Evaluating Operational and Environmental Factors in Circular Supply Chains: A Decision-Making Model Integrating Sustainability Dimensions

Abstract

Background: The transition from linear to circular supply chains (CSC) is critical for advancing sustainability, resilience, and resource efficiency, while supporting the UN Sustainable Development Goals (SDGs). However, existing studies rarely integrate internal operational performance with external PESTEL factors under the Benefits, Opportunities, Costs, and Risks (BOCR) perspective, limiting the ability to prioritize circular strategies holistically. Methods: This study develops a decision-making framework that combines the Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), enabling reliable prioritization of interdependent sustainability criteria. Results: A case analysis in the chemical industry demonstrates the applicability of the framework, enhancing transparency and reducing subjectivity in CSC evaluation. Findings highlight quality as the key operational attribute and social as the dominant PESTEL dimension, reinforcing the integration of internal and external factors toward SDG-oriented strategies. Conclusions: The study contributes theoretically by bridging operational and contextual dimensions in CSC evaluation under the BOCR perspective, and methodologically by advancing hybrid MCDM applications to address uncertainty. Managerially, the framework provides a structured tool for aligning circular supply chain strategies with organizational objectives and SDGs, supporting decision-making that strengthens environmental sustainability, stakeholder legitimacy, and resilience.

1. Introduction

Over the past few years, the transition from linear to circular supply chains (CSC) has emerged as a critical approach toward sustainability, resilience, and resource efficiency. Extensive research has examined how to identify the key enablers and barriers involved in this transformation across various sectors [1]. In this context, CSCs are increasingly recognized as a pathway to advancing sustainability and supporting the United Nations Sustainable Development Goals (SDGs) by preventing waste generation and improving resource efficiency [2]. For instance, ref. [3] investigated challenges and drivers for implementing remanufacturing practices. Similarly, ref. [4] analyzed attributes influencing the circular economy (CE) in emerging economies, while [5] highlighted how CE models in the textile sector reduce environmental damage and foster economic growth. Studies in agri-food supply chains [6] and the wine industry [7] also show that circular practices significantly enhance sustainable performance, especially when combined with green innovation capabilities and the integration of sustainability-oriented processes.

Additionally, recent approaches have emerged to understand the structural and relational elements that support circularity in supply chains. In particular, ref. [8] addressed a theoretical gap by linking CE transitions with supply chain management strategies, types, and tactics. In addition, ref. [9] explored how collaboration among stakeholders influences sustainable performance within CSC strategy, drawing on social exchange theory. Likewise, ref. [10] discussed how circular design features—particularly material, information, and financial flows—enable the transformation of traditional supply chains into circular ones, reinforcing broader CE ecosystems.

Parallel to these efforts, CSC has increasingly adopted Multiple Criteria Decision-Making (MCDM) to deal with complexity and trade-offs involved in sustainability-oriented decisions. For example, ref. [11] developed a model to integrate social sustainability into supplier selection by prioritizing suppliers based on both social and performance attributes, emphasizing its strategic role in achieving broader SDGs. Building on this, ref. [12] applied an MCDM approach to optimize an agri-food supply chain network in the saffron industry, addressing sustainability across environmental, economic, and social factors. Moreover, ref. [13] linked evaluation criteria to SDG 8 and SDG 12, while [1] integrated risk assessment into MCDM models, highlighting alignment with SDG 12.5. Other contributions include the development of advanced hybrid MCDM models to handle uncertainty and incomplete information, such as the integration of fuzzy logic [14], and the evaluation and prioritization of bioenergy technologies based on environmental, economic, technological, and social criteria [15]. A comprehensive review by [16] further shows the rapid growth and diversification of MCDM applications in sustainable engineering over the past decade.

Despite this growing body of work, few studies have applied MCDM methods to address the specific challenges of CSC, where decision problems involve interdependent criteria spanning Political, Economic, Social, Technological, Environmental, and Legal (PESTEL) factors [17]. Existing CSC evaluation approaches often focus either on internal operational metrics or on external contextual factors in isolation. Without integrating both dimensions, evaluations risk overlooking the interdependencies that determine the feasibility and strategic alignment of circular practices. Most existing research either examines circularity without structured decision models or applies MCDM techniques outside the context of CSC, limiting the ability to prioritize and align circular practices with strategic objectives. This gap underscores the need for integrative frameworks that combine MCDM approaches with CSC management to support data-driven decision-making in complex, sustainability-oriented contexts.

This paper develops a data-driven decision-making framework for CSC strategy by integrating MCDM methods. The approach combines operational performance and PESTEL factors, enabling more informed and effective decisions for advancing circularity. By explicitly linking these dimensions under the BOCR perspective, the framework operationalizes SDG alignment in practical decision-making contexts, ensuring that circular strategies can be evaluated and aligned with goals such as SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). The proposed approach adopts a multi-criteria analysis methodology to assess critical factors influencing the implementation of CSC [18]. The Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) were selected as the decision-making techniques due to their ability to derive reliable and consistent weights with fewer pairwise comparisons [19]. Although the Full Consistency Method (FUCOM) is also recognized as an effective approach for deriving criteria weights, BWM was selected given its extensive validation in sustainability-oriented supply chain contexts. Moreover, FTOPSIS was combined to explicitly address fuzziness and uncertainty in evaluating alternatives. This methodological choice ensures robustness while maintaining comparability with prior studies in CSC strategy.

Furthermore, the findings demonstrate that a data-driven and systematic evaluation of decision criteria can enhance transparency, reduce subjectivity, and improve alignment among stakeholders involved in CSC strategies. The resulting decision-support framework provides both theoretical insights and actionable pathways to accelerate the transition from linear to CSC models. The contributions are threefold: first, structured decision-making that integrates operational performance and PESTEL factors for CSC; second, applying an MCDM approach combining BWM and FTOPSIS to prioritize interrelated sustainability criteria; and third, integrating environmental and operational factors into a unified model aligned with the SDGs. To translate the approach into practice, the investigation examines operations of the chemical industry to demonstrate how structured evaluation can support circular strategies aligned with the SDGs, strengthening environmental sustainability and resilience; this choice is further elaborated in Section 4.1.

2. Literature Review

2.1. Sustainable CSC

The integration of digital transformation and CE practices has gained increasing attention as an enabler of CSC. Several studies have explored specific aspects of this intersection and revealed valuable insights, but also left notable gaps. For example, ref. [20] analyzed how digital transformation initiatives enhance effectiveness. Similarly, ref. [21] investigated how digital technologies support CSC structures in SMEs, improving innovation, environmental responsibility, and operational efficiency.

Taken together, these studies converge in showing that digital technologies act as enablers of CSC. However, they differ in scale and scope: while some emphasize applications in large corporations, others highlight challenges in SMEs and sector-specific contexts. Instead of listing multiple sectoral applications, this review emphasizes that the significance of these studies lies in demonstrating how digital tools can both accelerate innovation and expose structural limitations. For instance, blockchain adoption [22] is relevant not only for logistics efficiency but also because it illustrates how transparency and traceability can strengthen trust in circular practices—an aspect that is generalizable beyond courier supply chains. Similarly, IoT-enabled strategies [23] highlight the potential for resource recovery in high-tech industries, but their broader applicability depends on overcoming cost and infrastructure barriers faced by SMEs.

In addition to these digital and operational advances, CSC directly contributes to the broader sustainability agenda defined by the SDGs, particularly SDG 6, 7, 8, 9, 12 and 13. Rather than presenting each SDG with isolated examples, the critical point is that CSC practices simultaneously advance multiple sustainability dimensions. For example, the study by [24] on water-use efficiency under SDG 6 is significant because it shows how narrowing and regenerating resource flows can reduce waste at scale, but its relevance is limited to large corporations unless adapted for SMEs. Likewise, ref. [25] demonstrates how Industry 4.0 technologies reduce energy consumption (SDG 7), which is crucial for policy-making since energy efficiency is a transferable principle across sectors. While Industry 4.0 technologies have been widely examined for their role in reducing energy consumption and enhancing efficiency, recent studies indicate that Industry 5.0 is emerging as a complementary paradigm. By emphasizing human-centricity, resilience, and sustainable industrial ecosystems, Industry 5.0 extends the digital transformation agenda toward more inclusive supply chain practices [26,27].

With respect to SDG 8 and SDG 9, the review by [28] is particularly influential because it consolidates sustainable supply chain management into a framework that encourages innovation, while [29] contributes by proposing performance measurement systems that operationalize clean technologies. These works are significant because they move beyond sector-specific findings and provide methodological tools that can be generalized across industries.

For SDG 12, the case of sustainable packaging in cosmetics [30] is illustrative but sector-bound; its broader relevance lies in showing how consumer-facing industries can drive responsible production. Similarly, barriers identified in the pharmaceutical sector [31] highlight the importance of reverse logistics, but the generalizable insight is the need for governance mechanisms to manage hazardous waste across industries.

Finally, under SDG 13, bibliometric analyses [32] are critical because they reveal macro-level trends in CSC research, such as CO₂ reduction strategies, while [33] provides a socially significant perspective by showing how digital tools empower low-income consumers—an insight that connects circularity with inclusivity and climate action. While CSC practices contribute to multiple SDGs, the framework developed in this study focuses on those most directly operationalized through decision criteria.

Overall, these contributions reveal both convergence and tension in CSC research. While there is broad agreement that digital technologies accelerate innovation and enable transparency, inconsistencies remain regarding scalability across firm sizes and sectors. Some studies emphasize efficiency gains in large corporations, whereas others highlight structural barriers in SMEs, raising unresolved questions about transferability. Moreover, debates persist on whether digitalization primarily drives operational improvements or whether it also reshapes governance and stakeholder trust—an area where conceptual clarity is still lacking.

Despite these contributions, the literature still lacks integrative approaches that simultaneously capture internal operational performance and external macro-environmental pressures. Most studies remain fragmented, focusing either on technological enablers or sustainability outcomes, without bridging the two perspectives. In summary, the literature demonstrates that CSC is driven by digital transformation and aligned with the SDGs, but it remains underexplored in terms of holistic evaluation frameworks. This gap justifies the adoption of MCDM approaches, such as the one proposed in this study, to assess CSC implementation by integrating operational performance and PESTEL factors.

This review, therefore, moves beyond description by synthesizing convergences and tensions in CSC research, highlighting unresolved issues of scalability, governance, and transferability. These insights provide the theoretical foundation for the integrated BOCR–BWM–FTOPSIS framework proposed in this study.

2.2. Operational Performance Objectives

Those elements of operational performance that align with market requirements and consequently become the focus of the operation. Different authors in operations strategy propose their own sets of performance objectives, with no consensus on terminology. These are variously referred to as ‘performance criteria’, ‘strategic dimensions of operations’, ‘performance dimensions’, ‘competitive priorities’, and ‘strategic priorities’. In this context, the term ‘performance objectives’ is adopted. Although authors differ on the precise definition of performance dimensions, certain categories are commonly recognized. The set of five performance objectives is relevant to any type of operation, although their relative priorities may vary. These five encompass: Quality, speed, dependability, flexibility, and cost [34].

Quality has been extensively examined, and several applied research studies have been conducted, presenting an integrated environmental management model that aligns quality management principles with sustainable development approaches such as Corporate Social Responsibility (CSR), Green Supply Chain Management (GSCM), Environmental, Social, and Governance (ESG), and CE. This ensures synergy across ecological, social, and economic factors [35]. Moreover, integrating sustainability, circularity, and closed-loop systems with quality and excellence principles has been shown to strengthen firms’ long-term advantage under the resource-based view [36]. The convergence of SDG-12 and financial management [37,38] is particularly relevant because it links operational quality with broader sustainability practices. However, these insights remain largely conceptual, requiring empirical validation across different industries. While quality and cost are often seen as conflicting priorities, recent studies suggest that circular practices can reconcile them by leveraging digital technologies to reduce waste while improving standards, thereby advancing SDG-oriented outcomes alongside operational excellence.

Speed refers to the performance criterion that evaluates how quickly a product or service is delivered once demand is identified [39]. Studies such as [40,41] are significant because they show how AI and Industry 5.0 frameworks accelerate responsiveness in CSC. Therefore, their applicability beyond high-tech sectors remains uncertain, highlighting the need for integrative approaches that balance speed with sustainability outcomes. Collectively, these contributions underscore the role of advanced technologies in enhancing speed, responsiveness, and systemic adoption of sustainable practices.

Dependability refers to the consistency of product and service [36]. Dependability is reinforced by studies such as [42,43], which highlight automotive and recycling contexts. These are important because they demonstrate resilience in sector-specific supply chains, but their generalization requires caution since not all industries share the same digital readiness. Together, these insights reinforce dependability as a cornerstone for resilient, adaptive, and sustainable supply chain performance.

Flexibility refers to the capacity of production or supply systems to adapt quickly to changes, whether in product variety, process adjustments, demand shifts, or supply disruptions [39]. Evidence from Indian manufacturing firms [44] is significant because it shows flexibility mediates circular practices in emerging economies, though its generalizability to developed contexts remains uncertain. Similarly, ref. [45] highlights technology integration as a critical enabler of corporate sustainability, offering insights that extend beyond sectoral boundaries. Together, these contributions underscore flexibility as a pivotal capability for resilient, adaptive, and sustainable supply chains in the CE era. Flexibility and dependability, traditionally viewed as distinct, converge in CSC contexts where adaptive systems must also guarantee resilience.

Cost refers to minimizing operational expenses to strengthen competitiveness. Cost efficiency studies such as [46,47] are significant because they show how technology and policy jointly shape competitiveness in CSC. However, sector-specific findings such as food and textile [48,49,50] illustrate contextual challenges, reminding us that cost strategies must be adapted to industry realities rather than assumed universally applicable. Together, these insights show that minimizing costs in circular CSC is not only about operational efficiency but also about strategically leveraging technologies, policies, and collaborative practices to achieve long-term competitiveness. To consolidate these insights,

Table 1. Operational performance.

Alternative	Description	References
Quality	Delivering products and services that meet specifications, are free of defects, and consistently satisfy customer expectations.	[34]
Speed	Refers to how quickly products or services are delivered; advanced technologies enhance responsiveness and adoption of sustainable practices.	[39,40,41]
Dependability	Consistency of product and service delivery, reinforced by digital supply chains, IoT readiness, and resilience strategies in sustainability contexts.	[34,42,43]
Flexibility	Capacity to adapt to changes in demand, processes, or supply; pivotal for resilience and CE adoption through technologies and alliances.	[39,44,45]
Cost	Minimizing operational expenses to strengthen competitiveness and support long-term sustainability strategies.	[34]

summarizes the five operational performance objectives and their core descriptions.

In Table 1, cost is listed among the operational performance objectives for completeness. However, in this study, cost was not directly applied within the operational performance analysis, since it is explicitly addressed within the benefits, opportunities, costs, and risks (BOCR) framework. This avoids redundancy and ensures that cost is analyzed at a strategic level (medium- to long-term competitiveness) rather than at the short-term operational level. While operational cost could be considered separately, in this study, it is integrated into BOCR to emphasize its role in balancing financial sustainability with circular strategies.

Despite the breadth of research, few studies integrate all five performance objectives simultaneously within CSC. Most contributions remain siloed, focusing on one or two factors, which limits understanding of systemic trade-offs and synergies. This fragmentation underscores the importance of critically assessing which findings are generalizable and which remain sector-bound. In summary, the literature demonstrates that operational performance objectives are increasingly reframed through the lens of sustainability and circularity. However, the fragmented treatment of these objectives highlights the need for integrative frameworks that capture their interdependencies. This gap reinforces the relevance of adopting MCDM approaches, as proposed in this study, to evaluate CSC implementation holistically.

Beyond their operational relevance, these five objectives are not independent; their interaction with external macro-environmental pressures will be further elaborated in the next section. This reinforces the need for integrative approaches that evaluate operational performance in conjunction with broader contextual factors.

2.3. Political, Economic, Social, Technological, Environmental, and Legal

Originally, ref. [51] first highlighted environmental scanning as a crucial way for managers to track and anticipate external shifts. From this starting point, PEST and later PESTEL were developed to examine PESTEL influences on business. Over time, these tools evolved into more practical frameworks used across industries. Recent work shows that PESTEL remains valuable for guiding strategy in fast-changing environments [51,52].

Environmental strategies in CSC are significant because they embed sustainability into procurement, logistics, and operations. For example, ref. [39] shows how reverse logistics reduces ecological impact, but its applicability depends on sector readiness and regulatory support. This highlights the importance of aligning environmental practices with broader SDG principles.

In this sense, environmental strategies within CSC also reinforce SDGs principles by preventing waste generation and promoting efficiency in energy, water, and resource use. PESTEL encompasses the following factors:

Political refers to the influence of government policies, regulations, and international agreements on supply chains and CE transitions. The case of Brazil’s waste-to-energy sector [53] is relevant because it illustrates how policy frameworks can accelerate or hinder CE transitions. However, its generalizability is limited, as institutional capacity and enforcement vary across countries.

Similarly, economics refers to market conditions, investment strategies, and competitive pressures that affect resource allocation and profitability. Economic drivers such as global competition [54] and ICT adoption in Poland [55] highlight how resource allocation shapes CSC. However, these findings are context-specific, reminding us that economic pressures must be interpreted within local industrial structures. These two factors are closely intertwined, as regulatory frameworks often shape economic incentives and investment strategies. For instance, policy-driven mechanisms such as carbon taxes directly influence cost structures and competitiveness in global supply chains.

Social refers to societal values, cultural expectations, and community needs. Social initiatives like adaptive reuse [56] and waste reuse in Buenos Aires [57] are significant because they connect circularity with social equity. However, their influence is highly localized, showing that social drivers cannot be assumed universally applicable.

Technological refers to innovations and digital tools. Technological innovations, such as foresight in SMEs [58] and digital tracking, demonstrate how tools enable efficiency and transparency. Their broader relevance lies in showing that technology adoption is shaped by both consumer expectations and regulatory environments.

Environmental factors include sustainability targets and resource management. The evaluation of waste management under the European Green Deal [59] is significant because it demonstrates how supranational policy frameworks can accelerate circular transitions, though outcomes depend on governance capacity at the national level. Similarly, sanitation systems in Sweden [60] highlights how advanced infrastructure supports CE practices, but their transferability to countries with weaker institutional capacity is limited. Zero-waste city strategies [61] provide valuable insights into urban circularity, their success is highly context-dependent, requiring strong local governance and citizen engagement.

Finally, legal factors encompass laws, standards, and compliance requirements that regulate sustainable operations and CE adoption, ensuring that CSC strategies remain aligned with institutional frameworks. Evidence from circular packaging adoption in the fashion industry [62], as well as net-zero strategies in UK metal industries, illustrates how compliance obligations can act both as barriers and enablers of transformative change. More broadly, legal frameworks highlight the role of enforcement, certification, and liability mechanisms in shaping the pace and scope of circular transitions. To provide a structured overview of these external drivers,

Table 2. PESTEL.

Alternative	Description	References
Political	Influence of government policies, regulations, and international agreements on supply chains and CE transitions.	[53]
Economic	Market conditions, investment strategies, and competitive pressures are shaping resource allocation and profitability.	[54,55]
Social	Societal values, cultural expectations, and community needs are driving sustainable practices, and CE initiatives and social sustainability also encompass broader consequences for society, including labor conditions, equity, health, and community well-being. This dual perspective ensures that CSC strategies consider both societal acceptance and their wider social impacts.	[56,57]
Technological	Innovations and digital tools enabling efficiency, transparency, and sustainability in dynamic environments.	[58]
Environmental	Ecological considerations, sustainability targets, and resource management practices across the product life cycle.	[39,59,60,61]
Legal	Laws, standards, and compliance requirements regulating sustainable operations and CE adoption.	[62]

summarizes the six PESTEL dimensions and their core descriptions.

Despite the breadth of research, few studies adopt an integrative perspective that simultaneously considers all PESTEL factors. Most contributions remain fragmented, focusing on isolated drivers. This fragmentation underscores the need to critically assess which sectoral findings are transferable and which remain context-bound. Together, the PESTEL factors highlight how political frameworks, economic pressures, social expectations, technological innovations, environmental imperatives, and legal requirements interact to shape strategic decisions in sustainable and CSC. Recognizing these interdependencies allows organizations to anticipate challenges, leverage opportunities, and align their operations with long-term sustainability goals.

The connection between operational performance and PESTEL factors is therefore theoretical as well as practical: quality is shaped by environmental and social governance standards; speed is influenced by technological advances and economic pressures; dependability depends on regulatory frameworks and social expectations; flexibility responds to technological dynamics and economic volatility; and cost is directly affected by political and legal mechanisms such as taxation and compliance. This integrative perspective clarifies that operational performance cannot be assessed in isolation, but must be understood as embedded within the broader macro-environmental context.

Although operational performance has been extensively studied, the literature remains fragmented in how it integrates sustainability and circularity. This subsection critically examines how the five performance objectives, quality, speed, dependability, flexibility, and cost, are reframed under CSC contexts, highlighting both convergences and unresolved tensions.

2.4. Research Gaps Addressed

Although the literature on CSC has advanced, several critical gaps remain. Studies emphasize the need to validate CSC models in industrial contexts and to address systemic, financial, technological, and organizational barriers that hinder their adoption [46]. Research on green innovation further highlights the importance of examining adoption across varied market structures, supply chain dynamics, and policy contexts to understand how firms balance profitability with carbon reduction [63]. In addition, ref. [48] emphasizes that expanding CE adoption through broader datasets, diverse theoretical perspectives, and multistakeholder collaboration is significant because it directly addresses the challenge of generalizability. By incorporating multiple viewpoints and larger evidence bases, such approaches help firms and policymakers move beyond sector-specific limitations, offering more reliable insights for overcoming systemic barriers to CSC adoption. However, the influence of these strategies remains contingent on effective coordination among stakeholders, which is often uneven across industries and regions.

Furthermore, research highlights the need to incorporate social factors into sustainability analysis, broaden industry scope, and apply advanced modeling techniques [64]. Other studies emphasize supply chain flexibility as a mediator of digital and circular capabilities [65] and call for integrated CE frameworks, adaptable remanufacturing systems, and digital factory technologies to close material loops while addressing legislative constraints [66]. Despite these advances, few studies explicitly connect CSC evaluation frameworks with SDGs principles, leaving a gap in understanding how operational and environmental performance can be jointly optimized to prevent waste and enhance resource efficiency. These gaps align with this paper’s aim to propose a data-driven decision-making framework for CSC, integrating MCDM methods with PESTEL to provide reliable insights for advancing circularity. To further enhance clarity,

Table 3. Selected MCDM techniques and SDG alignment.

MCDM Technique	Application Context	Related SDGs	Reference
AHP Risk-integrated MCDM	CSC risk assessment	SDG 12.5 (Waste Reduction)	[1]
BWM + FTOPSIS (Hybrid fuzzy approach)	Lean manufacturing tool selection (cement industry)	SDG 9 (Industry, Innovation, Infrastructure), SDG 12 (Responsible Consumption)	[19]
GSDM (Grey Stratified Decision-Making)	Social sustainability in supplier selection (electronics industry)	SDG 8 (Decent Work), SDG 12 (Responsible Consumption)	[11]
Hybrid MCDM (Fermatean fuzzy CRITIC-EDAS)	Reverse logistics provider selection	SDG 12 (Responsible Consumption), SDG 9 (Innovation)	[14]
Hybrid MCDM (Hesitant fuzzy linguistic)	Bioenergy technology sustainability assessment	SDG 7 (Clean Energy), SDG 12 (Responsible Consumption), SDG 8 (Decent Work)	[15]
Metaheuristic + LP-metric + supportive MCDM	Agri-food supply chain (saffron)	SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption)	[12]
TOPSIS	Readiness for disruptive technology in supply chain	SDG 9 (Industry, Innovation, Infrastructure), SDG 12 (Responsible Consumption)	[18]

summarizes selected MCDM techniques applied in CSC-related studies and their alignment with the SDGs. While the literature on MCDM is extensive, this table highlights representative contributions most relevant to the scope of this review.

By bridging operational performance and macro-environmental pressures, the literature review not only identifies gaps but also establishes the theoretical foundation for integrating BOCR, BWM, and FTOPSIS as complementary tools for CSC evaluation. This synthesis advances the field beyond descriptive accounts toward a structured, theory-informed decision-making approach.

To strengthen the theoretical background, this study is anchored in three complementary perspectives that, taken together, illuminate both the potential and the limitations of CSC evaluation. The Resource-Based View (RBV) [67] highlights how operational performance objectives can be understood as internal capabilities; however, recent extensions into digitalization and sustainability [68,69] raise the question of whether RBV’s original assumptions are sufficient to capture the complexity of green and digital transitions. The Dynamic Capabilities (DC) framework [70,71] adds a processual lens, explaining how firms sense, seize, and reconfigure resources in turbulent environments. However, while studies show its relevance to circular transitions and resilience [72,73], debates persist on whether DC risks becoming an umbrella term for any adaptive process. Institutional Theory [74] further complicates the picture by emphasizing how coercive, normative, and mimetic pressures shape organizational practices. Contemporary contributions [75,76] illustrate how regulation, peer norms, and recognition can accelerate sustainability adoption, but also reveal paradoxes such as superficial compliance. Taken together, these perspectives not only provide conceptual rigor but also expose unresolved tensions, between internal capabilities and external pressures, between adaptation and coherence, that justify the integrative BOCR–BWM–FTOPSIS framework proposed in this study.

Table 4. Selected studies anchored in theoretical perspectives.

Theory	References	Context	Contribution
Dynamic Capabilities	[71,72,73]	Continuous innovation; circular transitions; digitalization in SMEs	Explains how reconfiguration of resources sustains innovation and resilience; absorptive capacity and organizational mindfulness enhance dynamic capabilities in digital and circular contexts.
Institutional Theory	[74,75,76]	Organizations; digital innovation; ESG disclosure	Shows how coercive, normative, and mimetic pressures shape practices; peer norms influence competitiveness; institutional recognition can legitimize or distort sustainability adoption.
Resource-Based View	[67,68,69]	Strategy; ESG digitalization; CSC and MSMEs	Internal resources sustain competitive advantage; digitalization as an enabler of ESG disclosures; circular innovation explained through RBV in SMEs.

summarizes selected studies anchored in these theoretical perspectives, highlighting representative contexts and contributions most relevant to CSC evaluation.

3. Methodology

3.1. Research Design

MCDM refers to numerical methods that support decision makers in selecting among discrete alternatives. These methods evaluate options against multiple criteria to determine overall utility. Common approaches include the weighted sum model, weighted product model, among others [77]. MCDM is widely acknowledged as a fundamental branch of Operational Research, providing systematic approaches to incorporate multiple, often conflicting criteria into decision-making [78,79].

In this study, BWM supports CSC strategy evaluation [80,81,82]. More specifically, the selection of the BWM is motivated by its suitability for weighting criteria in complex evaluation contexts and by its proven capability in managing evaluation procedures [83]. While approaches such as AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), and DEMATEL (Decision-Making Trial and Evaluation Laboratory) are suitable for ranking or classifying alternatives, they typically involve accurate numerical data or extensive normalization procedures, which makes them less appropriate for the integrative evaluation proposed here. In contrast, BWM requires fewer pairwise comparisons, reduces the cognitive burden on experts, and ensures higher consistency in weight elicitation, which is particularly valuable in sustainability-oriented decision contexts where criteria are interdependent and often conflicting.

Following the weighting stage, the fuzzy extension of TOPSIS was adopted because it incorporates linguistic uncertainty into the ranking procedure, allowing experts to express judgments using qualitative terms that are converted into fuzzy numbers. This feature is especially relevant in CSC evaluation, where expert opinions often involve ambiguity and subjective interpretation. One of the justifications for choosing TOPSIS lies in its ability to identify compromise solutions based on the relative closeness to ideal and anti-ideal solutions. Within this context, MCDM is particularly suitable for evaluating CSC strategy, as its implementation involves the simultaneous consideration of operational performance dimensions and broader macro-environmental factors that are inherently interrelated and often conflicting. Accordingly, this paper assesses operational performance and PESTEL aligned with the SDGs. Figure 1 illustrates the conceptual framework of the proposed CSC evaluation model. The integration of operational performance and PESTEL factors is explicitly aligned with SDGs, since each dimension corresponds to sustainability objectives such as SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). This alignment ensures that the evaluation does not remain abstract but operationalizes SDG principles within CSC decision-making.

The BOCR [17] serve as a structural step, allowing the investigation of both positives (benefits) and negatives (costs), akin to benefit–cost analysis approaches [84,85,86,87]. In Figure 1, BOCR are defined as criteria. These criteria are organized into opposing pairs, where B is opposed to C, and O is opposed to R. In this classification, B and O represent positive categories that generate or refer to factors associated with the certainty or future possibility of revenue or profit [86,87]. The detailed descriptions of each criterion are presented as follows [17,86,87]:

• Benefits: Factors that ensure positive consistency for the company when financial, qualitative, and quantitative aspects are considered in the processes.
• Opportunities: Improvements in processes through the implementation of identified critical success factors, aiming at positive financial or operational outcomes.
• Costs: Elements that generate initial expenses for the company, designed to bring benefits in the medium to long term.
• Risks: Applications or initiatives that may not lead to positive outcomes when implemented.

The justification for adopting BOCR lies in its established role within MCDM, where it has been applied by [88] to systematically balance positive and negative dimensions in complex evaluations. This ensures that both advantages and drawbacks are explicitly considered, reinforcing the comprehensiveness and robustness of the proposed framework.

As illustrated in Figure 1, the operational performance and PESTEL factors are defined as alternatives. From a sustainability perspective, this integrated assessment provides a structured pathway to operationalize the SDGs within CSC decision-making. Operational performance relates to efficiency, quality, and flexibility, which are closely associated with responsible production and operational resilience (Table 1), while PESTEL factors (Table 2) reflect governance, regulatory, and societal and social conditions emphasized in the SDGs. Therefore, the combined analysis establishes a baseline for evaluating how CSC strategies contribute to sustainability objectives in a balanced and context-sensitive manner.

Following the conceptual foundation, the next step focuses on structuring the decision-making model by prioritizing the BOCR components through the BWM. This prioritization ensures consistency in weight elicitation while reducing the cognitive burden on experts when dealing with interrelated criteria. Several contributions explore the barriers and facilitators of CSC using the BWM approach, supporting its suitability for prioritizing complex and interdependent criteria in sustainability-oriented decision contexts. In this study, the application of BWM also reinforces SDGs principles, since prioritizing benefits, opportunities, costs, and risks helps organizations prevent waste and enhance resource efficiency while advancing circular strategies. To translate the approach into practice, the investigation examines operations of the chemical industry. For both MCDM approaches (BWM and TOPSIS), expert-driven purposive sampling was adopted, involving fifteen experts in supply chain and sustainability. Participants were consultants with over 15 years of professional experience in chemical manufacturing and related industries across the Americas, with academic backgrounds in business, scientific disciplines, environmental studies, and production. The selection criteria emphasized senior professional experience, academic specialization, and geographic diversity, which broadened perspectives and reinforced representativeness. Data collection followed a structured process: expert judgments were first elicited individually to reduce conformity bias and subsequently aligned through moderated discussion rounds, allowing consensus to emerge.

In the case of the fuzzy linguistic scales, a five-level scale (very low–very high) was adopted and converted into triangular fuzzy numbers following established practices. Before the evaluation, experts received detailed instructions and illustrative examples to ensure consistent interpretation of the linguistic terms. Individual judgments were checked for coherence, and any inconsistencies were clarified during the consensus rounds, which ensured uniform interpretation across participants.

This consistent population and procedure ensured reliability across the weighting and ranking stages. The justification for the chosen sample size is grounded in expert-driven decision-making methods, which prioritize domain expertise, representativeness, and judgment quality over large sample sizes rather than statistical generalization [89,90,91]. This process ensured consistency and coherence of judgments while maintaining transparency and methodological rigor [92]. Figure 2 presents the methodological flowchart, summarizing the main steps of the CSC evaluation process.

Although the methodology assumes independence among criteria, it is acknowledged that CSC factors are inherently interdependent. This limitation is noted, and potential bias was mitigated through expert consensus rounds and the use of fuzzy linguistic scales, which capture ambiguity and overlapping judgments. Moreover, the BOCR structuring and integration of PESTEL dimensions provide a systemic perspective that partially reflects these interdependencies.

3.2. Best-Worst Method

The BWM is adopted in this study to prioritize the BOCR criteria. Considering the complexity of operations and CSC, which encompasses assessment of capability and collaborative willingness, BWM assures decision makers that they can define their relative preference by enhancing consistency and reducing the need for extensive comparisons [93]. Although this study does not empirically compare BWM with alternative MCDM methods, such a comparative analysis represents a valuable avenue for future research. In this study, BWM is implemented according to the process described in [93]:

A.

Identify the criteria, represented as $\left\{c_1,c_2,\dots ,c_n\right\}$.

B.

Identify the criterion considered most important and the one deemed least relevant.

C.

Conduct pairwise judgments among the criteria, applying a 1–9 rating scale. The best-to-other representation is expressed as $A_B=(a_{B1},a_{B2},\dots ,a_{Bn})$, where $a_{Bj}$ denotes the preference of B over j.

D.

The worst-to-others representation is expressed as $A_w={\left(a_{1w},a_{2w},\dots ,a_{nw}\right)}^T$, where T indicates the transpose of the vector.

E.

Determine the optimal set of weights $\left(w_1^{\ast },w_2^{\ast },\dots ,w_n^{\ast }\right)$.

F.

Execute an integrity check. The consistency ratio (CR) is calculated using ${\xi }^{\ast }$, where ${\xi }^{\ast }$ represents the optimal consistency value obtained from the linear optimization model. Equation (1) presents the calculation of the CR, which measures the reliability of the weights obtained in BWM. The parameter ${\xi }^{\ast }$ represents the maximum deviation in the pairwise comparisons, while CI denotes the consistency index.

$$CR={\displaystyle \frac{{\xi }^{\ast }}{CI}}$$

(1)

The CI in the BWM is defined on a 1–9 preference scale, with values ranging approximately from 0 up to 5.53, as reported in the methodological literature [93,94]. This ensures that the derived weights maintain logical coherence and that the decision-making process remains reliable.

3.3. Fuzzy TOPSIS

Once the BOCR priorities are defined, fuzzy logic is introduced to manage uncertainty in expert evaluations and to strengthen the multi-criteria analysis. Following [95,96] the process begins with:

A.

The criteria are defined, and a fuzzy scale is introduced. Performance levels are expressed through linguistic terms mapped to triangular fuzzy values. The scale progresses from no influence (values close to 0), through low and medium influence (intermediate values between 0.25 and 0.75), up to high and strong influence (values approaching 1.0). This linguistic-to-numeric mapping provides a structured way to capture subjective judgments, as reported in the methodological literature [95,97].

B.

Build the fuzzy decision matrix $(l_{ij},m_{ij},u_{ij})$. In the fuzzy decision matrix, $l_{ij},m_{ij},{ \mathrm{a}\mathrm{n}\mathrm{d} u}_{ij}$ represent the lower, middle, and upper bounds of the influence of criterion $i$ on criterion $j$, where $i$ is the influencing criterion and $j$ is the influenced criterion.

C.

Derive the area center numbers $\left(l,m,u\right)$ using Equation (2) [98]. Equation (2) derives the area center number (AC), summarizing the fuzzy judgments into a single representative value.

$$AC={\displaystyle \frac{(u-l)+(m-l)}{3}}+l$$

(2)

This step condenses the uncertainty of expert inputs into a usable score for weighting.

D.

Adjust the weights of the criteria. Define ${AC}_j$ for j Equation (3).

$$w_j={\displaystyle \frac{{AC}_j}{{\sum }_{j=1}^n{AC}_j}}$$

(3)

where $w_j$ denotes the normalized weight of criterion $j, {AC}_j$ represents the adjusted coefficient value associated with criterion j, and i and j denote the influencing and influenced criteria, respectively. These weights are later used in TOPSIS to build the proximity index.

E.

Employ TOPSIS to sort the choices. After fuzzy scores are defined, the method ranks them by closeness to the best reference. Each option is evaluated between the positive ideal and the negative ideal, with its value based on distance to these points [99].

E.1

Normalize option scores Equation (4).

$$v_{ij}=\left({\displaystyle \raisebox{1ex}{$d_{ij}$}\!\left/ \!\raisebox{-1ex}{$\sqrt{{\displaystyle {\sum }_{i=1}^n}{d}_{ij}^2}$}\right.}\right)$$

(4)

E.2

Obtain weighted scores using $w_j$ Equation (5).

$${{dn}_{ij}=w_j\times v}_{ij}=w_j\times \left({\displaystyle \raisebox{1ex}{$d_{ij}$}\!\left/ \!\raisebox{-1ex}{$\sqrt{{\displaystyle {\sum }_{i=1}^n}{d}_{ij}^2}$}\right.}\right)$$

(5)

E.3

Equations (6) and (7) is used to define the sets (IS⁺) and (IS⁻) [17]

$${\mathrm{IS}}^{+}=\left\{{max}_i{dn}_{ij}| j=\mathrm{1,2},\dots ,m\right\}=\left\{{dn}_1^{+},{dn}_2^{+},\dots {dn}_j^{+},\dots ,{dn}_m^{+}\right\}$$

(6)

$${\mathrm{IS}}^{-}=\left\{{min}_i{dn}_{ij}| j=\mathrm{1,2},\dots ,m\right\}=\left\{{dn}_1^{-},{dn}_2^{-},\dots {dn}_j^{-},\dots ,{dn}_m^{-}\right\}$$

(7)

E.4

Distances to (IS⁺) and (IS⁻) Equations (8) and (9).

$$D_i^{+}=\sqrt{{\sum }_{j=1}^m{\left({dn}_{ij}-{dn}_j^{+}\right)}^2}$$

(8)

$$D_i^{-}=\sqrt{{\sum }_{j=1}^m{\left({dn}_{ij}-{dn}_j^{-}\right)}^2}$$

(9)

E.5

Proximity index (CC_i) per option Equation (10).

$${CC}_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}$$

(10)

By completing the FTOPSIS procedure, the methodological framework is fully established. The next section applies this integrated model, combining operational performance and PESTEL, to assess whether implementation of the CSC is aligned with the SDGs.

4. CSC Framework Application

4.1. Evidence from Case Analyses

Evidence from case analyses has highlighted the relevance of MCDM approaches for solving operational and environmental problems in applied CE scenarios. This study focuses on an organization that is among the leading multinational firms in chemical production and a leading provider of chemical solutions in Latin America.

Although the chemical industry is environmentally challenging, it was deliberately chosen as a case context because of its high resource intensity, complex supply chains, and stringent regulatory pressures. These characteristics make it one of the sectors where CSC strategies are most urgently needed and potentially most impactful. By addressing sustainability in such a demanding environment, the framework is tested under conditions that maximize both the risks and the opportunities of CSC adoption.

4.2. BWM Results

BWM considered the BOCR criteria from Section 3.1. Benefits (B) were identified by experts as the most relevant criterion of the CSC approach. Conversely, Risks (R) were explicitly defined as the worst criterion, since risk management is generally regarded as a prerequisite for operations rather than a source of strategic advantage. Define $w_B,w_O,w_C, \mathrm{a}\mathrm{n}\mathrm{d} w_R$ as the weights of the Best (B), the remaining criteria (O and C), and the Worst criterion (R) [60]. These are presented in

Table 5. Results of pairwise judgments.

Judgment (BO/OW)	B	O	C	R
BO (Best = B, O = Others)	1	4	3	7
OW (O = Others, Worst = R)	2	3	4	1

.

This application uses $\xi$ detailed in Equations (11)–(13):

$$\min \xi$$

(11)

Based on the evaluations comparing (B to others) Equation (12):

$$\left\{\begin{array}{c}\left|{\displaystyle \frac{w_B}{w_O}}-4\right|\le \xi \\ \left|{\displaystyle \frac{w_B}{w_C}}-3\right|\le \xi \\ \left|{\displaystyle \frac{w_B}{w_R}}-7\right|\le \xi \end{array}\right.$$

(12)

Based on the evaluations comparing (others to R) Equation (13):

$$\left\{\begin{array}{c}\left|{\displaystyle \frac{w_B}{w_R}}-2\right|\le \xi \\ \left|{\displaystyle \frac{w_O}{w_R}}-3\right|\le \xi \\ \left|{\displaystyle \frac{w_C}{w_R}}-4\right|\le \xi \end{array}\right.$$

(13)

Normalization and non-negativity Equations (14) and (15):

$$w_B+w_O+w_C+w_R=1$$

(14)

$$w_j\ge 0 \forall j$$

(15)

The optimal value obtained from this optimization problem Equation (11) is denoted by ξ*, which is subsequently used to compute the consistency ratio defined in Equation (1). The solver was applied, producing the criteria weights. The consistency ratio is 0.119, which is below the threshold of 0.246 for a four-criteria model. The distribution of weights and the comparative importance of the criteria are summarized in

Table 6. Weight distribution across criteria.

Criteria	B	O	C	R
Criteria weights	0.32	0.24	0.31	0.13

.

From a managerial perspective, the BOCR results reveal that benefits (0.32) and costs (0.31) received relatively similar weights. This outcome reflects the expert interpretation of the specific case company, where potential gains from CSC strategies are closely tied to substantial investments and compliance costs. In this context, managers perceive benefits and costs as nearly balanced, since opportunities for value creation are inseparable from the financial and operational burdens of implementation. It is important to note that this finding is case-specific and does not necessarily represent the chemical sector as a whole. These BWM results are applied in the subsequent phase, using FTOPSIS.

4.3. FTOPSIS Results

The evaluation began with the application of fuzzy logic to the alternative’s operational performance and PESTEL in relation to the BOCR criteria, which served as input for the TOPSIS calculations. Subsequently, the normalization of the alternatives was performed and can be observed in

Table 7. Normalized assessment of operational performance.

Operational Performance/Criteria	B	O	C	R
Quality	0.571	0.525	0.534	0.497
Speed	0.505	0.519	0.500	0.504
Dependability	0.428	0.463	0.468	0.492
Flexibility	0.486	0.491	0.497	0.507

and

Table 8. Normalized assessment of PESTEL.

PESTEL/Criteria	B	O	C	R
Political	0.457	0.460	0.482	0.517
Economic	0.469	0.515	0.481	0.475
Social	0.545	0.512	0.481	0.536
Technological	0.523	0.511	0.552	0.468
Environmental	0.516	0.516	0.541	0.428
Legal	0.467	0.477	0.520	0.650

.

In Table 7, operational performance shows a balanced distribution across BOCR. Quality is strongest under benefits (0.571), flexibility is slightly higher under risks (0.507), while dependability remains the weakest overall.

In Table 8, PESTEL factors reveal sharper contrasts. Legal dominates under risks (0.650), social is relevant in both benefits and risks, and technological and environmental stand out under control. The weight-normalized assessment was also performed for the alternatives in relation to the BOCR criteria, and

Table 9. Weight-normalized assessment of operational performance.

Operational Performance/Criteria	B	O	C	R
Quality	0.183	0.126	0.165	0.065
Speed	0.162	0.125	0.155	0.065
Dependability	0.137	0.111	0.145	0.064
Flexibility	0.155	0.118	0.154	0.066

and

Table 10. Weight-normalized assessment of PESTEL.

PESTEL/Criteria	B	O	C	R
Political	0.146	0.111	0.150	0.067
Economic	0.150	0.124	0.149	0.062
Social	0.174	0.123	0.149	0.070
Technological	0.167	0.123	0.171	0.061
Environmental	0.165	0.124	0.168	0.056
Legal	0.149	0.115	0.161	0.085

summarize the results.

In Table 9, operational performance shows a differentiated distribution once weights are applied. Quality remains the strongest under benefits (0.183), while Flexibility records the highest value under risks (0.066). Dependability continues to present the lowest values across all criteria, reinforcing its weaker relative influence.

In Table 10, the PESTEL factors highlight more variation. Social achieves the highest value under Benefits (0.174), while legal stands out under risks (0.085). Technological (0.171) and environmental (0.168) dominate under control, confirming their importance in governance and sustainability. These results emphasize that, after weighting, PESTEL factors show sharper contrasts compared to operational performance, underscoring their differentiated strategic impact. Following the evaluation process, the focus now turns to the results.

Table 11. Proximity index of operational performance.

Proximity Index	Value	Operational Performance	Rank
CC1	0.702	Quality	1st
CC2	0.561	Speed	2nd
CC3	0.299	Dependability	4th
CC4	0.432	Flexibility	3rd

and

Table 12. Proximity index of PESTEL.

Proximity Index	Value	PESTEL	Rank
CC1	0.410	Political	5th
CC2	0.513	Economic	3rd
CC3	0.977	Social	1st
CC4	0.509	Technological	4th
CC5	0.517	Environmental	2nd
CC6	0.359	Legal	6th

present the proximity index values for both operational performance and PESTEL alternatives.

In Table 11, operational performance is ranked by the proximity index, with quality in first place (0.702), followed by speed, flexibility, and dependability. This result suggests that quality is the most decisive dimension in distinguishing performance, while dependability contributes the least to the final prioritization.

In the sensitivity analysis of the benefits criterion (Figure 3), the vertical black line represents an additional 10 percentage points assigned to the benefits criterion, increasing it from 0.32 to 0.42. The results show that the ranking of operational performance remains unchanged for any value above or below this threshold, confirming the stability of the proximity index under this criterion.

The sensitivity (Figure 3) further reinforces these findings. As the benefits increase, the slope of the quality line is the steepest, confirming its dominant role in shaping operational outcomes. Speed and flexibility show moderate growth, suggesting they are relevant but less decisive compared to quality. In contrast, dependability remains the least sensitive dimension, with a flatter trajectory, indicating limited influence on overall performance prioritization.

This analysis highlights that improvements in quality generate the most significant impact on operational performance, while gains in dependability have relatively minor effects. Therefore, strategic efforts should prioritize quality enhancement, supported by speed and flexibility, to maximize operational benefits.

In Table 12, the PESTEL factors show sharper contrasts. Social achieves the highest ranking (0.977), indicating its dominant role in shaping outcomes. Environmental, economic, and technological occupy intermediate positions, while political and legal appear least influential. This highlights that social factors are perceived as the most critical drivers, whereas regulatory and political aspects have a limited impact in the final evaluation.

The predominance of the social dimension can be explained by its balanced distribution across the BOCR categories. As shown in Table 10, social achieved the highest value under benefits (0.174) while also maintaining relevant contributions under costs (0.149) and risks (0.070). In contrast, technological and environmental factors concentrated their strength under control, and legal stood out only under risks. Political remained consistently weaker. This broader and more consistent influence across multiple criteria explains why the social dimension achieved the highest proximity index (0.977) in Table 12, prevailing over the other PESTEL factors. To confirm the robustness of this predominance, Figure 4 presents the sensitivity analysis of the benefits criterion, showing that the ranking of PESTEL factors remains unchanged even when the weight of benefits is varied.

In the sensitivity analysis of the benefits criterion (Figure 4), the vertical black line represents an additional 10 percentage points assigned to the benefits criterion, increasing it from 0.32 to 0.42. The results show that the ranking of PESTEL factors remains unchanged for any value above or below this threshold, confirming the stability of the proximity index under this criterion.

The sensitivity (Figure 4) further reinforces these findings. As the benefits increase, the slope of the social line is the steepest, confirming its dominant role in shaping outcomes. Environmental, economic, and technological display moderate growth, suggesting they are relevant but less decisive compared to social. In contrast, political and legal remain the least sensitive dimensions, with flatter trajectories, indicating limited influence on overall prioritization.

This analysis highlights that improvements in social factors generate the most significant impact on outcomes, while gains in political and legal dimensions have relatively minor effects. Therefore, strategic efforts should prioritize social drivers, supported by environmental, economic, and technological aspects, to maximize overall benefits.

To support the interpretation of the results, radar charts were employed to visually summarize the performance of the alternatives. Figure 5 and Figure 6 present the radar-based representations of the final evaluation outcomes for operational performance and PESTEL factors, respectively, allowing a comparative and multidimensional assessment of the alternatives.

Figure 5 illustrates the radar chart of operational performance, highlighting the relative positioning of quality, speed, dependability, and flexibility. The graphical representation shows a coherent performance pattern across dimensions, confirming the consistency of the ranking obtained through the FTOPSIS proximity index and facilitating the comparison among operational attributes.

Figure 6 presents the radar chart of PESTEL, synthesizing the relative importance of PESTEL factors. The shape and distribution of the radar profiles emphasize the dominance of social and environmental dimensions, while political and legal factors occupy comparatively smaller areas, which is consistent with the ranking results reported in Table 12.

Taken together, the analyses demonstrate that both operational performance and PESTEL factors contribute distinct and complementary perspectives to the proposed framework. While operational performance emphasizes internal attributes such as quality, speed, and flexibility, PESTEL highlights external drivers influencing CSC. The radar charts in Figure 5 and Figure 6 enhance the interpretability of the results by providing an integrated visual comparison of the alternatives, reinforcing the robustness and transparency of the evaluation process. Rather than emphasizing individual numerical rankings, the findings reveal a clear strategic pattern: circular decision-making is guided by value-oriented criteria, understood here as strategic benefits and opportunities that add organizational and societal value, while risks and reliability are treated as necessary boundary conditions. Reliability is considered a baseline requirement, whereas dependability was assessed as an operational performance dimension, both emphasizing the importance of consistent operations in CSC strategies. This consolidated evidence provides a solid foundation for advancing to the discussion of broader implications and managerial insights.

The obtained rankings primarily reflect the specific case company within the chemical industry, rather than the sector as a whole. These outcomes are case-specific, shaped by the company’s operational context and expert judgments. Nevertheless, the proposed framework is potentially generalizable to other industrial settings, since the methodological logic of integrating BOCR, BWM, and FTOPSIS can be applied across diverse sectors facing sustainability challenges.

It is acknowledged that expert-based evaluations inherently involve a degree of subjectivity. In this case application, the prioritization results were shaped by the judgments of purposively selected experts. To mitigate subjectivity, the study adopted structured elicitation procedures and consensus rounds to align interpretations. These measures enhanced coherence and reliability of the evaluations, while recognizing that expert perspectives remain a critical source of knowledge in sustainability-oriented decision-making.

These prioritization patterns are consistent with previous MCDM-based research on sustainability and CSC [100,101,102], which emphasize that social acceptance and environmental pressures are decisive drivers of organizational change. The near balance between benefits and costs also reflects findings in prior studies that highlight the trade-offs between value creation and compliance costs in sustainability-oriented strategies. This theoretical alignment reinforces the validity of the observed results and provides a solid foundation for the subsequent discussion.

5. Discussion

The findings suggest that decision-making in CSC strategy is primarily driven by the balance between operational performance and PESTEL feasibility. The relative prominence of BOCR criteria indicates that organizations pursuing circularity tend to emphasize benefits and opportunities, while costs are carefully managed and risks acknowledged but not dominant. This pattern reflects a pragmatic approach to circular transition, where practical feasibility guides strategic evaluation.

From an operational perspective, the results indicate that performance differentiation is mainly associated with factors related to output quality and process effectiveness, whereas reliability-oriented aspects appear to function as minimum requirements rather than competitive differentiators. This suggests that, within CSC strategy, maintaining consistent operations is perceived as a baseline condition, while improvements in quality and adaptability are more influential in shaping strategic priorities. Importantly, these results corroborate established perspectives in operations strategy [34], reinforcing the robustness of this interpretation.

The finding that dependability ranked lowest has important implications for resilience and long-term supply chain stability. While reliability is treated as a baseline requirement, its limited differentiation suggests that resilience-oriented practices may be undervalued in strategic prioritization. This highlights a managerial risk: overlooking dependability could weaken robustness in the face of disruptions, underscoring the need to integrate resilience explicitly into CSC strategies.

The results also reveal the interaction between internal operational capabilities and external macro-environmental pressures. Improvements in quality and adaptability strengthen the organization’s ability to respond to social and environmental demands, while external pressures amplify the need for operational excellence. This interplay demonstrates that CSC strategies are shaped by the dynamic alignment of internal strengths with external contextual challenges.

In contrast, the assessment of PESTEL reveals stronger differentiation effects, particularly in socially and environmentally oriented factors. This finding underscores the importance of external contextual conditions in shaping circular strategies, as social acceptance, environmental pressures, and stakeholder expectations can significantly influence the feasibility. At the same time, social sustainability extends beyond acceptance, encompassing broader impacts such as equity, labor conditions, and community well-being, which determine the legitimacy and long-term viability of CSC initiatives. Economic and technological aspects act as enabling conditions, while political and legal dimensions appear to exert a more indirect influence, often perceived as exogenous constraints rather than decision drivers. Our results corroborate the evidence reported by [103], who reinforce this view by highlighting social acceptance and environmental pressures as decisive in advancing source-separating sanitation systems.

The prominence of social and environmental dimensions can be theoretically explained by institutional and stakeholder perspectives, which emphasize legitimacy, social acceptance, and ecological responsibility as critical drivers of organizational change. Within CSC evaluation, these dimensions act not only as external pressures but also as normative forces that shape organizational priorities, reinforcing the view that social legitimacy and environmental stewardship are prerequisites for long-term adoption of circular practices.

The prioritization patterns identified in this study are consistent with results reported in previous MCDM-based research on sustainability and CSC [101,102,103]. The contribution to the literature lies in demonstrating how a structured decision-making framework can integrate operational performance and PESTEL factors in the context of CSC strategy. By applying an MCDM approach that combines BWM and FTOPSIS, the study advances methodological applications for prioritizing interrelated sustainability factors. Furthermore, the integration of environmental and operational factors into a unified model provides a novel perspective that supports strategic evaluation aligned with the SDGs. In addition, the findings demonstrate how CSC strategies can be operationalized in ways that reinforce SDGs by preventing waste and enhancing efficiency across supply chain processes. Nevertheless, we acknowledge that findings from this case should be interpreted with caution when generalizing to other sectors or regions.

Beyond methodological integration, the framework contributes to the CE and sustainability management literature by illustrating how operational and contextual dimensions can be jointly prioritized. This dual perspective advances theoretical understanding of CSC strategies as socio-technical systems, where organizational performance and external legitimacy are co-dependent. In this way, the study enriches debates on how circularity can be embedded into mainstream management practices.

Managerial Implications

This study offers several managerial implications for organizations advancing CSC strategy. First, managers should prioritize investments in output quality and process effectiveness, as these factors are perceived as key differentiators in performance. Second, reliability must be maintained as a baseline requirement, ensuring that circular practices do not compromise operational consistency. Third, the prominence of social and environmental factors calls for proactive stakeholder engagement, transparent communication, and alignment with environmental goals to strengthen legitimacy. Managers should also integrate broader social considerations into decision-making, ensuring that CSC strategies contribute to fair labor practices, community well-being, and equity, thereby aligning operational choices with SDG-oriented outcomes. Fourth, economic and technological factors should be leveraged as enabling conditions, guiding resource allocation toward innovations that support adaptability and efficiency. Finally, the integrated framework proposed in this study offers managers a structured tool to balance feasibility with strategic ambition, supporting decision-making processes that are both pragmatic and aligned with the SDGs.

In practical terms, managers can operationalize the framework by applying it to supplier selection, investment prioritization, and stakeholder engagement processes. For instance, when evaluating new suppliers, the framework can guide decisions by balancing quality and flexibility with social and environmental compliance. Similarly, investment choices in cleaner technologies can be assessed by weighing benefits against costs, while stakeholder dialogs can be structured around the social and environmental dimensions highlighted in the evaluation. These concrete applications enhance the usability of the framework in real decision-making environments.

Beyond managerial relevance, the findings also carry policy implications. The prominence of social and environmental dimensions highlights the need for public policies that foster stakeholder engagement, community well-being, and ecological responsibility as prerequisites for CSC adoption. At the same time, the weaker influence of political and legal factors suggests that regulatory frameworks and institutional support should be strengthened to provide clearer incentives and reduce uncertainty. Policymakers are therefore positioned to shape a decisive role by aligning regulations with sustainability goals, promoting transparency, and supporting innovation, thereby creating an enabling environment for CSC strategies.

6. Conclusions

This study demonstrates the applicability of an integrated BWM–FTOPSIS framework as a structured decision-support tool for CSC, capable of simultaneously capturing internal operational performance priorities and external contextual pressures under the BOCR perspective. The framework highlights how operational performance dimensions and external sustainability drivers interact, offering a coherent basis for aligning circular strategies with organizational objectives and relevant SDGs. Beyond its practical applicability, the study provides a theoretical contribution by demonstrating how the integration of BOCR, BWM, and FTOPSIS offers a comprehensive lens for CSC evaluation. This integration advances decision-making theory by linking value-oriented criteria with structured prioritization methods, thereby enriching sustainability and CE research with a multi-dimensional analytical framework.

In addition, the findings advance CSC theory by framing CSC as socio-technical systems, where operational performance and external legitimacy are co-dependent. This perspective highlights that resilience, social acceptance, and environmental stewardship are not peripheral but central to strategic prioritization. For the MCDM literature, the study extends the theoretical scope of decision-making models by showing how fuzzy BOCR-based evaluation can incorporate both internal capabilities and external contextual pressures, moving beyond efficiency-oriented assessments toward legitimacy-oriented evaluations.

The models’ outcomes are based on evaluations provided by experts in a specific CSC scenario, potentially restricting the broader consideration of the outputs [1]. However, the expert number is methodologically consistent with expert-driven MCDM approaches, where analytical depth and domain expertise are prioritized over large samples. Although the model can be applied to different industries, the results will naturally differ due to sector-specific characteristics [1]. The relative importance of BOCR may vary depending on organizational maturity in circular practices, regional context (such as regulatory frameworks, market structures), and institutional environments. Nevertheless, the study is subject to limitations. The models’ outcomes are based on evaluations provided by experts in a specific CSC scenario, which constrains the generalizability of the findings. The reliance on expert-driven assessments also introduces a degree of subjectivity, even though structured elicitation and consensus procedures were adopted to mitigate bias. Moreover, the context dependency of CSC strategies implies that results will naturally differ across industries, regions, and institutional environments. Future research should also extend the framework to diverse industrial contexts in order to validate its applicability across different supply chain environments.

In terms of future research avenues, future research could analyze the application of ANP within the MCDM method to more effectively reflect uncertainty and interdependencies. It is also important to distinguish between social acceptability, which influences feasibility through stakeholder legitimacy, and broader social benefits such as equity, labor conditions, and community well-being, which determine the long-term viability of CSC initiatives. In addition, as suggested by an anonymous reviewer, future research could extend the framework by integrating Industry 5.0 concepts, such as human-centric supply chains, AI-enabled circular systems, and resilient smart manufacturing ecosystems. These perspectives align with recent contributions in the literature, which emphasize the role of Industry 5.0 in fostering sustainable and resilient industrial transformation [26,27].

An extended assessment may contribute to appraising key operational performance and PESTEL considerations regarding how circular approaches are integrated into the SDGs and supply chain strategies. The framework aligns particularly with SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure), while also supporting SDG 13 (Climate Action) through waste prevention and efficiency gains. While the alignment with SDGs is conceptual, the framework also offers operational pathways. For instance, prioritizing quality and adaptability directly supports SDG 12 by reducing waste and improving resource efficiency. The emphasis on technological and environmental factors contributes to SDG 9 by guiding investments in innovation and cleaner production. Finally, the prominence of social sustainability reinforces SDG 13 by linking climate action with community well-being and stakeholder legitimacy. These examples illustrate how the evaluation results can be translated into actionable strategies that operationalize SDG principles within CSC contexts. By explicitly linking CSC evaluation with SDG principles, the study contributes to bridging a critical gap in the literature, showing how structured decision-making can simultaneously advance circularity, sustainability, and waste prevention.