Developing the supply chain of tomorrow: a path to resilience and sustainability Open Access

Resilient sustainable supply chains (RSSC) are gaining traction as supply chains have become vulnerable to disruptions and environmental scrutiny. By improving the resiliency and sustainability of the supply chains simultaneously, organizations can ensure long-term success and competitiveness in a rapidly changing global marketplace. An RSSC aims at achieving a trade-off among economic viability, social responsibility and environmental sustainability (Negri et al., 2024; Chowdhury et al., 2012). RSSC considers the entire supply chain (SC), i.e. from raw materials to end-of-life disposal to reduce adverse impacts, optimize positive outcomes and enhance responsiveness (Patidar et al., 2023a). As per Chowdhury et al. (2012) “A Resilient Sustainable Supply Chain is the management of resources with a view to meeting stakeholders' expectations so as to achieve high resilience and subsequent sustainability of organizations supply chain”.

The literature on supply chain management has focused on two key dimensions: supply chain resilience and sustainability (Negri et al., 2021). However, these aspects have mostly been studied in isolation, with limited exploration of how they can be effectively integrated to build a resilient sustainable supply chain. Researchers and practitioners have examined supply chain resilience, which refers to the ability of a supply chain to withstand and recover from disruptions and unexpected events, such as natural disasters, geopolitical conflicts or supply chain disruptions (Negri et al., 2021; Manurung et al., 2023; Zhu and Wu, 2022). Resilience strategies involves redundancy, flexibility, and adaptive capability within the supply chain to ensure business continuity in odd times (Negri et al., 2021). On the other hand, sustainability has been a dominant area of focus, emphasizing environmentally and socially responsible practices that focus on negative impact of SC on the environment and society (Negri et al., 2021; Oubrahim and Sefiani, 2024). Sustainable supply chain initiatives aim to minimize resource consumption, reduce greenhouse gas emissions, promote ethical sourcing and ensure fair treatment of workers (Negri et al., 2021). Some studies have attempted to combine resiliency and sustainability within the supply chain context, but these efforts have primarily centred on supply chain network design (López-Castro and Solano-Charris, 2021). Such research often focuses on optimizing the supply chain structure to achieve both resilience and sustainability objectives, without providing a framework that considers other critical factors and indicators (Negri et al., 2021). The research gap emerges from the absence of a holistic framework that not only addresses the integration of resilience and sustainability but also provides a set of clear indicators and factors essential for building a resilient sustainable supply chain (Negri et al., 2021; Warmbier et al., 2022; Manurung et al., 2023; Zhu and Wu, 2022). The SCs should be resilient and sustainable, which is clear to the organizations (what) but how to develop a resilient and sustainable supply chain is not clear (how). Therefore, it is important to know the common factors of resiliency and sustainability in a supply chain and how these factors integrate to support each other. There is a need to understand how resilience and sustainability can be amalgamated, leading to a more flexible, agile, robust and environmentally friendly supply chain (Yadav and Kumar, 2023; Singh and Modgil, 2025). The framework will provide insights and guidance to organisations and policy makers seeking to build a resilient sustainable supply chain.

Despite an increasing number of studies on resilient supply chains, limited empirical research explores how resilience mediates the relationship between antecedents (such as visibility, flexibility and collaboration) and sustainability in the Indian manufacturing context. Moreover, circularity and digitalization have been underexplored as contributors to sustainable outcomes in developing economies.

This calls for research that may identify factors that affect resilience and sustainability and assess their relationships, which leads to the emergence of the following research questions:

To answer these research questions, the study employs exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for validating the measurement model, followed by structural equation modelling (SEM) to test the hypothesized relationships.

The study contributes theoretically by integrating resilience as a mediating construct across multiple antecedents of sustainability. From a practical standpoint, the findings offer actionable insights for practitioners to improve visibility, flexibility, and collaborative mechanisms within their supply chains to achieve sustainability. This study develops and empirically validates a framework that integrates resilience as a mediating construct in a supply chain. It extends prior research by examining both linear and circular models and linking theoretical underpinnings to practical applications in the Indian industrial sector.

The rest of the article is structured as follows: Section 2 presents the literature review and development of the conceptual framework. Section 3 discusses the research methodology and model validation. Section 4 presents the results. Section 5 discusses the findings, theoretical and managerial implications. Section 6 concludes the article, highlighting its limitations and future research directions.

In the extant literature, a paucity was evident for the resilient sustainable supply chain. Though the majority of the studies focus on network designing but understanding resilient sustainable supply chain in terms of theoretical and conceptual aspects is very little focused (Patidar et al., 2023b; Michel-Villarreal, 2023). Table 1 shows the summary of key existing studies and comparison with the current study.

Primarily, factors and sub-factors were identified from the literature. This provided a list of seven factors and their corresponding 38 sub-factors. Furthermore, two factors – supply chain resiliency (SCRes) and sustainable supply chain performance (SSCP) with their corresponding sub-factors (four for SCRes and eight for SSCP) were adopted from Brandon-Jones et al. (2014) and Bag et al. (2020), respectively. The scales for these two constructs were adopted without any change. These factors and sub-factors (Table 2) were identified in conjunction with the next section – development of hypotheses.

The development of the research hypotheses is as follows:

Supply chain visibility refers to the ability to track and monitor products and materials as they pass various stages of the supply chain (Brau et al., 2023; Zimmermann et al., 2025). It is highly important as organisations demand to optimize their supply chains, reduce costs and improve customer satisfaction (Gunasekaran et al., 2004; Rainer et al., 2025). The key factors that create supply chain visibility are information sharing, data analytics capability and facility protection using devices.

The data acquired from the devices used for facility protection helps in achieving supply chain visibility (Al-Khatib, 2023). These include IoT devices and other devices such as security cameras, motion sensors and access control systems to monitor facilities and ensure that products and materials are secure. Protecting facilities from theft and other threats, organisations can minimize, reduce delays and enhance responsiveness to ensure that products are delivered on time and in good condition (Falagara Sigala et al., 2022). Another example of facility protection using devices is the use of video surveillance systems in warehouses and distribution centres to detect potential security threats and respond quickly to cope with potential disruptions (Raja Santhi and Muthuswamy, 2022).

A flexible supply chain quickly responds to the changes in customer demands, shifts in market conditions, and disruptions in the SC (Benzidia and Makaoui, 2020; Singh and Modgil, 2025). A flexible SC can mitigate disruption impact and confirm business continuity (Azadegan et al., 2020; Singh and Modgil, 2025). Flexibility is a critical enabler of supply chains, allowing firms to adapt to disruptions effectively. It supports risk management while maintaining continuity in operations. Imparting flexibility helps organizations to respond quickly to disruptions, such as pandemics, and economic depressions (Raja Santhi and Muthuswamy, 2022). It also enables organizations to minimize the impact of their supply chain operations on the environment (Ivanov, 2021).

Supply chain collaboration is the partnership and coordination among different players in the supply chain, namely, suppliers, manufacturers, distributors and customers (Jain et al., 2009; Maheshwari et al., 2025). Supply chain collaboration facilitates information sharing, aligning of goals and combining efforts to achieve objectives to improve supply chain performance (Wong et al., 2020). Collaboration helps in cost reduction, customer satisfaction enhancement and ensuring a resilient sustainable supply chain. Moreover, it also facilitates for effective risk management and information sharing about potential risks such as natural disasters, pandemics or economic downturns (Laufs and Waseem, 2020). This leads organizations to work together and mitigate the impact of disruptions, thereby ensuring business continuity. Furthermore, collaboration between supply chain stakeholders facilitates the sharing of information about the resource utilisation, energy consumption, waste generation, and the social impact of operations, thereby promoting sustainability (Esmaeilian et al., 2020). SC collaboration supports better decision-making by allowing organizations to share information and align their goals (Wong et al., 2020; Seo et al., 2025). Through collaboration, organisations can make informed decisions about supply chain optimisation, cost reduction and customer satisfaction enhancement simultaneously.

Effective supply chain control is required for increasing efficiency, reliability and performance of a supply chain (Francisco and Swanson, 2018; Wang and Zhang, 2025). It is vital for enabling effective responses to risks and disruptions. It ensures that operations remain aligned with sustainability goals under uncertain conditions. SC control helps organizations in operations monitoring and potential risk identification so that they can take preventive measures to mitigate the risk impacts (Aqlan and Lam, 2015; Ahuja and Kaur, 2025). Modern supply chains are looking for supply chain control towers that help organizations to monitor and manage resource utilisation, energy consumption and waste generation (Annosi et al., 2021). This helps organizations in environmental footprint reduction and sustainable operations.

SC circularity is managing supply chain operations in a closed-loop system, where wastage of resources is reduced, and the value of materials is preserved and extended (Farooque et al., 2019; Kreye, 2025). The supply chain circularity aims at creating a sustainable and regenerative system in contrast to the traditional linear model of take-make-dispose (Kuniawan et al., 2022). In the context of RSSC, circularity helps in reducing waste, reusing waste (such as packaging material waste) and making it an alternate source of supply. By minimizing waste and maximizing the value of materials, organizations can reduce their environmental footprint, harness the benefit of reusing the material and improve its performance (Farooque et al., 2019; Nunes, 2025). It helps organizations to be more resilient by reducing their dependence on finite resources and creating potential to absorb the risks of resource shortages or price fluctuations (Nygaard, 2023).

SC network design determines the optimal structure and configuration of a supply chain by considering factors such as the location of suppliers and customers, transportation costs, production capacities, and inventory levels (Sazvar et al., 2021; Ostovari et al., 2025). Building an efficient, reliable and cost-effective supply chain is the aim of a supply chain network design (Lemmens et al., 2016; Hussain et al., 2025). Ensuring the responsive and efficient ability of the supply chains is the ultimate aim of an RSSC. For being responsive to changing demands, disruptions and other related risks in a timely manner, organisations need to integrate flexibility and adaptability in their supply chain network design (Modgil et al., 2021). An effective network design also reduces environmental footprints by optimising transportation distances, production processes and reducing waste (Sazvar et al., 2021).

Imparting digital technologies and data analytics into the operations of a supply chain is its digitalization (Hallikas et al., 2021; Karuppiah et al., 2025). Generation of the data using IoT devices or sensors, data acquisition and data handling result in improved supply chain visibility, efficiency and resilience (Kurpjuweit et al., 2021). Incorporating digital technologies like Blockchain, Artificial Intelligence (AI) and Augmented reality/Virtual Reality (AR/VR) increases efficiency and responsiveness of the supply chain (Attaran, 2020; Kostadimas, 2025). Organisation may enhance their absorption and reactive capabilities by developing their prediction potential using AI and visualising using AR/VR (Zamani et al., 2022). In addition, the digital technologies may enhance the tracking and tracing potential of a supply chain. Blockchain technology has the potential to create environmental transparency, trust and authenticity (Kumar et al., 2023). Smart contracts help in increasing the controlling power of the SC by reducing its variability (Zhu and Kouhizadeh, 2019; Kostadimas, 2025).

Although there is a different school of thought for sustainability and supply chain digitalisation, which suggests that increasing digitalisation adversely affects supply chain sustainability as it is directly proportional to the energy consumption (Patidar et al., 2023b; Caiado et al., 2022). For resilience, digitalisation appears to be a boon. It increases the prediction potential of the supply chain (Zamani et al., 2022). Parallelly, it also helps in predicting possible scenarios and their consequences through digital twins (Burgos and Ivanov, 2021).

Resilient sustainable supply chain has two crucial components, i.e. resilience and sustainability, although the dependence of these on each other is debated in the literature. Davis et al. (2021), Pires Ribeiro and Barbosa-Povoa (2018) and Rashid et al. (2025) argue the dependence of sustainability on resilience. At the same time, Jain et al. (2017), Ivanov (2018) and Mollashahi et al. (2025) argued the dependence of resilience on sustainability. Chowdhury et al. (2012) defined resilient sustainable supply chain as the resource management to meet stakeholders' expectations and to achieve high resilience and subsequent sustainability. Based on the following hypothesis, a model is developed to get a clearer view:

Considering the theoretical underpinning, literature survey and expert opinion and domain knowledge of the authors, a conceptual model of the RSSC is proposed for further validation. Figure 1 represents the proposed conceptual model.

The model consists of seven factors (antecedents) and one measurable variable and a mediating variable. The developed model considers resilient sustainable supply chain as dependent variable as per the definition provided by Chowdhury et al. (2012).

The proposed model is tested with the data from the Indian manufacturing industries by using the survey methodology. A pilot test with 109 respondents was conducted to ensure clarity and reliability. The final version was distributed through professional and industrial networks, including LinkedIn and industry associations. We employed convenience sampling. In total, 278 responses were received, of which 257 were retained after data cleaning. The respondent profile distribution is listed in Table 3.

The survey was distributed to executives working in manufacturing industries in India. The sector faces challenges related to resource depletion, environmental degradation, climate change and global market pressures, making sustainability a necessity for its long-term viability and competitiveness.

EFA is a measurement method that enables researchers to identify underlying factors influencing a set of observed variables. EFA as its name suggests, is an exploratory method, meaning that it is used to explore the data to identify the patterns, without having a specific theory about the underlying factors. Mostly, EFA is used to reduce a large set of items into a smaller set of factors that explain the variation in the data.

Confirmatory factor analysis (CFA) is a method widely used in psychometrics and social sciences to establish and validate the relationships among observed variables and latent factors. It serves to confirm theoretical statements among the variables. CFA produces a measurement model that outlines relationships. Validity is gauged using fitness indices.

SEM, or path analysis, is a comprehensive measuring technique used in behavioural research, social sciences and sciences that tests a complex model of relationships between variables. This includes testing the relations among observed variables, potential factors and other variables that might affect the relations. It allows for testing direct and indirect relationships among variables, as well as estimating models' fitness.

Mediation analysis is a measurement technique that tests the indirect relationship between the predictor variable and outcome variable through a third variable, called a mediating variable. It is often used to test the effectiveness of an intervention or treatment, to understand how it works, and to identify potential points of intervention. The mediations are classified as full mediation, partial mediation or no mediation.

The survey method was applied to understand and explore the resilient sustainable supply chain for Indian manufacturing Industries. As depicted in the conceptual framework, supply chain resilience was considered as mediating variable as per definition given by Chowdhury et al. (2012). The measurement model assumes that indicators are reflective and unidimensional, with acceptable internal consistency (Cronbach's alpha >0.7). The mediation analysis follows Baron and Kenny's approach, assuming causal ordering between antecedents, mediator (resilience) and outcome (sustainability), with no significant omitted variables influencing these paths.

The suitability of the data is checked before EFA can be conducted by using CITC (corrected items total correlation), Bartlett's test and Kaiser–Meyer–Olkin (KMO) test. KMO value of 0.922, p value of 0.000 for Barletts' test suggest suitability of the data to perform EFA as suggested by Hair et al. (1995). CAID (Cronbach alpha if Item Deleted) values for all factors were above the minimum suggested value of 0.7, so the data is also reliable. Principal Component Analysis (PCA) with Varimax rotation was used to extract the factors.

Nine factors were extracted, having loadings from 0.577 to 0.825 as shown in Table 4. Absence of cross-loading was observed in performing exploratory factor analysis at a suppression level of 0.5. To test the common method bias, Herman's single-factor test was administered. The test explains the variance explained by the data when considered as a single factor. The dataset being one factor must not explain variance more than 50% (Das, 2017). Here, the independent variables, the dependent variables and both the variables together were tested and found explaining 31.97%, 44.75% and 32.64% variance, respectively. All assumptions of EFA were met and hence CFA can be performed to confirm the relational structure.

Authors in this study performed CFA with seven variables as factors (antecedents) of RSSC and the two variables of SC resilience and SC sustainability performance as simple variables for the Indian manufacturing industries. The CFA findings represent RMSEA of 0.051 (suggested value < 0.05; and acceptable between 0.05 and 0.10), chi-square test divided by degree of freedom (1.659) <3.0 and NFI (0.813) > 0.8. CFI and GFI were 0.915 and 0.782, respectively, which are under the acceptable as the values closer to 1 are considered better model fit. Thus, it can be concluded that the data sample has allowable findings in terms of Goodness of Fit statistics. The CFA figure (measurement model) is shown in Figure 2.

To ensure the quality of the survey instrument, its reliability and validity need to be tested (Paul and Maiti, 2008). Reliability value is predicted by the Cronbach's alpha values. Nunnally (1978) argued that the desired or risk-free level of Cronbach's alpha is ≥ 0.7, but 0.6 is also acceptable in exploratory studies. All the constructs in this study have Cronbach's alpha values greater than 0.7, making the questionnaire and the study highly reliable. The validity of constructs was checked by determining convergent, discriminant, nomological and criterion validity. To obtain convergent validity, three conditions need to be met (Das, 2017): item loadings >0.3, CR > 0.6 and AVE >0.5. Referring Table 4, all the conditions required to obtain convergent validity were met. To obtain discriminant validity, the conditions to be met are (Das, 2017): the values of the correlations between the constructs must be less than 0.9, if the values of AVE are more than the values of maximum shared variance (MSV) for each construct then discriminant validity is established, and the heterotrait-monotrait ratio of correlations (HTMT) values < 0.9 (Henseler et al., 2015; Hu and Bentler, 1999). The dataset used in the study met all the above conditions, thereby achieving discriminant validity. For nomological validity, Das (2017) argued that the correlations between all the constructs must be positive, as was found in this study. The study observes a significant criterion related to validity as eight of fourteen correlations were found significant. Multicollinearity was tested and the values of the variance inflation factor (VIF) were found to be less than 5; therefore multi-collinearity was not an issue (Chen, 2019). This refers to the acceptability of confirmatory factor analysis findings.

To examine the relationships between dependent and independent variables, structure equation modelling was used. Figure 3 shows the path diagram. The chi-square test value (1.681) < 3.0 and GFI was (0.779) < 0.95. In addition to the above, RMSEA (0.052) was greater than 0.05, which means the goodness of fit statistics unveil acceptable outcomes for the gathered data. Table 5 lists out the SEM model-path analysis results.

Table 5 lists all fifteen hypotheses represented in the conceptual model and their associations. Conducting path analysis revealed that ten hypotheses were fully supported, one was partially supported, and four hypotheses were not supported. The study revealed six hypotheses support SCRes and four hypotheses (three fully and one partially) support SSCP. SCND (−0.127) directly affects SSCP but not positively, hence it was considered as partially supporting. The hypothesis shows that SCRes supports SSCP, which supports the findings in line with the Chowdhury et al. (2012).

Mediation analysis revealed that out of seven indirect effects, three hypotheses support a full mediating condition, three show a partial mediating condition, and one with no mediating condition. Table 6 lists the mediation amongst variables. Overall, it can be concluded that to SCV, SCCol and SCND affect RSSC and SSCP both, whereas SCF, SCCo and SCD affect SCRes and RSSC both. Possible reason for this may be SCF somewhat supports redundancy, which is against SSCP and SCD is also based on the energy generation and consumption, therefore it may affect SSCP adversely but in terms of net results it supports RSSC.

Flexibility has a significant impact on sustainability (Edwin Cheng et al., 2022) and resilience (Piprani et al., 2022). However, this study reveals that the flexibility affects resilience but not sustainability. The possible reason may be Edwin Cheng et al. (2022) have considered sustainable supply chain flexibility considering green products, environmental practices and technology, and resource consumption. For example, redundancy enhances resilience but reduces sustainability. Similarly, demand fluctuations can be met by creating a buffer stock, which reduces sustainability by creating excess waste. However, supplier flexibility helps an organization (OEM) in achieving sustainability. The observed negative relationship between supply chain flexibility and sustainability may reflect inefficiencies caused by over-flexibility, such as frequent changes in production plans leading to waste or energy inefficiency, consistent with findings by Tao et al. (2025).

Next significant construct in the model is SC collaboration. Extant studies have discussed SCCo as a significant construct that affects resilience and sustainability (Belhadi et al., 2021; Nayal et al., 2022). This study also supports the literature. SCCo creates shared information, coordinated responses, resource pooling and risk sharing to improve resilience, and imparts shared goals, resource optimisation, knowledge sharing and stakeholder engagements to improve sustainability.

Digitalisation is the next significant construct in the model. Extant studies explored the impact of digitalisation on resilience and sustainability. This study asserts that SCD has a significant impact on resilience, which is in line with the findings of Shi et al. (2023), but does not have impact on sustainability, which is against the findings of Oubrahim et al. (2023). The negative or non-significant impact of digitalization on sustainability, though surprising, may be attributed to the technological maturity of the firms studied. As highlighted by Shahadat et al. (2023), firms in developing economies often face a digital divide where technology adoption does not translate immediately into environmental outcomes. The possible reason for the contradiction is the energy consumption.

SCCo can cater unexpected disruptions by judicious resource allocation and robust risk mitigation strategies. Control strengthens the SC ability to endure challenges and improve. Some researchers argue that SCCo does not affect sustainability performance directly, but does it by means of collaborative efforts and localised understanding. However, Gupta et al. (2022) and Hamprecht et al. (2005) suggested that control affects resilience and sustainable performance, respectively. Control indirectly fuels sustainability by providing financial stability, accurate risk assessments and long-term planning for sustainable initiatives.

SCV appeared to be the fifth most significant construct of this model. It also supports the findings of the studies by Dubey et al. (2017, 2019). SCV provides risk management and efficient responses to disruptions through information sharing. The information sharing also fosters collaboration and trust among supply chain partners. Supply chain visibility helps in tracing the origin of material, sourcing ethics and environment impact assessment.

SCND affects resilience fully and sustainability partially via SCRes, which is supported by the findings of Aman and Seuring (2023). The findings assert that SCND does affect SSCP but in a negative manner. Possible reasons may be larger the network, larger will be the operations, higher number of partners and possibly lower will be the sustainability. Joshi (2022) argued that SCND affects SSCP. Furthermore, impact of SCND on SSCP via SCRes is evident in risk-aware sustainable practices and financial stability.

Supply chain circularity (SCC) appears to be a non-significant construct. The findings of the study contradict the findings of Zhang et al. (2021) and support the findings of Le (2023). Possible reasons may be that authors in this study have considered circularity in terms of Reduce, Reuse and Recycle. The relatively low impact of SCC can be explained by infrastructural and regulatory bottlenecks in India. For instance, in the textile sector, closed-loop recycling practices remain fragmented due to a lack of formalized waste recovery systems (Charnley et al., 2024). In the Indian context, it is less popular to consider the reuse of material as an alternate source of supply. Circularity has a direct impact on sustainability. Overall, SCC does not affect SSCP via SCRes. Resiliency primarily deals with supply chain's ability to withstand and recover from disruptions. It does not address factors like diversified sourcing, contingency planning or response mechanism. SCC is crucial for the supply chain, but its impact on SCRes and SSCP may differ in scope and focus.

In the Indian manufacturing context, where supply chains often operate with limited digital infrastructure and fragmented logistics, the role of collaboration and visibility becomes even more critical. For example, industries such as automotive and pharmaceuticals rely heavily on multi-tier supplier networks, where disruptions at the lower tier can severely impact performance.

Based on the results and assessment, this article proposes to define an RSSC as a process of managing resources to meet stakeholder expectations by creating supply chain visibility, flexibility collaboration and control through the use of supply chain network design and digitalization by means of people, process and technology.

The work extends the body of knowledge on RSSC by providing its factors, sub-factors and co-relation among them. The validated model of the factors shows how resilience and sustainability can be imbibed in an SC simultaneously, directly or through mediating effects. The identified and validated seven factors and their corresponding 38 sub-factors or observed variables provide future researchers a ready reckoner or a launch pad to start their research.

The results of this study offer several strategic insights for supply chain managers aiming to enhance both resilience and sustainability. First, firms should prioritize the development of resilience-building capabilities, including supplier collaboration, robust internal control systems and real-time visibility tools, which collectively enable organizations to better absorb and respond to disruptions. Integrating digital transformation initiatives with day-to-day operational practices is also critical; while such technologies may not yield immediate sustainability benefits, they lay the foundation for long-term value creation by improving responsiveness and efficiency. A practical illustration of this can be seen during the COVID-19 pandemic, when several Indian automotive manufacturers that had invested in digital visibility solutions and flexible supplier contracts were able to adapt rapidly to supply disruptions, demonstrating the real-world effectiveness of these practices.

The findings of this study hold relevance not only for industry practitioners but also for policymakers seeking to foster resilient and sustainable supply chains. Government intervention is particularly crucial in scaling up digital infrastructure and eliminating the systemic barriers that hinder the widespread adoption of circular supply chain practices. To this end, policymakers should consider implementing standardized sustainability metrics and promoting mechanisms for cross-industry data-sharing. In addition, targeted incentives – such as financial subsidies or tax benefits – could encourage firms, particularly in sectors like textiles, packaging and automotive, to adopt collaborative and circular approaches. Regulatory measures mandating product recovery, combined with fiscal incentives for remanufacturing, would further support these goals.

This study has developed and tested a conceptual model of resilient sustainable supply chains (RSSC) for the Indian manufacturing sector. The model was built from literature and expert opinions, and then validated using EFA, CFA and SEM. It includes key factors such as supply chain visibility, flexibility, collaboration, control, network design and digitalization. The results show that resilience is a central link in the model, strengthening the positive effects of many of these factors on sustainability. Digitalization and collaboration emerged as strong drivers of resilience, while flexibility showed a more mixed role in sustainability. Circularity had only a small effect, which is probably due to infrastructure and policy challenges in the region. On the theory side, this work adds to supply chain literature by showing how resilience can be integrated and empirically tested in a developing economy context. On the practical side, it gives managers clear ideas on how to use visibility, control and collaboration to make their supply chains resilient sustainable. It also points policymakers towards building better digital infrastructure and supportive regulations to promote circular practices. The study is limited to the Indian manufacturing sector, so results might be different in other settings. Future studies could apply the model in other industries or countries, follow changes over several years and explore more about digital tools and circular economy practices in different situations. Overall, the framework presented here offers useful insights for both academicians and practitioners, encouraging firms to focus on resilience capabilities and urging policymakers to create conditions that support a shift towards supply chains that are resilient sustainable.