating these insights into a systematic CSCM assessment framework that aligns the updated SCOR model with enablers and outcomes, which are operationalized through the 10Rs strategies.

The remainder of this article is organized as follows: Section 2 outlines the methodology used in the literature review, including the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and bibliometric data analysis. Section 3 presents the results of the performance analysis and scientific mapping of the reviewed documents, along with the elements, categories, variables, and dimensions derived from the CSCM assessment results. Section 4 presents a proposed conceptual model for CSCM assessment, considering supply chain processes, CE strategies (Rs), and categories of CE enablers and flows. Section 5 discusses the findings. Finally, Section 6 presents the concluding remarks of this study.

2. Methodology

A systematic literature review is a critical method for examining and evaluating specific knowledge relevant to a focused research field. This systematic approach helps to organize detailed and thematic analyses that structure findings related to the adoption and implementation of CSCM assessment practices. Establishing eligibility criteria that are consistent with the research objectives is essential to gathering the essential documents for this review. Consequently, defining clear search methods is critical to ensure the reliability of findings, support robust conclusions, and facilitate effective decision-making [48,49]. This review was conducted in accordance with the PRISMA 2020 guidelines [50], including systematic database searches, screening, eligibility assessment, and reporting. According to the PRISMA guidelines for scoping reviews, this systematic review consists of four phases: identification, screening, eligibility and inclusion [51].

The identification phase, in accordance with PRISMA, involved the use of the globally recognized Scopus and WoS databases due to their extensive indexing and relevance [52]. These databases offer rigorous peer-reviewed coverage, structured metadata, and robust citation tracking to ensure the accuracy and replicability of bibliometric analyses. Other platforms, such as Google Scholar, were excluded due to limitations in quality control, duplication issues, and a lack of standardized metadata. Scopus and WoS were chosen to search for documents related to “circularity or circular economy”, “supply chain or value chain”, and “assessment”. It is important to note that the term “assessment” is often associated with words such as “evaluation”, “metrics”, “index”, “indicators”, and “measurement”, which are not synonymous but have significant similarities. The search equation was designed to align with the objectives of the study, emphasizing the interaction between CSCs and the concept of evaluation. Using the search equation (circular* AND (“supply chain” OR “value chain”) AND (assess* OR evaluat* OR metric* OR indicator* OR index* OR measur*)) within the article title fields, the study conducted this search in mid-July 2025. As shown in Figure 1, this resulted in 114 papers from Scopus and 66 papers from WoS.

The PRISMA screening phase included deduplication, which revealed three duplicates in both Scopus and WoS. This process yielded 112 documents: 49 unique to Scopus, one unique to WoS, and 62 present in both databases. During the eligibility phase, full-text articles were assessed for inclusion or exclusion, and the reasons for each were documented. Two documents from Scopus and two from WoS were excluded because they focused on different topics unrelated to CSCM assessment. These excluded documents covered topics such as cattle manure recycling for pasture fertilization without a SC assessment approach, strategies to mitigate COVID-19 impacts without discussing performance metrics or supply chain assessment, human rights impact assessment, and SDGs for agri-food supply chains. Additionally, two documents from Scopus and WoS were excluded because one was a retraction note, and the other was the respective retracted article. According to PRISMA guidelines, the inclusion phase identifies studies for qualitative and quantitative synthesis. In this case, 106 documents were included: 47 unique to Scopus, one exclusive to WoS, and 58 present in both databases (see Appendix A).

2.1. Quantitative Analysis

Quantitative analysis in a systematic review typically employs two primary sets of analytical methods: performance analysis and science mapping. Performance analysis involves assessing productivity and impact, providing insights into the performance of authors, institutions, or journals. Conversely, science mapping is a relational method that uncovers clusters of knowledge and connections within a given field, allowing researchers to identify important topics, trends, and collaborations. These approaches provide a comprehensive view of the scientific landscape to support informed decision-making in research and academia [53].

Following the collection of documents for quantitative analysis, the next step is to select metrics. In this study, metrics are obtained from the basic methodologies described in the bibliometric analysis toolbox presented by Donthu et al. [53] and Mukherjee et al. [54]. These results are subjected to a comprehensive evaluation using performance assessment and science mapping techniques. This includes the use of metrics related to publications, citations, co-authorship, and co-word analysis to examine the evolution of research approaches within the field of university sustainability assessment. The results of the quantitative analysis serve a variety of practical purposes, including assessing and documenting the productivity and impact of researchers, defining the scope of coverage claims, revealing prevailing social dynamics or hidden biases to guide improvement initiatives, identifying anomalies for further investigation, and assessing relative performance to support equitable decision-making.

For the bibliometric analysis, VOSviewer (version 1.6.19) software was employed to construct and visualize keyword co-occurrence networks, enabling the identification of research clusters and thematic interconnections. In addition, Microsoft Excel (version 2509) was used to compute the collaboration index and other descriptive statistics (distribution of publications by year, type of document, subject area, and country), providing complementary quantitative insights into the CSCM assessment literature.

2.2. Qualitative Analysis

The qualitative analysis to track evolving trends within a field and to identify critical knowledge gaps, shedding light on areas that may have received insufficient attention or remain underdeveloped. However, it is critical for a literature review to identify gaps and explore underexplored areas, while considering the implications of the research for theory development and practical applications. A content analysis approach was applied and, following standard procedures, the selected articles were systematically coded to identify recurring categories related to assessment frameworks, enablers, outcomes, and circular economy strategies. This allowed us to synthesize patterns, highlight commonalities and differences across studies, and derive the core components of the proposed assessment framework.

The contributions of the identified documents were then methodically organized and assimilated to identify the primary components of assessment methodologies for CSCM. These components include the review of assessment frameworks, methodologies used in CSCM assessment, and barriers and risks associated with CSCM. Subsequently, a model for CSCM assessment was formulated based on the evaluation of existing CSCM assessment approaches. This model presents the enablers, CE strategies, and rationale that are essential for integrating prevailing CSCM frameworks. The CE enablers include aspects such as business models, technology, collaboration, and design, while the CE strategies, encompassing the 10 Rs, include resell/reuse, repair, reduce, refuse, remanufacture, refurbish, repurpose, recycle, recover energy, and remine.

The CSCM frameworks used in this research to propose the CSCM assessment model include EMF frameworks, Balance Scorecard (BSC), SCOR model, and Rs frameworks. As comprehensive work, this study incorporates elements consistent with both bibliometric and content analysis methodologies. This includes quantifying publications and citations, examining contributions by country/territory, assessing the distribution of citations across journals and subject areas, and exploring both established and emerging concepts [55].

3. Results

The results of this systematic review are presented in two main parts. First, the bibliometric analysis offers a quantitative overview of the CSCM assessment literature, including publication trends, prominent authors and institutions, sources, and thematic clusters. This analysis maps the evolution and current state of research in the field. Second, a qualitative content analysis examines the main approaches to CSCM assessment in detail, including frameworks, methodologies, enablers, barriers, and circular economy strategies.

3.1. Bibliometric Analysis

The temporal distribution of publications (2009–2025) shown in Figure 2 reveals a clear and accelerating trajectory in CSCM assessment research. After an incipient phase with sporadic contributions between 2009 and 2016 (less than one document per year on average), output increased modestly during 2017–2020 with eight publications. A turning point occurred from 2021 onward, when publication rates rose sharply, with 95 documents (88.8% of the total) concentrated in the period 2021–2025. This exponential growth reflects the consolidation of CSCM assessment as a research priority, driven by global concerns about climate change, resource scarcity, and the transition toward circular and low-carbon supply chains. The trend demonstrates the growing recognition of assessment frameworks as essential tools to evaluate not only economic efficiency but also environmental performance, resource use, and emissions reduction.

Regarding subject areas, the analysis confirms the strong environmental orientation of CSCM assessment. Environmental Science accounts for 22.8% of the publications, followed by Engineering (18.0%) and Business, Management and Accounting (15.4%). These three domains together comprise more than half of the literature, underscoring the multidisciplinary nature of the field. Additional contributions come from Energy (11.4%), Social Sciences (8.8%), and Computer Science (6.3%), while the remaining 17.3% is distributed across seven other areas. This distribution reflects the dual character of CSCM assessment: on the one hand, grounded in environmental and technical evaluation (e.g., life cycle assessment, resource flows, emissions metrics); and on the other, enriched by managerial, social, and computational approaches that address decision-making, stakeholder engagement, and the integration of Industry 4.0 tools. The prominence of Environmental Science as the leading domain demonstrates that CSCM assessment is primarily framed as a response to ecological challenges rather than as a purely operational efficiency exercise.

Furthermore, most of the retrieved documents correspond to scientific articles (82.1%), followed by conference papers (8.5%), reviews (6.6%), and book chapters (2.8%). This predominance of journal articles reflects the consolidation of CSCM assessment research within high-impact scholarly publications, while reviews and book chapters provide complementary conceptual and methodological insights. In addition, the analysis shows that 43.4% of the surveyed documents offer some form of open access (gold, green, bronze, or hybrid), while the remaining 56.6% require subscription or payment for full access. This distribution highlights both the increasing trend toward open science and the persistent barriers to accessibility within the field of CSCM assessment, suggesting opportunities to enhance the dissemination and availability of research outputs for broader audiences.

Further analysis of collaboration revealed that 103 of the documents reviewed were co-authored, representing 97.2% of the total sample, while only 3 documents (2.8%) were attributed to a single author. In addition, the cumulative number of authors involved in multi-authored works amounted to 406, resulting in a collaboration index of 3.94. This indicates that, on average, each collaborative document in this domain involves nearly four authors. These findings underscore the importance and breadth of CSCM assessment within the scholarly community and highlight the field’s reliance on collaborative research, which brings together diverse perspectives, methodologies, and contributions.

The literature on CSCM assessment has been disseminated across 62 sources, with a strong concentration in high-impact journals, most of them ranked in the first quartile (Q1) of the Scimago Journal Rank.

Table 1. Primary sources in CSCM assessment.

Journal	Publisher	Max Quartil (Scimago)	Docs.
Journal of Cleaner Production	Elsevier	Q1	14
Business Strategy and the Environment	Wiley	Q1	6
Resources, Conservation and Recycling	Elsevier	Q1	5
Sustainable Production and Consumption	Elsevier	Q1	5
Sustainability (Switzerland)	MDPI	Q1	4
Frontiers in Sustainability	Frontiers Media	Q2	3
Science of the Total Environment	Elsevier	Q1	3
Production Planning and Control	Taylor and Francis	Q1	3

shows that the leading source is the Journal of Cleaner Production (Elsevier), with 14 publications, which has become the primary outlet for studies linking supply chains, circularity, and environmental performance. Other journals with significant contributions include Business Strategy and the Environment (Wiley), Resources, Conservation and Recycling (Elsevier), and Sustainable Production and Consumption (Elsevier). Additional relevant outlets are Sustainability (MDPI), Frontiers in Sustainability (Frontiers Media), Science of the Total Environment (Elsevier), and Production Planning and Control (Taylor & Francis). The prevalence of these journals demonstrates that CSCM assessment is primarily addressed within the broader sustainability and environmental management discourse, ensuring both academic credibility and practical applicability.

At the publisher level, Elsevier dominates with 50 documents (47% of the total), followed by Springer with 21 documents (20%), and MDPI with 8 documents (8%). Other important contributors include Emerald and Wiley (7 documents each, 7%), as well as Taylor & Francis (5 documents, 5%) and Frontiers Media (4 documents, 4%). This concentration reflects Elsevier’s central role in shaping the discourse on CSCM assessment, while also evidencing a notable diversification across publishers, which enhances the visibility and multidisciplinarity of the field.

The bibliometric analysis identified 356 authors, including one author responsible for seven documents (Kant R.), one author responsible for six documents (Lahane S.), one author responsible for four documents (Kazancoglu), one author responsible for three documents (Mangla), and 37 authors responsible for two documents each, representing a significant cohort of researchers focused on CSCM assessment. As shown in

Table 2. Influential authors in CSCM assessment.

Authors	Affiliation	Country	Documents in Databases		Total Docs.
			Scopus	Scopus and WoS
Kant R.	S. V. National Institute of Technology	India	3: [6,56,57]	4: [24,45,58,59]	7
Lahane S.	S. V. National Institute of Technology	India	3: [6,56,57]	3: [24,45,58]	6
Kazancoglu Y.	Yaşar Universitesi	Turkey	1: [60]	3: [40,61,62]	4
Mangla S.K.	University of Plymouth O P Jindal Global University	United Kingdom India	1: [60]	2: [22,63]	3

, Kant and Lahane, co-authors on six documents, emerge as prominent authors in the field of CSCM assessment, accounting for 6.6% of the scientific output in this domain.

In terms of author relationships, Lahane and Kant, affiliated with S. V. National Institute of Technology (India), co-authored six published documents. These authors use the Pythagorean fuzzy DEMATEL method to assess the barriers and measure the performance of CSC implementation [6,24,56]. They also evaluate and rank solutions to mitigate CSC risks [58], and collaborate with other authors on a comparative case analysis related to a performance measurement framework for CSC implementation [45], as well as an evaluation and ranking of CSC implementation enablers [57]. Another author with a substantial number of publications is Kazancoglu from Yaşar Universitesi (Turkey), who collaborates with Kayikci on the assessment of smart CSC readiness and maturity level of small and medium enterprises [61] and on the performance evaluation of reverse logistics in food CSCs [60]. Kazancoglu is also collaborating with other authors on supply chain management performance evaluation based on CE [62] and performance measurement based on a circular SCOR model during COVID-19 [40]. Mangla, who contributes three documents to the research topic, also participates in the study presented by Kazancoglu [60].

Figure 3 illustrates the scientific contributions by country/territory, with India leading with 26 documents. The United Kingdom follows with 18 documents, while Italy ranks third with 15 documents. Other notable contributors are China (11 documents), Iran (8 documents), the United States (8 documents), as well as Germany, Spain, and Turkey (7 documents each). Additional relevant contributions come from the Netherlands (5 documents), France, Sweden, and Taiwan (4 documents each), and Belgium, Indonesia, Lithuania, Pakistan, and Vietnam (3 documents each). Several other countries, including Austria, Brazil, Canada, Greece, Malaysia, Norway, Poland, Portugal, Serbia, and Switzerland, contribute with 2 documents each, while a long tail of nations, including Australia, Mexico, Peru, South Africa, South Korea, and others, contribute one publication each.

At the regional level, Europe dominates with 82 documents (77.4%), followed by Asia with 67 documents (63.2%), confirming their central role in CSCM research. North America accounts for 11 documents (10.4%), while South America (5 documents, 4.7%), Africa (2 documents, 1.9%), and Oceania (1 document, 0.9%) show emerging but still limited participation.

This concentration suggests that the scientific development of CSCM assessment is still shaped by specific regional contexts, potentially narrowing the diversity of perspectives and case applications available in the literature. Specifically, it reflects the increasing engagement of developing regions in advancing the circular supply chain agenda. These findings reveal imbalances in the global research landscape, where frameworks and methodologies may primarily reflect the priorities and conditions of Europe and Asia. They also underscore the need for CSCM assessment frameworks that are versatile and adaptable across different geographies, including emerging economies where implementation challenges and enablers may differ.

In the bibliometric analysis,

Table 3. Prominent institutional affiliations.

Affiliation	Docs	% Docs *
S.V. National Institute of Technology (India)	8	7.5%
University of Tehran (Iran)	4	3.8%
Yaşar Üniversitesi (Turkey)	4	3.8%
The University of Sheffield (United Kingdom)	3	2.8%
Università degli Studi di Catania (Italy)	3	2.8%
Università degli Studi di Palermo (Italy)	3	2.8%
Università del Salento (Italy)	3	2.8%
Allameh Tabataba’i University (Iran)	3	2.8%
O.P. Jindal Global University (India)	3	2.8%
Sheffield University Management School (United Kingdom)	3	2.8%

* Documents involving one or more authors with their respective institutional affiliations.

shows the institutions with the highest number of document affiliations. The S.V. National Institute of Technology (India) stands out with 8 associated documents (7.5%), mainly linked to the contributions of Kand and Lahane. Several other institutions follow with a notable presence, including the University of Tehran (Iran, 4 documents, 3.8%) and Yaşar Üniversitesi (Turkey, 4 documents, 3.8%), where Kazancoglu has been particularly active. In Europe, several universities report three documents each: the University of Sheffield (United Kingdom), the Università degli Studi di Catania (Italy), the Università degli Studi di Palermo (Italy), and the Università del Salento (Italy). Similarly, Allameh Tabataba’i University (Iran), O.P. Jindal Global University (India), and the Sheffield University Management School (United Kingdom) also contribute three documents each, representing 2.8% of the total output per institution. As a result, India takes a leading position in CSCM assessment publications, while also highlighting significant academic activity in Iran, Turkey, the United Kingdom, and Italy. The presence of multiple institutions from diverse regions underscores the global and multidisciplinary nature of this research field, evidencing strong collaborations and the emergence of centers of excellence driving the development of CSCM assessment worldwide.

The topic trend map in Figure 4 shows the evolution of the most recurrent keywords in three time periods: 2009–2020, 2021–2023, and 2024–2025. These intervals were selected to ensure a relatively balanced distribution of documents and to highlight the progressive consolidation of research themes in CSCM assessment. The diagram illustrates how topics have emerged, grown, or declined in relative importance across periods. During the first period (2009–2020), research focused mainly on foundational themes such as Circular Economy, Supply Chains, Life Cycle Analysis, and Environmental Impact, indicating an early exploration of circular principles within supply chain contexts. From 2021 to 2023, the field expanded significantly, with Circular Supply Chain and Sustainable Development gaining prominence, while methodological approaches such as multi-criteria decision-making (MCDM), Fuzzy Approach, and Cost Analysis began to appear. This stage reflects a transition from conceptual discussions to quantitative assessment frameworks.

In the most recent period (2024–2025), the consolidation of methodological approaches is evident. MCDM becomes one of the leading keywords, while Decision-Making and Indicators maintain steady growth, reflecting the increasing focus on developing and applying decision-support and performance measurement tools for CSCM. At the same time, Environmental Impact, Recycling, Sustainability, and Life Cycle Analysis remain strong, confirming the sustained relevance of environmental performance in circular assessments. Conversely, topics such as Performance Measurement and Plastics show a decline, suggesting a thematic shift toward integrated frameworks rather than isolated application contexts. The trend analysis demonstrates the field’s evolution from conceptual foundations to mature, data-driven assessment approaches aligned with circular supply chain management.

The co-occurrence analysis, performed with VOSviewer (Figure 5), consolidated 858 keywords into 28 terms surpassing the minimum occurrence threshold of five. These clusters collectively reflect the conceptual and methodological foundations of the CSCM assessment literature. The resulting map positions circular economy (Cluster 1—red, 12 items) as the central and indispensable node, strongly connected to circular supply chain, supply chain management, sustainable development, decision-making, MCDM, and risk assessment. This cluster represents the strategic and managerial dimension of circular supply chains, emphasizing performance evaluation, multi-criteria decision-making methods, and the integration of sustainability into supply chain strategy. The frequent co-occurrence of MCDM and risk assessment underscores the growing use of hybrid quantitative approaches to handle uncertainty in CSCM evaluation.

Cluster 2 (green, 10 items) includes life cycle, life cycle assessment, recycling, economic aspect, value chains, and sensitivity analysis. This cluster captures the operational and analytical dimension, where performance indicators and life-cycle-based tools are applied to quantify environmental and economic impacts. The strong links among life cycle, recycling, and economic aspect indicate that CSCM research is heavily grounded in assessing material flows, recovery processes, and efficiency gains associated with circular strategies. Cluster 3 (blue, 5 items) connects sustainability, environmental economics, waste management, fuzzy mathematics, and food supply. This cluster represents the environmental and modeling dimension, reflecting efforts to integrate environmental economics and mathematical modeling into CSCM applications, particularly in sectoral contexts such as agri-food and waste management.

Altogether, the topic landscape reveals that circular economy, supply chain, and sustainable development function as the central bridging nodes linking the three clusters. The connections across clusters indicate an increasing convergence between conceptual models of circularity, quantitative assessment methods (e.g., MCDM, LCA), and sector-specific applications. This structure highlights the multidisciplinary nature of CSCM assessment, combining sustainability science, operations management, and environmental modeling to develop comprehensive evaluation frameworks.

3.2. Components of Assessment Approaches from the Literature Review

The results of the literature review allow for the identification of the most used frameworks and methodologies for CSCM assessment, as well as the most common barriers and risks to its implementation.

3.2.1. Assessment Frameworks

The review reveals that the most widely applied evaluation frameworks in CSCM assessment are based on performance indicators, often configured as hybrid systems that integrate multiple dimensions. Among these, life cycle–based approaches dominate the literature [36]. For instance, Noya et al. [64] evaluated the environmental performance of a traditional pork supply chain in Catalonia, identifying feed production and transport as critical stages. Similarly, Nikkhah et al. [65] analyzed the circularity of a food waste valorization system (olive oil refining) using LCA, where natural gas consumption was found to be the largest contributor to impacts. Applications in the olive oil sector are also recurrent: Cinardi et al. [34] examined CE strategies through LCA, while Spina et al. [66] and Spada [67] employed LCA and life cycle costing (LCC) approaches, respectively, to establish circular practices.

Beyond agri-food, Josa and Borrion [68] applied LCA to cement, concrete, and reinforced concrete supply chains to examine decarbonization strategies, and Hammar et al. [69] assessed the environmental impacts of a circular textile value chain, supported by the circular footprint formula to account for recycling, energy recovery, and waste generation. Nguyen et al. [70] extended life cycle thinking to the biomass sector, incorporating environmental, economic, and social impacts across the supply chain. Other applications of LCA include the plastic packaging sector [71], additives in food [72], reusable plastic bottles in equilibrium models that compare single-use and reusable alternatives [73], household appliances such as washing machines [15], direct and indirect emissions from steel production [74], water management in agri-food systems [75], and material flow analysis in the aluminum industry [8]. These studies confirm the versatility of LCA in quantifying environmental pressures at different supply chain stages.

The literature shows other frameworks linked to sustainability dimensions with performance indicators. For example, by Calzolari et al. [76], Kumar et al. [63] and Primadasa et al. [77] integrated economic, social, and environmental dimensions, complemented with CE indicators, to operationalize CSCM metrics. Halter et al. [30] systematized CE indicators at the product and company levels across economic, social, environmental, and technical categories. Similarly, Primadasa et al. [78] proposed a CSC indicator framework for the palm oil industry with categories such as exploitation and exploration, while Nguyen et al. [70] classified sustainability and circularity indicators for biomass supply chains into environmental quality, resource depletion, social and economic sustainability, and circularity. Other studies expand the three classical sustainability dimensions by incorporating governance, policy, and strategic-technological aspects [20,61]. However, the social dimension remains the least developed [36], as highlighted by Hidalgo-Carvajal et al. [18], who proposed classification schemes for social performance in CSCs, identifying communication gaps and limited stakeholder engagement as persistent challenges.

A third group of works adapts strategic frameworks. Some studies rely on the BSC, with Saroha et al. [32] expanding the traditional four dimensions (financial, customer, internal, innovation/learning) to include environmental, social, and cost dimensions aligned with sustainability goals. Likewise, Lahane et al. [45] extended the BSC in the manufacturing sector to include organizational, operational, logistical, technical, and marketing perspectives. Parallel to this, frameworks inspired by the SCOR model have been increasingly adapted to CE. Vegter et al. [79] reviewed SCOR processes (Plan, Source, Make, Deliver, Return, Enable) and proposed additional processes (Use, Recover) directly linked to CE strategies such as repair, reuse, remanufacturing, refurbishing, upcycling, recycling, and downcycling. Ozbiltekin-Pala et al. [40] applied the SCOR framework with 17 performance indicators, prioritizing demand fulfillment rate, while Jain et al. [27] used 20 indicators, focusing on resource consumption and environmental impacts rather than costs or production metrics.

Another relevant set of studies draws on frameworks proposed by the EMF. These include the butterfly model distinguishing biological and technical flows [9] and material circularity indicators [15,37]. To a lesser extent, alternative tools have been explored. Lindahl et al. [80] applied the Green Performance Map to enhance manufacturing circularity, while Mohanty et al. [81] emphasized economic sustainability through metrics such as total revenue, net present value, and internal rate of return, complemented with CO₂ emissions as an environmental parameter.

As a result, LCA frameworks remain dominant, supported by their flexibility to capture environmental impacts across supply chains, while sustainability indicator systems broaden the analysis beyond the triple bottom line to include governance, policy, and technological aspects. Strategic frameworks such as BSC and SCOR demonstrate increasing adaptability to CE contexts, while EMF models and alternative tools offer complementary perspectives. Collectively, these findings highlight both the richness and fragmentation of current approaches, underscoring the need for integrated frameworks that systematically incorporate environmental, economic, social, and governance dimensions to advance effective and sustainable CSCM practices.

3.2.2. Methodologies for CSCM Assessment

The methodologies identified in the literature are diverse and often hybrid in nature, typically intertwining fuzzy logic approaches, MCDM methods, hierarchical analyses, and expert judgment. This methodological richness reflects the complexity of CSCM assessment, where uncertainty, interdependence, and competing criteria must be addressed simultaneously. Fuzzy-based methods are particularly prominent. Pythagorean fuzzy approaches have been applied by Lahane and Kant [6,24,56] and Vya and Yadav [26] to support decision-making under uncertainty. Evangelista et al. [82] employed a Fermatian fuzzy set framework to reduce epistemic uncertainties in judgment processes, while Saraji and Streimikiene [1] applied fuzzy techniques to identify influential indicators related to eco-design, sustainable procurement, circular supplier selection, logistics, and end-of-life processes.

Hybrid approaches include fuzzy Delphi applications: Dolatabad et al. [83] employed Institutional Fuzzy Delphi (IFD) in the hospital supply chain sector, while Kurrahman et al. [84] combined fuzzy Delphi, entropy weighting, and exploratory factor analysis to construct a hierarchical LCA structure. Other examples include Gray Delphi–DEMATEL–ANP for risk identification in CE strategies [85], fuzzy AHP for supply chain indicator selection and risk control [29,86], and spherical fuzzy AHP for barrier assessment in the battery industry [87]. More recently, advanced fuzzy models such as q-Rung Orthopair Fuzzy Sets (q-ROFS) have been combined with CoCoSo and CRITIC to enhance prioritization [88]. Majiwala and Kant [59] integrated SWARA and CoCoSo to evaluate Industry 5.0 barriers, while Saraji and Streimikiene [1] applied SWARA-COPRAS with picture fuzzy sets to weight indicators and rank manufacturing sectors.

Among classical MCDM methods, the Analytic Hierarchy Process (AHP) is the most widely adopted. It has been used to weigh sustainability dimensions and key performance indicators [63], which were classified in several levels or categories like financial value customer service, costs and benefits, business process and environmental performance [89]. AHP is also used to evaluate risks and enablers [57,58], prioritize CE challenges and barriers [90,91], and assess technological maturity [92]. Applications extend to performance indicator weighting [26,40,86,93]. The Best-Worst Method (BWM) has also gained relevance, as demonstrated by Yadav et al. [22,94], Amiri et al. [33] and Singh and Gupta [95] for prioritizing CSC enablers and barriers, and by Krstić et al. [96] for risk management in agri-food chains. Hybrid approaches include BWM combined with CoCoSo [97], fuzzy inference systems [98], or ISM-MICMAC to classify indicator interdependencies [78]. Dorfeshan et al. [99] argued that BWM is superior to AHP due to fewer comparisons and higher consistency. Other relevant MCDM methods include TOPSIS for critical factor evaluation [100,101] and DEMATEL for cause–effect analysis [6,16,24,56,83,85,92,102,103].

Expert judgment is also a recurrent element, often combined with MCDM methods. Panels of experts have been used to validate indicators and criteria in CSCM studies [23,33,57,63,82]. Other methodological innovations include PROMETHEE II, applied by Nguyen et al. [70,104] alongside entropy weighting, Data Envelopment Analysis (DEA) for efficiency assessment [14,105], and Structural Equation Modeling (PLS-SEM) for testing causal relationships [3]. Other specific methods such as emergy analysis have been used for the environmental assessment of CCS [106]. More recently, artificial intelligence has been proposed to identify value-adding variables in circular business models [107], while Hasan et al. [108] developed mathematical optimization for closed-loop supply chains, complemented by a circular index proposed by Rabta [109]. Similarly, Karayilan et al. [110] proposed linear, single-objective optimization models to maximize environmental benefits and circularity in European plastic waste supply chains.

As shown above, CSCM assessment methodologies span from fuzzy logic and expert-based approaches to advanced MCDM techniques and emerging AI-driven models. AHP and BWM stand out as dominant tools for weighting indicators and prioritizing risks, while hybrid methods (fuzzy–Delphi–DEMATEL combinations) highlight the trend toward integrated approaches. This diversity demonstrates methodological maturity, but also fragmentation, as there is no standardized protocol for integrating environmental, social, and economic criteria. Future research should focus on consolidating methodological practices while expanding the integration of environmental indicators such as GHG emissions, water use, and biodiversity impacts to ensure robust CSCM assessments.

3.2.3. CSCM Barriers and Risks

The literature reveals a wide range of barriers to CSCM implementation, which vary depending on regional, industrial, and organizational contexts. At a general level, Calzolari et al. [76] identified technological limitations, institutional constraints, consumer acceptance of used products, and supply chain visibility as major challenges for large European multinationals. In developing countries such as India and Turkey, lack of top management support and commitment has emerged as the most significant barrier [56,87]. Financial and economic obstacles are also recurrent: Amiri et al. [33] reported financing and technological constraints in Iranian industries, while Saroha et al. [32] emphasized the high cost of environmentally friendly materials in Indian manufacturing. In pharmaceuticals, Kharat et al. [91] highlighted barriers related to technology, investment returns, regulations, stakeholder management, and corporate strategy.

Other studies provide sector-specific insights. Thompson et al. [111] identified the dismantling process of lithium-ion cells as a neglected barrier during the design phase. In Pakistan’s fast-moving consumer goods sector, Shaikh et al. [23] and Lahane and Kant [24] noted a lack of training, knowledge, information, and managerial commitment. Shang et al. [88] reported that limited knowledge of Industry 4.0 and circular approaches, together with data security concerns, hampers technological integration. Singh and Gupta [95] linked barriers in low-carbon technology adoption to weak regulatory frameworks and the absence of advanced clean technologies. Regulatory and policy gaps also appear in other contexts: Evangelista et al. [82] observed restrictions in waste collection systems, while Radavičius et al. [101] pointed to inconsistent legislation, low collaboration in value chains, and uncertain recycling volumes in Europe. Finally, Hossain et al. [112] emphasized material-specific challenges in plastic recycling, such as variability in plastic types, lack of collection for certain materials, and limited recovery facilities requiring long-distance transport.

Alongside these barriers, CSCM also entails multiple risks, spanning environmental, economic, social, logistical, and operational dimensions [62,92,113,114]. Lahane and Kant [58] categorized them into operational and technological risks, product recovery risks, supply–demand uncertainties, and broader environmental, economic, and social challenges. Within these categories, common risks include financial limitations and sourcing constraints (economic risks), regulatory instability and lack of support programs (environmental risks), and managerial or policy-related deficiencies (social risks). Afroozi et al. [85] identified pollution, waste mismanagement, and program deficiencies as critical risks in lithium-ion battery CSCs. Similarly, Saroha et al. [32] noted risks tied to training costs, the growing need for knowledge dissemination, and the high cost of eco-friendly materials.

In agri-food contexts, Krstić et al. [96] highlighted risks linked to product characteristics, logistics, and management, while Ramos et al. [103] added contamination, collaboration challenges, and farm-level growth dynamics. Chhimwal et al. [8] further pointed to the back end of CSCs as a source of risk propagation during CE implementation. Social risks are equally relevant: Hidalgo-Carvajal et al. [18] emphasized externalities such as lack of stakeholder feedback and inadequate information flows, while Bathrinath et al. [113] advocated for closed-loop practices to reduce risks associated with traditional linear models. Finally, Tietz et al. [100] stressed the importance of education and community engagement campaigns to mitigate adoption barriers and long-term risks. To address these risks, it is necessary to identify potential risks within supply chain management to proactively prevent or mitigate them. This collaborative effort within the supply chain aims to reduce the vulnerability of the entire supply chain. Consequently, such actions could ensure uninterrupted operations, strengthen market share, and increase overall competitiveness [86].

Then, barriers to CSCM span from technological and financial constraints to governance, regulatory, and cultural challenges, with significant variation across regions and industries. Risks, in turn, reflect the systemic vulnerabilities of circular practices, including financial limitations, regulatory uncertainty, and social externalities. Addressing these issues requires integrated risk management and proactive mitigation strategies, with a strong emphasis on governance, collaboration, and stakeholder engagement to ensure successful CSCM adoption.

4. Proposed Model for CSCM Assessment

Existing frameworks and models for CSCM assessment display considerable diversity in scope and methodology, yet they converge on the objective of delivering comprehensive performance measurement and evaluation. Jain et al. [27] and Veloso et al. [28] highlight that circular supply chains must integrate decision-making across strategic, tactical, and operational levels, proposing frameworks that incorporate indicators aligned with each level. Complementing this perspective, Ozbiltekin-Pala et al. [40] advance an assessment framework grounded in the SCOR model dimensions and CE principles, capturing the holistic transition from linear supply chains to circular configurations. Likewise, Kurt et al. [115] develop an indicator framework designed to extend product life cycles, conserve energy, materials, and labor, and reduce pollution, while integrating diverse supply chains into a unified evaluative approach.

Other contributions adapt balanced performance frameworks to the circular context. Saroha et al. [32] and Lahane et al. [45] expand the traditional BSC to include seven and six perspectives, respectively, addressing both strategic and operational aspects of CSCM. Kazancoglu et al. [62] similarly propose a model comprising six dimensions—environmental, economic, operational, logistics, organizational, and marketing—thus widening the evaluative spectrum. Lee et al. [29] extend this effort by analyzing CE indicators and suggesting exemplary CSC indicators across five circularity dimensions: monetary, energy and environmental, material, time, and efficiency, tailored to actors such as producers, collectors, recyclers, and users.

Recent studies introduce sectoral and readiness-based frameworks. For instance, McDaid et al. [98] assess CSC readiness in the dairy industry through dimensions such as financial and economic resources, technology and infrastructure, environmental impacts, market and social aspects, supply chain and operations, management and organizational capacity, human capital, and government and policy. Vegter et al. [7,17] propose measurement systems oriented toward resource efficiency, emphasizing minimal consumption of materials, water, and energy, while maximizing recovery streams. In parallel, the Ellen MacArthur Foundation (EMF) [116] distinguishes between strategic enablers—which capture organizational commitment to circularity—and operational outcomes, represented by circular flows and the application of CE principles.

Collectively, these approaches underscore the importance of CSCM frameworks that combine strategic intent and operational performance, ensuring that enablers (policies, governance, management practices) translate into measurable circular outcomes (recovery flows, resource efficiency, emission reductions). Such dual orientation forms the foundation for the model proposed in this study.

4.1. Enablers

Despite the multiple benefits associated with CSC adoption, its implementation in emerging economies remains uncertain, making it necessary to identify and prioritize the enablers that facilitate transition [43]. Lahane et al. [57] categorize CSC enablers into organizational, operational, strategic, environmental and regulatory, economic, social, and technological or infrastructure dimensions. Among these, environmental and regulatory factors emerge as the most critical, reflecting the urgency of addressing climate change pressures and ecological resource scarcity. Similarly, Kurrahman et al. [84] emphasize eco-design, information management systems, risk assessment, and circular strategy enhancement as key levers, while Yadav et al. [94] classify CE enablers into strategy and policy, technology, processes, management, and human resources.

A recurring theme in the literature is that collaboration and coordination across the supply chain are essential. Bakkal and Kabadayi [87] argue that the successful adoption of CE principles requires active cooperation among independent companies. Expanding this perspective, Khedmati-Morasae et al. [117] propose a taxonomy of CE enablers, encompassing interventions at the design, business model, network, and systemic levels. McDaid et al. [98], in their readiness framework, highlight technology, infrastructure, and financial-economic conditions as decisive categories for CSC adoption. In the context of Indian manufacturing organizations, Ganguly and Farr [118] identified the most significant enablers for managing a CSC as an organization’s knowledge-sharing capabilities, organizational structure, and support from top management. Vyas and Yadav [26] confirm this finding, adding that management and strategy, organizational and human resources, technological capabilities, environmental pressures, and individual commitment all play a role in enabling CSCs.

Likewise, innovation-oriented and design-driven perspectives reinforce the centrality of systemic change. Bressanelli et al. [119] highlight design, business models, collaboration, and digitalization as primary enablers and levers of CSC, a view echoed by Howard et al. [9], who position design and business models as the foundation, complemented by collaboration and reverse channels that govern material, product, and service flows. These contributions illustrate that CSC enablers operate across multiple levels, from technical and organizational capabilities to systemic, collaborative, and policy-related drivers, all of which must be aligned to facilitate effective CSCM adoption.

4.1.1. Business Model

The successful integration of circular design principles into business models should foster increased circulation of resources, extended product lifespans, reduced consumption of virgin products, and enhanced production efficiency. These models often prioritize strategies such as product life extension, product-as-a-service schemes, sharing platforms, and closing loops for products that cannot be directly reused without remanufacturing [19,120].Several authors underscore the strategic role of business models in CSC adoption. Kurt et al. [115], Kayikci et al. [61], and Lahane et al. [45] argue that CSC requires a strong strategic foundation that incorporates diverse variables beyond purely operational aspects. Howard et al. [9] explicitly position the business model as a strategic component of CSC, intrinsically linked to waste and resource management. Along similar lines, Cavicchi and Vagnoni [10] conceptualize CE as a closed-loop model that directly interacts with multiple sustainability dimensions.

From a competitive standpoint, Chhimwal et al. [8] suggest that CSC-oriented business models can serve as a source of advantage for firms implementing circular practices. Likewise, Jain et al. [27] identify the business model as one of the central dimensions of CSC, reinforcing its role as an indispensable enabler. Taken together, these perspectives indicate that the business model is not only an operational tool but also a strategic enabler of CSCM. It shapes how firms approach design, production, consumption, and resource recovery, ultimately determining the depth and effectiveness of circular economy integration across supply chains.

4.1.2. Technology

According to the EMF [116], technology and innovation are essential enablers for CSCM. The literature consistently highlights the importance of strengthening the technological dimension to overcome CSC challenges [105]. However, successful adoption requires more than technical capability since it depends on organizational support and managerial commitment. Without top management interest, personnel are unlikely to embrace new technologies or practices [23]. Thus, technology should be seen not only as an operational tool but also as a strategic enabler that ensures feasibility, scalability, and a clear path toward technological maturity [92,120]. The rise of Industry 4.0 provides a fertile ground for CSC innovation. Yadav et al. [22] propose a framework based on Industry 4.0 solutions, recognizing their transformative potential for manufacturing supply chains. Key technological enablers in this context include mass customization and big data-driven decision-making systems, which enhance equipment flexibility and responsiveness [90,121]. Supporting digital infrastructure, particularly information systems for product tracking and lifecycle management, has also been identified as critical for CSC implementation [97].

Technology also plays a crucial role in advancing environmental sustainability goals. Digital transformation, combined with effective management practices, enables firms to progress toward carbon neutrality [122]. Blockchain-based enablers, for instance, improve governance, strengthen data security and knowledge training [92], and enhance traceability and transparency in CSCs [123]. These capabilities allow for innovations such as digital product passports and standardized technical data sheets, which help optimize costs, ensure compliance with environmental regulations, and increase supply chain efficiency [97,101]. Business intelligence and IoT technologies provide real-time monitoring, predictive analytics, and optimization tools that improve sustainability performance across industries [5].

In renewable energy supply chains, IoT integration enhances monitoring, control, and operation, leading to reduced energy consumption, lower maintenance costs, and increased efficiency [124]. Likewise, data analytics and integration support the transition to renewable raw materials, promote product-service systems, and enable revenue growth through remanufacturing and service-based business models [125]. Consequently, technology is a multifaceted enabler of CSCM. It not only drives operational efficiency and circularity but also fosters organizational transformation by aligning digitalization with environmental, strategic, and business objectives.

4.1.3. Collaboration

Collaboration among stakeholders is a cornerstone of CSCM, as it enables the optimization of materials, products, and services across the entire supply chain. Considering a multi-stakeholder approach and participatory mechanisms directly contribute to achieving sustainable development strategies [126]. Effective collaboration facilitates eco-innovation and technological performance, directly enhancing circularity outcomes [127]. Conversely, the lack of collaboration is repeatedly identified as a critical barrier to CSC implementation [23], despite being one of the less studied dimensions in the literature [9].

A key challenge is the absence of performance measurement systems that capture the interdependencies among supply chain actors [7]. This gap in stakeholder management metrics limits the ability to identify effective collaborative actions and avoid unintended consequences, constituting a major barrier to CSC adoption [88]. Closing supply chain loops requires a clear understanding of interrelationships among stakeholders [60], effective supplier–customer collaboration [13] and the development of governance mechanisms that align interests across the network [105].

Collaboration at the customer level allows firms to better understand consumer intentions toward used products, a critical indicator of CSC adoption success [6]. Likewise, strategic supplier integration supports the adoption of sustainable practices, ensuring responsible resource use, pollution reduction, and competitive advantage without compromising natural systems [99]. Long-term stakeholder engagement and integration are thus essential to build economically and socially sustainable CSCs [31], reinforcing the broader sustainable development agenda [20].

Digital solutions can further strengthen collaboration. For instance, large-scale centralized databases could allow waste collectors, repair organizations, and manufacturers to consolidate information, promoting more efficient end-of-life management [101]. Moreover, the degree of external integration correlates strongly with the number of circular goals pursued, with higher integration affecting multiple stages of the product or service life cycle [37]. Therefore, collaboration acts as a critical enabler of CSCM by linking supply chain actors, fostering transparency, and amplifying the impact of circular initiatives. Without robust collaboration, CSCs risk fragmentation and inefficiency, while coordinated networks have the potential to deliver large-scale progress toward circularity [128].

4.1.4. Design

The design of products and services plays a decisive role in enabling CSCs, as it determines the extent to which products can be reused, repaired, remanufactured, or recycled. The coordinated integration of value chains into CSCs depends on designing products that facilitate end-of-life strategies such as repair, disassembly, and modularization [35]. Prioritizing circular design, including the assessment of waste management and the design of materials, parts, and products for circularity, enhances CSC effectiveness and resource efficiency [84].

Design strategies generally follow complementary logics such as slowing resource loops, through long-life product design and life-extension strategies, and closing resource loops, through design for disassembly, modularity, and compatibility with biological and technological cycles [129]. In this regard, Howard et al. [9] consider design as one of the four fundamental components of CSCM, emphasizing that circular economy models should be circular and regenerative from the design phase. Similarly, Bressanelli et al. [119] identify design as one of the three key levers of CSCM, aimed at eliminating waste and ensuring material recovery. Jain et al. [27] include design as one of the three dimensions of CSC, alongside business model and supply chain management, noting that reverse logistics performance depends on circular-friendly product design. In line with this, Chen et al. [3] and Thompson et al. [111] highlight design as a decisive factor, pointing out that the lack of circular design is a primary barrier in contexts such as lithium battery dismantling.

From a supplier-oriented perspective, circular design encompasses sustainable sourcing, closed-loop manufacturing practices, product design for circularity, and close collaboration with suppliers, often supported by life cycle assessment (LCA). From a customer-oriented perspective, circular design can enable durability and repair, product-as-a-service models, recovery programs, consumer education, and reverse logistics [130]. From these perspectives, whether framed as a dimension, lever, component, or enabler, design emerges as a strategic pillar of CSCM. Its integration not only facilitates circular flows but also ensures alignment with business models and supply chain practices, making it indispensable for the transition toward sustainable and circular value chains.

4.2. Resources and Flows

According to Jain et al. [27], unlike linear supply chains, CSCs should be evaluated based on resource consumption and environmental impacts throughout the supply chain. To achieve this goal, CE strategies should aim to return the maximum amount of material and energy flows to the production process by reducing waste and pollution [4]. Vegter et al. [7] argue that CE strategies should primarily focus on minimizing material, water, and energy use while maximizing the number of recovery streams. As a result, several studies have focused on material flows [8,19], considering the use of virgin resources [13], energy flows [3,81], and water flows [75]. Nguyen et al. [104] proposed a decision support system to quantify and assess sustainability and circularity in energy systems, with a focus on energy efficiency and GHG. Recently, Sartzetaki et al. [5] proposed environmental and operational metrics for CE practices based on emissions, energy, water, and waste.

Beyond these approaches, resource and flow assessments in CSCM have increasingly adopted LCA and MFA to capture the environmental implications of resource use and recovery. Such methods allow researchers to trace material and energy flows across production, distribution, consumption, and reverse logistics, providing insights into the effectiveness of circular strategies. For instance, several studies highlight the role of water footprint and carbon footprint methodologies in assessing trade-offs between increased circularity and reduced environmental impacts, emphasizing the need for integrated metrics that consider both environmental and operational efficiency [69,131].

Moreover, recent research has drawn attention to the role of digital technologies and Industry 4.0 enablers such as IoT sensors, blockchain-based traceability, and big data analytics in monitoring resource use and optimizing flows in real time [5,97]. The integration of these technologies with CE depends on systemic assessments facilitating the identification of inefficiencies, promoting resource recovery, and enhancing transparency across supply chain stages [132]. Similarly, the adoption of renewable energy sources and energy recovery mechanisms has been suggested as a critical step to reduce dependence on non-renewable inputs and mitigate climate-related risks [124].

In this regard, environmental benefits may require moderate economic trade-offs, resulting in a reduction in total profits. To prevent crises related to the depletion of non-renewable resources, decision-makers must have options that balance environmental sustainability with economic benefits and demonstrate the viability of integrating the circular economy into the supply chain [131]. The integration of Rs strategies into supply chain assessment frameworks has therefore become essential to ensure long-term resilience. These strategies, combined with advanced metrics and digital tools, enable firms to redesign supply chains toward models that are resource-efficient, low-emission, and socially responsible.

4.3. Circular Economy Strategies

The implementation of CE strategies in CSCs is commonly structured through the Rs frameworks, which encapsulate different levels of resource efficiency and circularity. Vegter et al. [17] recommend aligning CSC strategies with these Rs, as summarized in

Table 4. CE strategies in the Rs models.

CE Strategies	3Rs		4Rs				6Rs						8Rs	9Rs		10Rs
	[128]	[38]	[133]	[63]	[135]	[37]	[32]	[8]	[117]	[93]	[114]	[35]	[38]	[120]	[5]	[40]	[39]	[41]	[134]
Refuse															✔	✔	✔	✔	✔
Rethink/Redesign								✔			✔				✔	✔	✔
Reduce	✔	✔	✔	✔	✔		✔						✔	✔	✔	✔	✔	✔	✔
Reuse/Resell	✔	✔	✔	✔	✔		✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔
Repair					✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔
Refurbish							✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔
Remanufacture						✔	✔	✔	✔	✔	✔	✔		✔	✔	✔	✔	✔	✔
Repurpose										✔					✔	✔	✔	✔	✔
Recycle	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔
Recover			✔	✔		✔						✔	✔	✔		✔	✔	✔	✔
Re-mine									✔					✔				✔	✔
Regenerate													✔
Reverse													✔
Renewable energy														✔

. The literature shows considerable variation: Chen et al. [38] discuss both 3Rs (reduce, reuse, recycle) and 8Rs (reduce, reuse, repair, refurbish, recycle, recover, regenerate, reverse); Kirchherr et al. [133], referencing Potting et al. [39], restructure the framework into 4Rs (reduce, reuse, recycle, recover), also adopted by Kumar et al. [63]; while Saroha et al. [32] and Chhimwal et al. [8] extend the framework to 6Rs, emphasizing repair, refurbish, and remanufacture. More comprehensive models include the 9Rs applied by Sartzetaki et al. [5] in the context of airports, and the 10Rs introduced by Ozbiltekin-Pala et al. [40] through the SCOR model, as well as by Reike et al. [41] and Sudusinghe & Seuring [134], who distinguish between short, medium, and long-term loops.

Understanding the scope, similarities, and differences among these Rs is essential to avoid redundancy and to consolidate overlapping strategies. Simplification not only reduces the operational complexity of CSCM frameworks but also supports the creation of a coherent set of indicators that can systematically measure supply chain circularity [117]. For the purposes of this study, the 10Rs framework is adopted to ensure comprehensive coverage of stakeholder contributions. Refuse (R0), Rethink/Redesign (R1), and Reduce (R2) aim to increase circularity by promoting smarter product use and manufacture, focusing from a planning-oriented approach on minimizing resource consumption in supply chains, potentially reducing current consumption or even eliminating the use of certain materials and resources. Reuse/Resell (R3), Repair (R4), Refurbish (R5), Remanufacture (R6), and Repurpose (R7) aim to extend the life of products and their parts, encourage reuse and keep them in circulation for as long as possible through restoration, renewal and product update actions, as well as by adapting them for different uses. R8: Recycle (R8) and Recover (R9) focus on recovering waste materials, whether from the company itself, other companies, waste or landfills, and transforming such waste into new products or materials, and extracting energy from waste streams. This structured approach ensures that CE strategies in CSCs address the entire product life cycle, from smarter design and production to recovery and reintegration, thereby maximizing environmental, economic, and social benefits.

In addition to the widely adopted 10Rs strategies, several studies have proposed extended sets of Rs that introduce additional dimensions of circularity. One such set is Re-mine, which focuses on recovering materials from landfills (urban mining) or waste deposits [134], allowing components, products, or materials from a first life cycle to be reincorporated into secondary use stages or distributed through different value chains and industrial sectors [117]. This practice ranges from the selective recovery of valuable parts to the systematic extraction of minerals [41] and biochemical sources [120]. Regenerate emphasizes ecosystem restoration and renewable energy integration [136], where safeguarding, restoring, and increasing the resilience of ecosystems are prioritized [137]. Renewable energy refers to the use of sustainable, low-carbon, and resource-efficient energy sources and processes [120], playing an important role in achieving sustainability and energy supply in the current era [124]. These less frequently used strategies expand the scope of circular economy implementation by incorporating ecosystem services, urban mining, and impact mitigation perspectives.

Although the Rs models dominate the CSCM literature, it is also important to acknowledge the McKinsey ReSOLVE framework [138], which defines six strategic actions: Regenerate, Share, Optimize, Loop, Virtualize, and Exchange. There are several correspondences between the two approaches. Regenerate overlaps with R1: Rethink/Redesign and R9: Recover, focusing on restoring ecosystems, renewable energy use, and reintegration of biological resources into the biosphere. Share is aligned with R3: Reuse, R4: Repair and R7: Repurpose, emphasizing shared use, secondhand markets, and prolonging product lifetimes through maintenance and design for durability. Loop corresponds to strategies such as R5: Refurbish, R6: Remanufacture, and R8: Recycle, aiming to close material cycles through recovery operations.

Optimize is aligned with R2: Reduce, highlighting resource efficiency gains through removing waste in production and supply chain, digitalization, big data, and automation, which go beyond the material focus of Rs. Finally, Virtualize and Exchange overlaps with R0: Refuse, R1: Rethink/Redesign and R7: Repurpose, dematerializing products and services through digital alternatives, and replacing outdated technologies and materials with advanced, renewable, or more sustainable alternatives. In this sense, Rs models provide a detailed operational view of circular strategies linked to material, energy, and water flows, while ReSOLVE offers a complementary strategic orientation, particularly relevant for technology, digitalization, and systemic transformation.

Beyond the well-established 3R to 10R frameworks, recent studies have expanded the conceptualization of circular strategies to encompass a much broader range of “R” principles. Uvarova et al. [139] introduced a comprehensive typology of 60R principles, organized into four overarching categories (Reduce, Reuse, Recycle, and Reverse Logistics). This taxonomy incorporates strategies such as Reduce toxins, Replace with renewable or local resources, Route-tracking, Refuse useless purchases, Reassemble, Refit, Redeploy, Rectify, Reutilize resources, Recapture, Resynthesize, and Reform, among others, reflecting a wide operational and managerial spectrum of circular practices. Similarly, Prochatzki et al. [140] added Retreat (replacement of worn parts for continued use) to the list of circular strategies, while Papamichael et al. [141,142] proposed an extended set of Rs including pRevent and Rent.

Extending even further, Zorpas [143] identified over 100R principles that transcend material efficiency and incorporate social attitudes, technological innovation, and well-being, such as Recharge, Reverence to nature, Refashion, Resuscitate, Retrofit, and Revenue streams. These Rs not only support smarter resource management but also contribute to the SDGs, environmental policies, legislations/directives, and industrial strategies. Schneider et al. [144] emphasized that the growing complexity of R-based frameworks necessitates the development of methodologies and circular indicators to identify and select the most applicable Rs at the product design stage. These emerging frameworks highlight the evolution of circular strategies from operational resource loops toward systemic, behavioral, and technological transformations, underscoring the need for adaptive CSCM assessment tools capable of integrating this expanding landscape of R principles.

4.4. Proposal for a CSCM Assessment Framework

The literature review highlights the prevalence of multiple frameworks for CSCM assessment, each with different emphases. These include the BSC [32,45], the SCOR model [27,40,79], frameworks centered on enablers, levers, and dimensions [5,26,27,61,92,116,119], the EMF frameworks [9,37,115], and frameworks structured around CE strategies expressed as Rs [32,39,41,63]. CE-oriented models provide more accurate estimations of environmental and economic impacts when supply and production operations are explicitly considered [145].

Table 5. CSCM frameworks.

Authors	SCOR	BSC	EMF Frameworks	CE Enablers	Rs Frameworks
[40]	✔				✔
[79]	✔				✔
[27]	✔			✔
[93]	✔
[32]		✔			✔
[45]		✔
[62]		✔
[9]			✔	✔
[37]			✔
[115]			✔
[119]				✔
[116]				✔
[92]				✔
[61]				✔
[39]					✔
[41]					✔
[63]					✔
[38]					✔
[8]					✔
[5]					✔
[26]					✔

summarizes these approaches. Some authors also propose hybrid models, such as Saroha et al. [32], who combine the BSC with Rs strategies, extending traditional BSC dimensions with environmental, social, and cost perspectives while measuring CSCM through 6Rs. Similarly, Howard et al. [9] propose a CE indicator framework based on technical and biological approaches, including enablers such as design, business models, reverse network management, and system enablement. Other studies emphasize that CSC performance is influenced by multiple contextual factors such as technology, industry type, and customer priorities [26].

A recurring reference point in the literature is the SCOR model, which links supply chain processes with CE goals. Jain et al. [27] emphasize the use of key indicators such as innovative business models, eco-design, and efficient supply chain practices to minimize waste, material consumption, and carbon footprint. Vegter et al. [79] extend SCOR processes (Plan, Source, Make, Deliver, Return, Enable) by introducing Use and Recover, aligning the model with circular objectives such as decoupling growth from resource depletion, reducing water and energy use, minimizing waste, and maximizing recovery flows. Likewise, Ozbiltekin-Pala et al. [40] merge SCOR with Rs strategies, providing KPIs for each SCOR process to accelerate circularity, while Alfina et al. [93] adapt SCOR to the healthcare sector by mapping performance indicators into environmental, social, economic, and logistical criteria. Collectively, these studies demonstrate the versatility of SCOR as a foundation for CSCM assessment.

The SCOR model is particularly relevant as it provides a widely accepted methodology, diagnostic tools, and benchmarking capabilities, making it a global reference for both private and public organizations [146,147]. Traditionally, SCOR included five core processes (Plan, Source, Make, Deliver, Return) [27,40,79], and in its most recent update by the Association for Supply Chain Management (ASCM), these have evolved into Synchronize (Plan), Supply (Order, Transform), Demand (Source, Fulfill), and Regenerate (Return), all coordinated by the overarching process Orchestrate.

Building on this foundation, the proposed framework in this study adopts SCOR as its structural base but supplements it with enablers and circular outcomes, inspired by the EMF Circulytics approach [116]. Enablers include business model enablers, such as CE awareness among top management, integration into strategic planning, financial support, communication of CE progress, employee training, and active participation in CE initiatives [102]. Technology enablers, including digital information systems, CE-oriented assets and machinery, and technological solutions that extend product and equipment lifecycles [92,97,120]. Collaboration enablers, addressing partnerships across customers, suppliers, companies, associations, governments, and NGOs to facilitate data sharing, promote CE, and implement recovery strategies [99,102]. Design enablers, covering policies for circular products and services, circular product portfolios, eco-design practices, and sustainable supplier selection [3,27,111].

On the outcomes side, the framework identifies material, water, and energy flows as critical indicators of circularity. Material flows encompass CE-oriented products during use and end-of-life, revenues from CE products, recirculated by-products, waste recovery, and hazardous waste management, directly linked to GHG emission reductions [108]. Water flows include reductions in extraction, reuse of discharged water, integration of rainwater and wastewater, and treatment of pollutants before discharge. Energy flows focus on reduced consumption, renewable energy integration, revenues from energy-efficient products, and renewable energy generation, reducing the environmental impacts of fossil fuels [124]. The proposed model integrates the 10Rs strategies, assigning them to SCOR processes and associating them with material, energy, and water flows, as suggested in previous works [40]. This alignment ensures that CE strategies are operationalized across the supply chain, linking enablers with measurable outcomes and providing a comprehensive, multi-dimensional framework for CSCM assessment shown in Figure 6.

To facilitate its use as an assessment tool, the proposed framework can be operationalized as a structured sequence of steps. First, supply chain processes are mapped using the updated SCOR (Synchronize, Supply, Demand, Regenerate, Orchestrate) model to ensure all stages where circular practices may occur are identified. Next, the presence and maturity of the four enablers (business model, technology, collaboration, and design) are evaluated for each process through measurable indicators such as CE awareness, digitalization, stakeholder integration, and eco-design practices. This step can be conducted through checklists, expert scoring, or structured surveys. Third, the outcomes are assessed by quantifying the material, water, and energy flows using the operational data available for each SCOR process, such as the percentage of recycled inputs, water reuse ratios, and the share of renewable energy. Fourth, these flows are linked to the corresponding R strategies, identifying which circular practices are being implemented and at what intensity. This clarifies whether a process contributes refuse, rethink, reduce, reuse, repair, refurbish, re-manufacture, repurpose, recycle, or recover. Finally, the results are consolidated into indices or scores for each process and flow. This allows for benchmarking across organizations, sectors, and over time. This stepwise procedure enables the framework to go beyond conceptual mapping by providing a replicable method to measure circularity, highlight gaps in enablers or outcomes, and support continuous improvement decisions in CSCM.

For instance, consider a manufacturing supply chain. In the Synchronize dimension, the business model dimension is evaluated by analyzing whether the company integrates CE into strategic planning (e.g., circularity targets, resource efficiency goals, stakeholder engagement strategies). Collaboration is also measured through the degree of cross-functional alignment and the presence of industry partnerships to coordinate CE goals. Flow indicators include the establishment of material circularity targets (R1: Rethink/Redesign), energy goals (share of renewable energy to be adopted—R9: Recover/Regenerate), and water efficiency benchmarks (R2: Reduce). During Demand dimension, collaboration enablers focus on partnerships with distributors and retailers to implement reverse logistics, as well as customer education programs for repairing or reusing options. Technology enablers include digital platforms for tracking product lifetimes and enabling service-based models. Flow metrics may include the percentage of products collected through take-back systems (R3: Reuse), number of products sold in second-hand markets (R3: Reuse), and the share of appliances repaired during their use phase (R4: Repair).

For the Supply dimension, design enablers are evaluated by verifying whether products are modular and easy to disassemble, while technology enablers include the adoption of eco-efficient machinery and digital traceability systems for raw materials. Material flows are measured by the proportion of recycled steel or plastics in new appliances (R8: Recycle), energy flows by the percentage of renewable electricity used in manufacturing (R9: Recover), and water flows by the amount of treated and recirculated water in production (R2: Reduce). In the Regenerate dimension, collaboration enablers involve partnerships with recyclers, as well as agreements with municipalities for waste collection. Business model enablers include offering refurbishment or remanufacturing services to extend product lifetimes. Flow indicators include the proportion of components recovered for remanufacturing (R6: Remanufacture), the number of products refurbished and resold (R5: Refurbish), and the amount of valuable materials extracted at end-of-life (R8: Recycle, R9: Recover). Water flows may also be assessed by the percentage of water reclaimed from end-of-life treatment processes.

5. Discussion

This literature review sought to address a recurring gap in the field, expressed as the limited availability of systematic reviews, tools, and empirical studies for CSCM assessment [16,17,37]. The bibliometric analysis confirms that CSCM assessment has rapidly evolved into a consolidated research domain, with exponential growth in publications since 2021, and concentration in leading Q1 journals. The geographic analysis shows that CSCM assessment research is strongly concentrated in Europe and Asia, with limited contributions from other regions. This imbalance suggests that many existing frameworks may reflect regional priorities and contexts, leaving gaps in applicability for underrepresented regions. Therefore, the proposed framework aims to provide a more generalizable structure that can be adapted across geographies, helping to reduce disparities in the development and implementation of CSCM assessment tools.

However, several imbalances and gaps persist. The dominance of specific publishers and geographic regions suggests that research is still driven by relatively narrow academic communities, potentially limiting the diversity of case studies and perspectives. Furthermore, keyword clusters and subject areas highlight the predominance of environmental and engineering dimensions, while social and ecosystem-related aspects such as biodiversity, soil health, and community impacts remain underexplored. Future research should therefore integrate a broader set of environmental and social indicators and foster cross-regional and interdisciplinary collaboration, not only to enrich scientific understanding of CSCM but also to enhance its policy relevance and governance implications for sustainable resource management.

The evaluation framework proposed in this study builds on the recommendations of the EMF [116], particularly the dual categorization into enablers and outcomes. Regarding enablers, also referred to as levers, facilitators, or dimensions, the review identified business model, collaboration, technology, and design as key components. The business model has been emphasized as a regenerative system based on waste and resource management [10] and even as a source of competitive advantage [8,27]. The technology enabler plays a strategic role in consolidating CSC, supported by contributions highlighting its transformative impact on manufacturing and supply chain digitalization [22,23,90,92,105,123]. The collaboration enabler, although among the least studied, is essential for managing stakeholder relationships, whose absence constitutes one of the greatest risks for CSC performance [13,18]. Design is repeatedly identified as a critical barrier and success factor, as circularity must be embedded from the product and system design stage [3,7,111].

With respect to outcomes, the framework incorporates material, water, and energy flows, which the literature often conceptualizes through Rs strategies. Several studies propose consolidated Rs groupings, such as the 4Rs (reduce, reuse, recycle, recover) [133] or frameworks combining short, medium, and long-term loops [41]. However, this study retains the 10Rs perspective, ensuring comprehensive coverage of circular strategies across flows. Unlike BSC-based frameworks [32,45] or EMF-inspired approaches [37,115], the proposed model anchors itself in the SCOR framework, particularly in its most recent version, aligning circularity assessment with globally accepted supply chain processes. In this sense, the proposal closely relates to studies by of Jain et al. [27], Vegter et al., [79], and Ozbiltekin-Pala et al. [40], while adding value by explicitly connecting SCOR processes with enablers, material/energy/water flows, and Rs strategies. This integration allows the framework to function as a comprehensive CSCM measurement system, bridging strategic drivers with operational outcomes.

Upon reviewing the literature on CSCM assessment, we found that it approaches the subject from multiple angles, including risks, enablers, barriers, and integrative frameworks. While these themes may seem fragmented, collectively, they represent the various ways in which CSCM performance has been conceptualized and measured thus far. Integrating these perspectives is essential to constructing a comprehensive view of the field because it shows that no single dimension can capture the complexity of CSCM assessment. The convergence of these diverse approaches provides the foundation for the proposed framework, which systematizes enablers, outcomes, and CE strategies within the SCOR model’s structure.

When compared with prior reviews, this study advances the field in several ways. Early contributions, such as Jain et al. [27], offered a conceptual strategic framework based on grounded theory, while Mangla et al. [43] focused mainly on identifying barriers in the context of India. Batista et al. [35] conceptualized CSCs by linking them to earlier narratives such as reverse logistics, green supply chains, and closed-loop supply chains. Farooque et al. [2] provided a structured literature review that unified definitions of CSCM but did not propose operational assessment frameworks, emphasizing the need for further research on design for circularity, procurement, and collaboration. Lahane et al. [44] emphasized the potential of advanced modeling and MCDM approaches for assessing enablers, barriers, and innovative business models. Zhang et al. [46] proposed a multidimensional CSCM framework combining practice cases and academic research.

More recently, Vegter et al. [7] developed a performance measurement system emphasizing interdependencies between circularity, economic, environmental, and social performance objectives. Other reviews, such as Bressanelli et al. [119], focused on specific industries like electronics, while MahmoumGonbadi et al. [148] and Walker et al. [36], and consistently highlighted the underrepresentation of the social dimension. Calzolari et al. [13] synthesized decision support tools into composite indicators but also found limited inclusion of circularity and social measures, confirming a trend toward cost- and environment-focused assessments. Similarly, Kaiser et al. [105] showed that the uneven distribution of costs and benefits in CO₂-based value chains often biases performance measurement toward financial indicators, despite calls for resource and impact-based metrics [27].

Chrispim et al. [47] expanded the debate to CE assessment tools more broadly, identifying limitations such as the underrepresentation of social indicators, stakeholder engagement, and R-strategies. However, their focus was on general CE tools rather than supply chain-specific assessment. In contrast, our review explicitly addresses assessment frameworks for CSCM, combining a systematic review with bibliometric analysis to map publication trends, leading sources, authors, and keyword co-occurrences, while also performing a qualitative synthesis of frameworks, methodologies, barriers, risks, enablers, and CE strategies. Most importantly, our study proposes an integrated CSCM assessment framework that aligns the updated SCOR model with enablers (business model, technology, collaboration, design) and outcomes (material, water, and energy flows) linked to CE strategies (10Rs). This integrative and operationally grounded proposal is not found in previous reviews, positioning our contribution as both novel and practically relevant for benchmarking and policy guidance in CSCM.

Limitations of the Research

This review has some limitations. First, the relatively limited number of available and selected documents reflects the emerging stage of CSCM research. This is consistent with existing findings indicating that both academic literature and business practice in this field are still in early development. Second, the proposed framework, while comprehensive, outlines a general structure for assessing circular economy progress in supply chains. It offers flexibility for adaptation across sectors but requires the development of specific, measurable indicators to operate its components.

A limitation of this review is that the analysis of CE strategies was primarily based on the Rs models, given their prevalence in CSCM literature and their direct operational link to material, water, and energy flows. However, complementary frameworks such as ReSOLVE introduce strategic dimensions that are less explicit in the Rs but crucial for addressing digitalization, innovation, and systemic transformation. Another limitation relates to the search strategy. In this study, we restricted the search query to document titles, with the aim of retrieving only papers explicitly addressing CSCM assessment. While this approach increased the precision of results, it may have excluded relevant studies indexed only under abstracts or keywords. Future systematic reviews could expand the search to title–abstract–keywords fields to broaden coverage and ensure a more comprehensive mapping of the field.

6. Conclusions

This review examined assessment approaches for Circular Supply Chain Management (CSCM) by combining bibliometric mapping with qualitative content analysis. The results confirm that CSCM assessment has rapidly consolidated as a research field, but with persistent imbalances in geographic contributions, disciplinary perspectives, and underrepresentation of social and ecosystem dimensions. The qualitative synthesis showed that existing frameworks converge on two main categories, which are enablers (business model, technology, collaboration, and design) and outcomes (material, water, and energy flows), typically operationalized through Rs strategies. Building on these insights, this paper proposes a comprehensive CSCM assessment framework that integrates the updated SCOR model with enablers and outcomes, thereby bridging strategic drivers with measurable resource flows and providing a systematic tool to assess circularity across supply chains.

For stakeholders, the framework offers a structured way to evaluate circular practices, benchmark progress, and align business models, technologies, and collaborations with tangible results in resource efficiency. Policymakers can also use it to identify gaps, promote sector-wide adoption, and design incentives for circular supply chains. Future research should prioritize the development of measurable and scalable indicators for each SCOR process, expand the integration of social and ecosystem dimensions, and validate the framework through empirical applications in diverse sectors and regions. Advancing quantitative modeling and readiness indexes will also be key to translating CSCM assessment into actionable practices that accelerate the transition to sustainable and circular supply chains.