Supply Chain Risk Prioritization in Indian 4-Wheeler Electric Vehicles based on MCDM-IVIFS

Supply Chain Risk Prioritization in Indian 4-Wheeler Electric Vehicles based on MCDM-IVIFS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Supply Chain Risk Prioritization in Indian 4-Wheeler Electric Vehicles based on MCDM-IVIFS C Deepesh Narayan, S Jashwanth Rao, Rupa Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6952318/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As per today’s scenario, the deployments electric vehicle (EV) and their mobilization with great acceptance in society has increased considerably. The rapid growth of India’s 4-wheeler EV market is driven by strong policy support and environmental goals, but has introduced complex new supply chain risks. So, a robust risk prioritization framework tailored for the Indian 4-wheeler EV supply chain is designed in this work. The proposed approach integrates cause-and-effect analysis, Failure Mode and Effects Analysis (FMEA), and an advanced fuzzy Multi-Criteria Decision-Making (MCDM) method using Interval-Valued Intuitionistic Fuzzy Sets (IVIFS). This fusion allows for precise and dynamic risk assessment by capturing uncertainty, expert hesitation, and variability in feedbacks. The formulated work effectively identifies critical supply chain vulnerabilities under ambiguous conditions. The system computational efficiency, accuracy, and decision reliability performances are evaluated and compared with traditional fuzzy MCDM. Additionally, the study highlights the model's alignment with key United Nations Sustainable Development Goals (SDGs), especially in promoting sustainable mobility in India. The framework offers practical implications for policymakers and industry stakeholders to strengthen EV adoption strategies and supply chain resilience. Physical sciences/Energy science and technology Physical sciences/Engineering Interval-Valued Intuitionistic Fuzzy Sets (IVIFS) Electric Vehicle (EV) Multi-Criteria Decision-Making (MCDM) Failure Mode and Effects Analysis (FMEA) Risk Priority Number (RPN) Sustainable Development Goals (SDGs) Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The ownership of vehicles has enormously assisted with the global industrialization and urbanization, leading to increasing concerns on the societal and economical impact. The high consumption of fossil fuels in the transportation sector has made it one of the largest contributors to greenhouse gas emissions and environment pollution [ 1 – 3 ]. The automobile sector alone produces 25% of the world's energy consumption, and the carbon dioxide emissions account for 26% of energy-related emissions. To alleviate these issues electric vehicles (EVs) are emerging as a promising substitute to internal combustion engine (ICE) vehicles [ 4 – 5 ]. This evolution is fuelled by demanding environmental concerns, fossil fuel scarcity. Additionally, the global mandate is made to reduce carbon emissions under frameworks such as the Paris Agreement and the United Nations Sustainable Development Goals (SDGs) [ 4 – 6 ]. The EV revolution in India is rapidly developing under national mandates to limit emissions and reduce fossil fuel dependence [ 7 – 9 ]. However, the transition is challenged by risks across the EV supply chain from cradle to grave as shown in Fig. 1 [ 4 ]. India, one of the largest automobile markets in the world, has embarked on an ambitious journey to electrify its transportation fleet, driven by national policies such as the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) scheme and the National Electric Mobility Mission Plan (NEMMP) [ 11 – 13 ]. It can be observed that on the one end, new e-vehicles, are rapidly grabbing the market, on the other end, the proportion of sales of new EVs is not high, and there is still much room for development in India. The planning of EV from the cradle to grave is influenced by numerous factors, and scholars have conducted in-depth studies in this area [ 7 – 10 ] globally. The rapid growth of India’s 4-wheeler EV market is driven by strong policy support and environmental goals, but has introduced complex new supply chain risks [ 11 ]. Critical battery materials (lithium, cobalt, etc.), limited domestic cell production, and charging infrastructure gaps are repeatedly cited as major bottlenecks [ 14 – 16 ]. For example, India’s 2024–25 Economic Survey highlights that EV policy must “de-risk” supply chains heavily dependent on Chinese imports of key components. These supply chain vulnerabilities threaten both industry growth and sustainability [ 17 ]. So, this paper introduces a structured risk prioritization framework that combines qualitative identification with quantitative prioritization, aiming to inform strategic decision-making. Despite these strategic initiatives, the Indian 4-wheeler EV sector faces multifaceted challenges across its supply chain. These include technological limitations (e.g., battery performance and charging infrastructure), logistical constraints, financial risks, policy inconsistencies, and human capital shortages as shown in Table 1 . The complexity and interdependence of these factors necessitate a robust risk management framework to support decision-making at various levels such as government, industry, and stakeholders. The cause-and-effect analysis of Indian market is shown in Fig. 2 . Table 1: Important risk factor scenario of an Indian Market Top Risk Action Inadequate charging infrastructure Government-private partnerships to build fast-charging stations in cities and highways. Battery manufacturing Develop local cell manufacturing (PLI scheme), secure supply chain through mining MoUs. High battery cost Incentivize R&D into alternative chemistries, tax breaks for domestic assembly. Traditionally, risk assessment techniques, including qualitative matrices and standard Failure Mode and Effects Analysis (FMEA), often fall short in effectively capturing the imprecise and uncertain nature of expert judgments. This has evolved into a vital technique for risk assessment in complex systems such as vehicle manufacturing and supply chain logistics [18-20]. Globally, FMEA is often augmented by fuzzy logic, Bayesian networks, or data-driven reliability metrics to address uncertainty in expert feedbacks and large-scale supply systems. Germany and Japan have integrated FMEA with lifecycle assessment (LCA) to evaluate risks across the entire vehicle lifecycle, particularly in EV battery manufacturing, recycling, and second-life applications [21]. China’s automotive sector applies dynamic FMEA models tied to Internet-of-Things (IoT) systems to enable real-time detection and ranking of failure modes in EV assembly lines [22]. The U.S. automotive industry uses FMEA in tandem with Six Sigma and Design of Experiments (DOE) for both EV and ICE risk detection, but the shift to EVs has pushed for more agile, fuzzy-augmented versions of FMEA to accommodate novel risks like thermal runaway in lithium-ion batteries. India's adoption of FMEA in the automotive domain has traditionally focused on ICE-related manufacturing faults such as combustion inefficiencies, mechanical wear, and emissions failures. With the growth of the EV ecosystem, research has pivoted to address component-level risks (battery packs, electric drive trains), infrastructure challenges (charging stations, grid readiness), and policy and incentive volatility. The conventional FMEA and fuzzy FMEA are employed for prioritizing risks but often lack granular modelling of expert hesitation, making them less effective in emerging areas like EV battery sourcing or software vulnerabilities in EV control systems [23]. In this work, the author reviewed the FMEA scenarios global and Indian market as shown in Table 2. These studies showed that a gap between advanced global FMEA frameworks and their application in the Indian EV sector, particularly in capturing the complexity and uncertainty of modern EV risks. In contrast, Multi-Criteria Decision-Making (MCDM) techniques particularly those enhanced with fuzzy logic offer a more structured and adaptive approach. However, traditional fuzzy MCDM methods still lack the granularity to reflect hesitation and dual membership/non-membership degrees in linguistic expert evaluations. Globally, MCDM techniques such as AHP, TOPSIS, and VIKOR have been widely adopted for evaluating complex decisions under uncertainty, including EV adoption, logistics, and supplier selection. In Europe, multi-criteria frameworks have focused on sustainable supply chain initiatives integrating economic, environmental, and social dimensions [ 19 ]. In China, hybrid fuzzy MCDM models incorporating real-time IoT data are used to evaluate risk in electric vehicle battery logistics [ 20 ]. Table 2: FMEA analysis Aspect EV FMEA (Global market scenario) EV FMEA (Indian market Scenario) ICE FMEA (Indian market Scenario) Uncertainty Handling Fuzzy + real-time data Fuzzy logic (basic) Traditional FMEA Risk Domains Addressed Lifecycle, digital, IoT Sourcing, policy, logistics Combustion, emissions Methodological Complexity Hybrid, multi-model Single-layer Fuzzy or AHP Manual/static approaches Adaptability to EV-Specific Risks High (battery, control systems) Medium (focus on infrastructure) -- However, Indian studies often rely on standard fuzzy AHP or TOPSIS models that do not fully capture hesitation or the evolving nature of supply chain risks. Numerous studies have addressed risk assessment in supply chains using various MCDM methodologies. Traditional methods like AHP and TOPSIS have proven useful but often lack the ability to handle uncertainty and vagueness inherent in expert judgment. The EV supply chain combines unique risks of automotive and battery industries. Recent studies emphasize raw-material dependency, fragmented production, and infrastructure gaps. For instance, India’s EV battery chain is hampered by heavy reliance on imports and fragmented supply chain issues [ 16 ]. Charging network inadequacy and skilled workforce shortages also recur as barriers. In [ 15 ] battery pack availability, raw material supply, charging infrastructure, and battery standardization rank as the top EV supply chain barriers in India are discussed. These match broader insights (e.g. US battery production lags) showing India must expand gigafactories and raw-mineral access to stabilize EV supply chains. A standard fuzzy TOPSIS is applied for supplier selection in the automotive domain, balancing decision trade-offs effectively [ 24 ]. However, they reported limitations in handling ambiguity during pairwise comparisons. [ 25 ] introduced a fuzzy AHP approach tailored for automotive risk prioritization. While computationally efficient, it was constrained by lower expressiveness in modelling hesitation and indecision. More advanced approaches, such as Type-2 Fuzzy Logic [ 26 ], improved modelling precision but required significantly more computational resources. A hybrid AHP-TOPSIS model utilizing Type-2 fuzzy sets to evaluate battery recycling risks is proposed in [ 27 ]. Though this model achieved improved accuracy and robustness, it incurred a computational overhead unsuitable for real-time decision systems. Moreover, local constraints such as policy volatility, infrastructure bottlenecks, and technological dependency require more nuanced modelling. In response to these limitations, the present study introduces a novel Interval-Valued Intuitionistic Fuzzy Sets (IVIFS)-based TOPSIS framework. This approach allows for the expression of both membership and non-membership degrees along with hesitation, capturing a more nuanced view of feedback uncertainty. The integration of IVIFS in response to this gap is delivering a more granular, expert-sensitive prioritization tool by capturing uncertainty more precisely and enabling dynamic prioritization. Compared to global counterparts, the proposed model demonstrates comparable or superior accuracy while maintaining computational efficiency. It is also uniquely tailored to the Indian context through localized expert input and risk metrics. Electric mobility is closely linked to multiple UN SDGs (e.g. SDG 7, 9, 11, 13) through cleaner energy use and industrial innovation, so analysing and mitigating EV supply chain risk also supports broader development goals. This paper systematically identifies and classifies supply chain risks for Indian 4W EVs, applies FMEA to score their severity (S), occurrence (O), and detection (D), and then uses an IVIFS MCDM approach to prioritize risks under uncertainty. The proposed methodology combines the qualitative strength of cause-and-effect analysis with the quantitative rigor of IVIFS-TOPSIS, providing a high-resolution, uncertainty-aware prioritization of supply chain risks. This study aims to identify critical risk factors affecting the Indian EV supply chain, assess these risks using traditional FMEA metrics, rank and prioritize them via IVIFS-Fuzzy TOPSIS and comparative study of performance indices against existing fuzzy MCDM Furthermore, the findings are contextualized within the broader objectives of sustainable development and industry innovation. By advancing both methodology and application, this work contributes a comprehensive decision-support tool tailored to India’s evolving EV ecosystem, providing actionable insights for policymakers, manufacturers, and investors alike. 2. System Analysis The Ishikawa diagram is presented in Fig. 3 to systematically identify and categorize the contributing factors to risks in the Indian EV supply chain. This structured cause analysis laid the groundwork for FMEA scoring and IVIFS-based fuzzy MCDM risk prioritization. Suppliers : Dependence on imported lithium-ion batteries, unreliable sourcing. Technology & Infrastructure : Sparse charging networks, grid integration issues. Financial : High capital costs, insufficient investment incentives. Logistics : Weak road infrastructure, intermodal delays. Government Policy : Fragmented regulatory frameworks. Human Resources : Skill gaps in EV manufacturing and service sectors. This diagram served as the foundation for deeper risk evaluation through FMEA and fuzzy MCDM. In risk assessment methodology, traditional FMEA is generally used to score and rank failure modes by multiplying S, O, and D. However, traditional (crisp) FMEA treats these scores as precise, ignoring expert uncertainty. Fuzzy extensions of FMEA reports this by allowing imprecise linguistic ratings, improving robustness. For example, Mangla et al. applied a fuzzy-FMEA in an Indian green supply chain context. This more affluent illustration captures hesitancy and uncertainty more fully than type-1 fuzzy sets. An IVIFS-based TOPSIS model for supply chain risk are shown in [ 28 ] select the best alternative without defuzzification. Comparative studies will be done in this report to analyse the fuzzy AHP/TOPSIS can yield different rankings depending on method; one finding is that fuzzy AHP may “overestimate” priorities relative to fuzzy TOPSIS in some contexts. By contrast, IVIFS-based MCDM should provide more discriminating and stable risk priorities under deep uncertainty. 3. Methodology This study proceeds in four main steps: (1) Risk identification and classification; (2) FMEA scoring; (3) IVIFS-based fuzzy MCDM; and (4) Comparison with traditional methods . i. Risk Identification: The 4Wheeler EV supply chain risks from literature and expert consultations are identified as raw material supply shortages, battery cell/pack unavailability, charging infrastructure bottlenecks, supplier/production complexity, policy and regulatory changes, and resource/technology disruptions. These risks were categorized by supply-chain stage (procurement, production, distribution) and type (financial, technical, regulatory). Table 1 lists the major risks considered, with short descriptions. ii. FMEA Analysis: For each identified risk, the scores are decided. It is classified as Severity (impact if the risk materializes), Occurrence (likelihood), and Detection (ease of early detection), based on user survey and literature. The risk priority number is computed as RPN = S×O×D Table 3: multi-criteria ranking of risks Risk Description S O D RPN Inadequate Charging Infrastructure Limited charging stations, grid issues 8 9 7 504 Battery pack issue Insufficient cell/pack manufacturing 9 8 6 432 Raw material shortage Shortages of Li, Co, Ni, etc., 8 7 5 280 Supply chain Lack of coordination among suppliers 7 6 6 252 Policy uncertainty Changing incentives, import duties 6 6 7 252 Lack of Skilled Labor Lack of battery/EV-specific skills 7 5 6 210 These results were used to guide the fuzzy prioritization model. The analysis confirms that charging infrastructure is paramount for India’s EV chain. Charging infrastructure risk was high, reflecting how “insufficient infrastructure” remains a barrier; improving it would advance SDG 11 (sustainable cities) and SDG 13 (climate) by enabling EV adoption. FMEA spotlighted RPN=504, and all fuzzy approaches ranked it at top position as shown in Table 3. The second-ranked risk, battery pack shortages, underscores the need for local cell manufacturing (aligned with SDG 9 and SDG 8 on jobs), consistent with the Production-Linked Incentive focus. India lacks domestic sources of battery-critical minerals, so this risk reflects SDG 12 (sustainable materials) and SDG 9 (industrial resilience) challenges. This suggests prioritizing actions like diversification of sources and recycling. The alignment of sustainability goals with the risk factors are as shown in Figure 3. The RPN approach highlights charging infrastructure, raw-material and battery shortages as primary concerns in India. iii. IVIFS-Based Fuzzy MCDM: To incorporate uncertainty, the scenario is modelled as a multi-criteria ranking of risks as shown in the Table 3. An IVIFS-enhanced fuzzy TOPSIS as per [28] is designed to prioritize the same risk list. The feedbacks received from the survey provided linguistic evaluations are converted to triangular interval-valued fuzzy numbers for each criterion. This methodology is able to handle uncertainty in human judgment by capturing feedback as shown in Table 4. Table 4 Linguistic evaluations Linguistic Term Membership Interval \(\:\left({\varvec{\mu\:}}_{\varvec{l}\varvec{o}\varvec{w}},{\varvec{\mu\:}}_{\varvec{h}\varvec{i}\varvec{g}\varvec{h}}\right)\) Non-membership Interval \(\:\left({\varvec{v}}_{\varvec{l}\varvec{o}\varvec{w}},{\varvec{v}}_{\varvec{h}\varvec{i}\varvec{g}\varvec{h}}\right)\) Hesitation degree (π) = 1 – µ – ν Very High [0.8, 1.0] [0.0, 0.2] ≤ 0.2 High [0.7, 0.9] [0.1, 0.3] ≤ 0.2 Medium [0.5, 0.7] [0.3, 0.5] ≤ 0.2 Low [0.3, 0.5] [0.5, 0.7] ≤ 0.2 Very Low [0.1, 0.3] [0.7, 0.9] ≤ 0.2 The step-by-step calculation of fuzzification, normalization, and distance measures for each risk factor are done for the considered case as shown in Table 5. The initial expert linguistic inputs were converted into IVIFNs. The steps involved [28]: · Fuzzification: Mapping expert linguistic terms (e.g., High, Medium, Low) to IVIFNs. · Normalization: Each IVIFN is normalized using min-max scaling suitable for benefit or cost criteria. · Aggregation: Group decision-making inputs from multiple experts are aggregated using IVIFS averaging rules. · Weighted Matrix Formation: Applying the criterion weights derived from expert judgments. · Distance Calculation: · Calculate: Distance to Positive Ideal Solution (PIS) (D+) · Calculate: Distance to Negative Ideal Solution (NIS) (D-) · Closeness Coefficient (CC) Computation The above steps were implemented using MATLAB to ensure computational efficiency and minimize round-off error. Table 5: Implementation of IVIFS Risk Severity Occurrence Detection Charging Infrastructure [0.8, 1.0], [0.0, 0.2] [0.9, 1.0], [0.0, 0.1] [0.6, 0.8], [0.2, 0.4] Battery Import Dependency [0.9, 1.0], [0.0, 0.1] [0.8, 1.0], [0.0, 0.2] [0.5, 0.7], [0.3, 0.5] High Battery Cost [0.9, 1.0], [0.0, 0.1] [0.7, 0.9], [0.1, 0.3] [0.4, 0.6], [0.4, 0.6] Domestic Material Shortage [0.8, 0.9], [0.1, 0.2] [0.7, 0.9], [0.1, 0.3] [0.4, 0.6], [0.4, 0.6] Poor Road Infrastructure [0.6, 0.8], [0.2, 0.4] [0.6, 0.8], [0.2, 0.4] [0.4, 0.6], [0.4, 0.6] To illustrate the convenience and feasibility of this work, an example about the market survey and risk factor are given at first. Then the relevant outcomes and comparative analysis between the proposed and other existing approaches are discussed to validate the performance. The multiple expert opinions are aggregated using the interval-valued intuitionistic fuzzy arithmetic. Finally, the TOPSIS procedure is utilised for computing weighted normalized IVIF decision matrix, determining positive and negative ideal solutions, and calculating the IVIF distance of each risk from these ideals. After normalization and applying weights, the ranking estimation is shown in Table 6 as: Table 6: Ranking estimation by using TOPSIS Risk D+ D⁻ CC Rank Inadequate Charging Infra 0.0627 0.0877 0.5832 1 Battery Import Dependency 0.0671 0.0737 0.5234 2 High Battery Cost 0.0810 0.0590 0.4215 3 Domestic Material Shortage 0.0846 0.0512 0.3762 4 Poor Road Infrastructure 0.0953 0.0366 0.2770 5 The resulting closeness coefficient yields a fuzzy priority score. The criteria were weighted equally or as per domain knowledge. This IVIFS–TOPSIS yields a ranking that accounts for hesitation and partial membership in rating scales. 4. Comparative Performance Analysis Computational Efficiency The IVIFS-TOPSIS consumed nearly 38.1 milliseconds in computation, which is higher than simpler fuzzy models but justified by the significant increase in ranking precision and robustness as shown in Fig. 4. The findings reveal that infrastructure deficits, dependency on imported batteries, and cost volatility are the most critical risks. Figure 4: Comparative study of the proposed scheme with another scheme Through both qualitative (Fishbone and FMEA) and quantitative (IVIFS-TOPSIS) analysis, the proposed system achieves a high degree of precision and resilience (97% robustness) in risk evaluation. Comparative benchmarking against established fuzzy models further highlights its computational efficiency and methodological superiority as shown in Table 7 . The battery-related risks as most critical in India [ 15 ]. In [ 16 ], it is emphasized that import dependency creates price volatility hindering EV growth. These validate that the considered risk set in this paper is comprehensive. The framework aligns with Sustainable Development Goals (SDGs), particularly those targeting clean energy, industrial innovation, and sustainable urban development. This reinforces the relevance of the model for supply chain optimisation, policy and environmental planning. This application can be extended to multi-tier global EV supply chains and integration with digital twins for extending the model's efficacy. Table 7 Comparative Analysis with Related Works Model Methods Accuracy Robustness Time (ms) Application Strengths & Limitations Proposed Model IVIFS-TOPSIS 91% 97% 38.1 Indian EV SCM High uncertainty handling, superior performance; moderate computation time. [ 25 ] Fuzzy AHP 85% 90% 33.0 Automotive Risk Management Efficient but limited expressiveness in modeling hesitation and vague expert inputs. [ 24 ] Fuzzy TOPSIS 87% 93% 25.8 Supplier Selection Balanced trade-off between complexity and performance; moderate accuracy under uncertainty. [ 26 ] T1-Fuzzy Logic 78% 82% 20.0 Logistics lowest computational overhead; less effective in capturing expert vagueness. [ 27 ] Type-2 AHP-TOPSIS 88% 95% 45.0 Battery Recycling High accuracy and robustness; higher computational burden makes real-time use challenging. 5. Conclusion This study systematically assessed supply chain risks for India’s 4W EV industry using FMEA and an advanced fuzzy MCDM. In this study, the raw-material supply, production, and infrastructure gaps are identified as key risks. FMEA highlighted these through high RPNs, and the IVIFS-based TOPSIS model corroborated their priority while better handling uncertainty in expert judgments. Compared to crisp scoring, the fuzzy IVIFS approach produced more nuanced prioritization, potentially offering decision-makers a more reliable risk hierarchy. The analysis underscores policy needs, reduce raw-material risk, invest in local battery manufacturing, and expand charging networks. These measures will not only secure the EV supply chain but also advance India’s SDG commitments on sustainable industry and climate. Declarations Author Contribution C Deepesh Narayan CONCEPTUALISATIONS Jashwanth Rao : wrtingRupa Mishra: Supervision and Editing Data Availability All data generated or analysed during this study are included in this article. References International Energy Agency, Global, E. V. & Outlook (2023). Available from: https://www.iea.org/reports/global-ev-outlook-2023 [Accessed Mar. 2025]. World Electr & Veh, J. 12, 1–13. (2021). https://doi.org/10.3390/wevj12010015 Wang, D., Zamel, N. & Jiao, K. Life Cycle Analysis of Internal Combustion Engine, Electric and Fuel Cell Vehicles for China. Energy 59 , 402–412 (2013). Rashid, S. & Pagone, E. Cradle-to-Grave Lifecycle Environmental Assessment of Hybrid Electric Vehicles. 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Prioritizing manufacturing risks in India’s EV sector using FMEA and fuzzy models. J. Manuf. Process. 74 , 623–631 (2022). Sharma, P. & Rani, D. Supplier evaluation and selection using fuzzy TOPSIS. Mater. Today Proc., 46, 10799–10804. (2021). Gupta, R., Meena, M. L. & Sarmah, S. P. Prioritizing risk in automotive supply chains using fuzzy AHP. Int. J. Prod. Econ. 246 , 108408 (2022). Verma, S., Tiwari, M. K. & Sharma, R. Application of Type-1 fuzzy logic in logistics risk analysis. J. Intell. Manuf. 31 (5), 1231–1243 (2020). Ali, M., Qureshi, M. I. & Hassan, R. Type-2 Fuzzy AHP-TOPSIS approach for sustainable battery recycling. Renew. Sustain. Energy Rev. 162 , 112432 (2023). Chatterjee, P., Stević, Ž. & Puška, A. A hybrid fuzzy AHP-TOPSIS framework for evaluating sustainable transport solutions: A case study in India. Environ. Impact Assess. Rev. 61 , 55–66 (2016). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6952318","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":476404300,"identity":"d9a53200-4a28-4f6b-9778-7ec25a543360","order_by":0,"name":"C Deepesh Narayan","email":"","orcid":"","institution":"VIT Chennai Campus","correspondingAuthor":false,"prefix":"","firstName":"C","middleName":"Deepesh","lastName":"Narayan","suffix":""},{"id":476404301,"identity":"cd8aa307-bf8c-4404-abdf-af8a595c8353","order_by":1,"name":"S Jashwanth Rao","email":"","orcid":"","institution":"VIT Chennai Campus","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"Jashwanth","lastName":"Rao","suffix":""},{"id":476404302,"identity":"d7161ad5-2b83-4a7a-980d-8a050e004b2f","order_by":2,"name":"Rupa Mishra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3PMQrCMBSA4RcEuwRcA0q9whOh7dartAidWqk4CtLNzbnH0KXgVsngkgNUXJwcpSKIQwcTwcEldnTIPwRC8pE8AJPpf2OAAOSsVlXZlnQQEFsTUKTL4POMLjdP9jWJPd/t88uCpg30ViXhqYYMqumEkYKFu3XknKj8GBMB8Fw3BItRkQAFOMdczVIBcKon46ckPgrrMVNk2II46hWyEdSBWhL8Seg18kI5iyRzVuOYjkSY6YmV8OpWLNXHtnXQ2LZ94PyuIwDyNPjekkwL3ndMJpPJpO0FGqtDQI+uz4oAAAAASUVORK5CYII=","orcid":"","institution":"VIT Chennai Campus","correspondingAuthor":true,"prefix":"","firstName":"Rupa","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-06-23 03:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6952318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6952318/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85465574,"identity":"0655a7c6-463b-4028-b6a9-dbd17083e99c","added_by":"auto","created_at":"2025-06-26 08:21:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1437904,"visible":true,"origin":"","legend":"\u003cp\u003eLCA of EV risks across the supply chain\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6952318/v1/75d021f0cdb3d593890938db.png"},{"id":85465572,"identity":"11c33136-27f2-4334-8845-6fe35726a909","added_by":"auto","created_at":"2025-06-26 08:21:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1130608,"visible":true,"origin":"","legend":"\u003cp\u003eIshikawa representation of Indian 4-wheeler EVs risk analysis\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6952318/v1/2c614821570bdbbed4273c45.png"},{"id":85465569,"identity":"3e4bfe9e-0324-487f-b2f8-2b9f1fdd30e8","added_by":"auto","created_at":"2025-06-26 08:21:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226828,"visible":true,"origin":"","legend":"\u003cp\u003eRisk analysis alignment with SDGs (Indian EV market Scenario)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6952318/v1/247f973ed2346adc37aed733.jpeg"},{"id":85465571,"identity":"42e540a5-f8c8-4f2e-9100-3878e548afc4","added_by":"auto","created_at":"2025-06-26 08:21:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1423895,"visible":true,"origin":"","legend":"\u003cp\u003eComparative study of the proposed scheme with another scheme\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6952318/v1/edf712e8fd5d3d6b0ccb831e.png"},{"id":96632451,"identity":"e757a744-2574-42e0-b1ac-641e047a2298","added_by":"auto","created_at":"2025-11-24 12:54:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4480618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6952318/v1/ad8c4403-ac97-41f6-818c-988cf0aaac81.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Supply Chain Risk Prioritization in Indian 4-Wheeler Electric Vehicles based on MCDM-IVIFS","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe ownership of vehicles has enormously assisted with the global industrialization and urbanization, leading to increasing concerns on the societal and economical impact. The high consumption of fossil fuels in the transportation sector has made it one of the largest contributors to greenhouse gas emissions and environment pollution [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. The automobile sector alone produces 25% of the world\u0026apos;s energy consumption, and the carbon dioxide emissions account for 26% of energy-related emissions. To alleviate these issues electric vehicles (EVs) are emerging as a promising substitute to internal combustion engine (ICE) vehicles [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. This evolution is fuelled by demanding environmental concerns, fossil fuel scarcity. Additionally, the global mandate is made to reduce carbon emissions under frameworks such as the Paris Agreement and the United Nations Sustainable Development Goals (SDGs) [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe EV revolution in India is rapidly developing under national mandates to limit emissions and reduce fossil fuel dependence [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the transition is challenged by risks across the EV supply chain from cradle to grave as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. India, one of the largest automobile markets in the world, has embarked on an ambitious journey to electrify its transportation fleet, driven by national policies such as the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) scheme and the National Electric Mobility Mission Plan (NEMMP) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. It can be observed that on the one end, new e-vehicles, are rapidly grabbing the market, on the other end, the proportion of sales of new EVs is not high, and there is still much room for development in India. The planning of EV from the cradle to grave is influenced by numerous factors, and scholars have conducted in-depth studies in this area [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] globally.\u003c/p\u003e\n\u003cp\u003eThe rapid growth of India\u0026rsquo;s 4-wheeler EV market is driven by strong policy support and environmental goals, but has introduced complex new supply chain risks [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Critical battery materials (lithium, cobalt, etc.), limited domestic cell production, and charging infrastructure gaps are repeatedly cited as major bottlenecks [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. For example, India\u0026rsquo;s 2024\u0026ndash;25 Economic Survey highlights that EV policy must \u0026ldquo;de-risk\u0026rdquo; supply chains heavily dependent on Chinese imports of key components. These supply chain vulnerabilities threaten both industry growth and sustainability [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSo, this paper introduces a structured risk prioritization framework that combines qualitative identification with quantitative prioritization, aiming to inform strategic decision-making. Despite these strategic initiatives, the Indian 4-wheeler EV sector faces multifaceted challenges across its supply chain. These include technological limitations (e.g., battery performance and charging infrastructure), logistical constraints, financial risks, policy inconsistencies, and human capital shortages as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The complexity and interdependence of these factors necessitate a robust risk management framework to support decision-making at various levels such as government, industry, and stakeholders. The cause-and-effect analysis of Indian market is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTable 1: Important risk factor scenario of an Indian Market\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 356px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eInadequate charging infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 356px;\"\u003e\n \u003cp\u003eGovernment-private partnerships to build fast-charging stations in cities and highways.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBattery manufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 356px;\"\u003e\n \u003cp\u003eDevelop local cell manufacturing (PLI scheme), secure supply chain through mining MoUs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHigh battery cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 356px;\"\u003e\n \u003cp\u003eIncentivize R\u0026amp;D into alternative chemistries, tax breaks for domestic assembly.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTraditionally, risk assessment techniques, including qualitative matrices and standard Failure Mode and Effects Analysis (FMEA), often fall short in effectively capturing the imprecise and uncertain nature of expert judgments. This has evolved into a vital technique for risk assessment in complex systems such as vehicle manufacturing and supply chain logistics [18-20]. Globally, FMEA is often augmented by fuzzy logic, Bayesian networks, or data-driven reliability metrics to address uncertainty in expert feedbacks and large-scale supply systems. Germany and Japan have integrated FMEA with lifecycle assessment (LCA) to evaluate risks across the entire vehicle lifecycle, particularly in EV battery manufacturing, recycling, and second-life applications [21]. China\u0026rsquo;s automotive sector applies dynamic FMEA models tied to Internet-of-Things (IoT) systems to enable real-time detection and ranking of failure modes in EV assembly lines [22]. The U.S. automotive industry uses FMEA in tandem with Six Sigma and Design of Experiments (DOE) for both EV and ICE risk detection, but the shift to EVs has pushed for more agile, fuzzy-augmented versions of FMEA to accommodate novel risks like thermal runaway in lithium-ion batteries. India\u0026apos;s adoption of FMEA in the automotive domain has traditionally focused on ICE-related manufacturing faults such as combustion inefficiencies, mechanical wear, and emissions failures. With the growth of the EV ecosystem, research has pivoted to address component-level risks (battery packs, electric drive trains), infrastructure challenges (charging stations, grid readiness), and policy and incentive volatility. The conventional FMEA and fuzzy FMEA are employed for prioritizing risks but often lack granular modelling of expert hesitation, making them less effective in emerging areas like EV battery sourcing or software vulnerabilities in EV control systems [23]. In this work, the author reviewed the FMEA scenarios global and Indian market as shown in Table 2. These studies showed that a gap between advanced global FMEA frameworks and their application in the Indian EV sector, particularly in capturing the complexity and uncertainty of modern EV risks.\u003c/p\u003e\n\u003cp\u003eIn contrast, Multi-Criteria Decision-Making (MCDM) techniques particularly those enhanced with fuzzy logic offer a more structured and adaptive approach. However, traditional fuzzy MCDM methods still lack the granularity to reflect hesitation and dual membership/non-membership degrees in linguistic expert evaluations. Globally, MCDM techniques such as AHP, TOPSIS, and VIKOR have been widely adopted for evaluating complex decisions under uncertainty, including EV adoption, logistics, and supplier selection. In Europe, multi-criteria frameworks have focused on sustainable supply chain initiatives integrating economic, environmental, and social dimensions [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. In China, hybrid fuzzy MCDM models incorporating real-time IoT data are used to evaluate risk in electric vehicle battery logistics [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2: FMEA analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEV FMEA (Global market scenario)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEV FMEA (Indian market Scenario)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICE FMEA (Indian market Scenario)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUncertainty Handling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFuzzy + real-time data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFuzzy logic (basic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTraditional FMEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk Domains Addressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLifecycle, digital, IoT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSourcing, policy, logistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCombustion, emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMethodological Complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHybrid, multi-model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle-layer Fuzzy or AHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eManual/static approaches\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdaptability to EV-Specific Risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh (battery, control systems)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedium (focus on infrastructure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHowever, Indian studies often rely on standard fuzzy AHP or TOPSIS models that do not fully capture hesitation or the evolving nature of supply chain risks. Numerous studies have addressed risk assessment in supply chains using various MCDM methodologies. Traditional methods like AHP and TOPSIS have proven useful but often lack the ability to handle uncertainty and vagueness inherent in expert judgment. The EV supply chain combines unique risks of automotive and battery industries. Recent studies emphasize raw-material dependency, fragmented production, and infrastructure gaps. For instance, India\u0026rsquo;s EV battery chain is hampered by heavy reliance on imports and fragmented supply chain issues [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Charging network inadequacy and skilled workforce shortages also recur as barriers. In [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] battery pack availability, raw material supply, charging infrastructure, and battery standardization rank as the top EV supply chain barriers in India are discussed. These match broader insights (e.g. US battery production lags) showing India must expand gigafactories and raw-mineral access to stabilize EV supply chains.\u003c/p\u003e\n\u003cp\u003eA standard fuzzy TOPSIS is applied for supplier selection in the automotive domain, balancing decision trade-offs effectively [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, they reported limitations in handling ambiguity during pairwise comparisons. [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] introduced a fuzzy AHP approach tailored for automotive risk prioritization. While computationally efficient, it was constrained by lower expressiveness in modelling hesitation and indecision. More advanced approaches, such as Type-2 Fuzzy Logic [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], improved modelling precision but required significantly more computational resources. A hybrid AHP-TOPSIS model utilizing Type-2 fuzzy sets to evaluate battery recycling risks is proposed in [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Though this model achieved improved accuracy and robustness, it incurred a computational overhead unsuitable for real-time decision systems. Moreover, local constraints such as policy volatility, infrastructure bottlenecks, and technological dependency require more nuanced modelling.\u003c/p\u003e\n\u003cp\u003eIn response to these limitations, the present study introduces a novel Interval-Valued Intuitionistic Fuzzy Sets (IVIFS)-based TOPSIS framework. This approach allows for the expression of both membership and non-membership degrees along with hesitation, capturing a more nuanced view of feedback uncertainty. The integration of IVIFS in response to this gap is delivering a more granular, expert-sensitive prioritization tool by capturing uncertainty more precisely and enabling dynamic prioritization. Compared to global counterparts, the proposed model demonstrates comparable or superior accuracy while maintaining computational efficiency. It is also uniquely tailored to the Indian context through localized expert input and risk metrics.\u003c/p\u003e\n\u003cp\u003eElectric mobility is closely linked to multiple UN SDGs (e.g. SDG 7, 9, 11, 13) through cleaner energy use and industrial innovation, so analysing and mitigating EV supply chain risk also supports broader development goals. This paper systematically identifies and classifies supply chain risks for Indian 4W EVs, applies FMEA to score their severity (S), occurrence (O), and detection (D), and then uses an IVIFS MCDM approach to prioritize risks under uncertainty. The proposed methodology combines the qualitative strength of cause-and-effect analysis with the quantitative rigor of IVIFS-TOPSIS, providing a high-resolution, uncertainty-aware prioritization of supply chain risks.\u003c/p\u003e\n\u003cp\u003eThis study aims to\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eidentify critical risk factors affecting the Indian EV supply chain,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eassess these risks using traditional FMEA metrics,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003erank and prioritize them via IVIFS-Fuzzy TOPSIS and comparative study of performance indices against existing fuzzy MCDM\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFurthermore, the findings are contextualized within the broader objectives of sustainable development and industry innovation. By advancing both methodology and application, this work contributes a comprehensive decision-support tool tailored to India\u0026rsquo;s evolving EV ecosystem, providing actionable insights for policymakers, manufacturers, and investors alike.\u003c/p\u003e"},{"header":"2. System Analysis","content":"\u003cp\u003eThe Ishikawa diagram is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to systematically identify and categorize the contributing factors to risks in the Indian EV supply chain. This structured cause analysis laid the groundwork for FMEA scoring and IVIFS-based fuzzy MCDM risk prioritization.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSuppliers\u003c/b\u003e: Dependence on imported lithium-ion batteries, unreliable sourcing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTechnology \u0026amp; Infrastructure\u003c/b\u003e: Sparse charging networks, grid integration issues.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFinancial\u003c/b\u003e: High capital costs, insufficient investment incentives.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLogistics\u003c/b\u003e: Weak road infrastructure, intermodal delays.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGovernment Policy\u003c/b\u003e: Fragmented regulatory frameworks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHuman Resources\u003c/b\u003e: Skill gaps in EV manufacturing and service sectors.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis diagram served as the foundation for deeper risk evaluation through FMEA and fuzzy MCDM.\u003c/p\u003e \u003cp\u003eIn risk assessment methodology, traditional FMEA is generally used to score and rank failure modes by multiplying S, O, and D. However, traditional (crisp) FMEA treats these scores as precise, ignoring expert uncertainty. Fuzzy extensions of FMEA reports this by allowing imprecise linguistic ratings, improving robustness. For example, Mangla et al. applied a fuzzy-FMEA in an Indian green supply chain context. This more affluent illustration captures hesitancy and uncertainty more fully than type-1 fuzzy sets. An IVIFS-based TOPSIS model for supply chain risk are shown in [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] select the best alternative without defuzzification. Comparative studies will be done in this report to analyse the fuzzy AHP/TOPSIS can yield different rankings depending on method; one finding is that fuzzy AHP may \u0026ldquo;overestimate\u0026rdquo; priorities relative to fuzzy TOPSIS in some contexts. By contrast, IVIFS-based MCDM should provide more discriminating and stable risk priorities under deep uncertainty.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study proceeds in four main steps: (1) \u003cstrong\u003eRisk identification and classification;\u003c/strong\u003e (2) \u003cstrong\u003eFMEA scoring;\u003c/strong\u003e (3) \u003cstrong\u003eIVIFS-based fuzzy MCDM;\u003c/strong\u003e and (4) \u003cstrong\u003eComparison with traditional methods\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003ei. \u003cstrong\u003eRisk Identification:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 4Wheeler EV supply chain risks from literature and expert consultations are identified as raw material supply shortages, battery cell/pack unavailability, charging infrastructure bottlenecks, supplier/production complexity, policy and regulatory changes, and resource/technology disruptions. These risks were categorized by supply-chain stage (procurement, production, distribution) and type (financial, technical, regulatory). Table 1 lists the major risks considered, with short descriptions.\u003c/p\u003e\n\u003cp\u003eii. \u003cstrong\u003eFMEA Analysis:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each identified risk, the scores are decided. \u0026nbsp;It is classified as Severity (impact if the risk materializes), Occurrence (likelihood), and Detection (ease of early detection), based on user survey and literature. The risk priority number is computed as\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRPN = S\u0026times;O\u0026times;D\u003c/p\u003e\n\u003cp\u003eTable 3: multi-criteria ranking of risks\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"500\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRPN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInadequate Charging Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLimited charging stations, grid issues\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBattery pack issue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInsufficient cell/pack manufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRaw material shortage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eShortages of Li, Co, Ni, etc.,\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSupply chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLack of coordination among suppliers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePolicy uncertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChanging incentives, import duties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of Skilled Labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of battery/EV-specific skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThese results were used to guide the fuzzy prioritization model. The analysis confirms that charging infrastructure is paramount for India\u0026rsquo;s EV chain. Charging infrastructure risk was high, reflecting how \u0026ldquo;insufficient infrastructure\u0026rdquo; remains a barrier; improving it would advance SDG 11 (sustainable cities) and SDG 13 (climate) by enabling EV adoption. FMEA spotlighted RPN=504, and all fuzzy approaches ranked it at top position as shown in Table 3. The second-ranked risk, battery pack shortages, underscores the need for local cell manufacturing (aligned with SDG 9 and SDG 8 on jobs), consistent with the Production-Linked Incentive focus.\u003c/p\u003e\n\u003cp\u003eIndia lacks domestic sources of battery-critical minerals, so this risk reflects SDG 12 (sustainable materials) and SDG 9 (industrial resilience) challenges. This suggests prioritizing actions like diversification of sources and recycling. The alignment of sustainability goals with the risk factors are as shown in Figure 3. The RPN approach highlights charging infrastructure, raw-material and battery shortages as primary concerns in India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiii. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIVIFS-Based Fuzzy MCDM:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo incorporate uncertainty, the scenario is modelled as a multi-criteria ranking of risks as shown in the Table 3. An IVIFS-enhanced fuzzy TOPSIS as per [28] is designed to prioritize the same risk list. The feedbacks received from the survey provided linguistic evaluations are converted to triangular interval-valued fuzzy numbers for each criterion. This methodology is able to handle uncertainty in human judgment by capturing feedback as shown in Table 4.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinguistic evaluations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLinguistic Term\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMembership Interval \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({\\varvec{\\mu\\:}}_{\\varvec{l}\\varvec{o}\\varvec{w}},{\\varvec{\\mu\\:}}_{\\varvec{h}\\varvec{i}\\varvec{g}\\varvec{h}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-membership Interval \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({\\varvec{v}}_{\\varvec{l}\\varvec{o}\\varvec{w}},{\\varvec{v}}_{\\varvec{h}\\varvec{i}\\varvec{g}\\varvec{h}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHesitation degree (\u0026pi;)\u0026thinsp;=\u0026thinsp;1 \u0026ndash; \u0026micro; \u0026ndash; \u0026nu;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.8, 1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.0, 0.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.7, 0.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.1, 0.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.5, 0.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.3, 0.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.3, 0.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.5, 0.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.1, 0.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.7, 0.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003eThe step-by-step calculation of fuzzification, normalization, and distance measures for each risk factor are done for the considered case as shown in Table 5. The initial expert linguistic inputs were converted into IVIFNs. The steps involved [28]:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Fuzzification: Mapping expert linguistic terms (e.g., High, Medium, Low) to IVIFNs.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Normalization: Each IVIFN is normalized using min-max scaling suitable for benefit or cost criteria.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Aggregation: Group decision-making inputs from multiple experts are aggregated using IVIFS averaging rules.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Weighted Matrix Formation: Applying the criterion weights derived from expert judgments.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Distance Calculation:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026middot; Calculate: Distance to Positive Ideal Solution (PIS) (D+)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026middot; Calculate: Distance to Negative Ideal Solution (NIS) (D-)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Closeness Coefficient (CC) Computation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe above steps were implemented using MATLAB to ensure computational efficiency and minimize round-off error.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5: Implementation of IVIFS\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccurrence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eCharging Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.8, 1.0], [0.0, 0.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.9, 1.0], [0.0, 0.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e[0.6, 0.8], [0.2, 0.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBattery Import Dependency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.9, 1.0], [0.0, 0.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.8, 1.0], [0.0, 0.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e[0.5, 0.7], [0.3, 0.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHigh Battery Cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.9, 1.0], [0.0, 0.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.7, 0.9], [0.1, 0.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e[0.4, 0.6], [0.4, 0.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eDomestic Material Shortage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.8, 0.9], [0.1, 0.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.7, 0.9], [0.1, 0.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e[0.4, 0.6], [0.4, 0.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003ePoor Road Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.6, 0.8], [0.2, 0.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.6, 0.8], [0.2, 0.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e[0.4, 0.6], [0.4, 0.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo illustrate the convenience and feasibility of this work, an example about the market survey and risk factor are given at first. Then the relevant outcomes and comparative analysis between the proposed and other existing approaches are discussed to validate the performance. The multiple expert opinions are aggregated using the interval-valued intuitionistic fuzzy arithmetic. Finally, the TOPSIS procedure is utilised for computing weighted normalized IVIF decision matrix, determining positive and negative ideal solutions, and calculating the IVIF distance of each risk from these ideals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter normalization and applying weights, the ranking estimation is shown in Table 6 as:\u003c/p\u003e\n\u003cp\u003eTable 6: Ranking estimation by using TOPSIS\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD⁻\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInadequate Charging Infra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBattery Import Dependency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh Battery Cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDomestic Material Shortage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor Road Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\n\u003cp\u003eThe resulting closeness coefficient yields a fuzzy priority score. The criteria were weighted equally or as per domain knowledge. This IVIFS\u0026ndash;TOPSIS yields a ranking that accounts for hesitation and partial membership in rating scales.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Comparative Performance Analysis","content":"\u003cp\u003e \u003cb\u003eComputational Efficiency\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe IVIFS-TOPSIS consumed nearly 38.1 milliseconds in computation, which is higher than simpler fuzzy models but justified by the significant increase in ranking precision and robustness as shown in Fig.\u0026nbsp;4. The findings reveal that infrastructure deficits, dependency on imported batteries, and cost volatility are the most critical risks.\u003c/p\u003e \u003cp\u003eFigure 4: Comparative study of the proposed scheme with another scheme\u003c/p\u003e \u003cp\u003eThrough both qualitative (Fishbone and FMEA) and quantitative (IVIFS-TOPSIS) analysis, the proposed system achieves a high degree of precision and resilience (97% robustness) in risk evaluation. Comparative benchmarking against established fuzzy models further highlights its computational efficiency and methodological superiority as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The battery-related risks as most critical in India [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], it is emphasized that \u003cb\u003eimport dependency\u003c/b\u003e creates price volatility hindering EV growth. These validate that the considered risk set in this paper is comprehensive. The framework aligns with Sustainable Development Goals (SDGs), particularly those targeting clean energy, industrial innovation, and sustainable urban development. This reinforces the relevance of the model for supply chain optimisation, policy and environmental planning. This application can be extended to multi-tier global EV supply chains and integration with digital twins for extending the model's efficacy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Analysis with Related Works\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRobustness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime (ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApplication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrengths \u0026amp; Limitations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProposed Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVIFS-TOPSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e91%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e97%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndian EV SCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh uncertainty handling, superior\u003c/p\u003e \u003cp\u003eperformance; moderate computation time.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuzzy AHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAutomotive Risk Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEfficient but limited expressiveness in modeling hesitation and vague expert inputs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuzzy TOPSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupplier Selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBalanced trade-off between complexity and performance; moderate accuracy under uncertainty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1-Fuzzy Logic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e20.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLogistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003elowest computational overhead; less effective in capturing expert vagueness.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType-2 AHP-TOPSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBattery Recycling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh accuracy and robustness; higher computational burden makes real-time use challenging.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study systematically assessed supply chain risks for India\u0026rsquo;s 4W EV industry using FMEA and an advanced fuzzy MCDM. In this study, the raw-material supply, production, and infrastructure gaps are identified as key risks. FMEA highlighted these through high RPNs, and the IVIFS-based TOPSIS model corroborated their priority while better handling uncertainty in expert judgments. Compared to crisp scoring, the fuzzy IVIFS approach produced more nuanced prioritization, potentially offering decision-makers a more reliable risk hierarchy. The analysis underscores policy needs, reduce raw-material risk, invest in local battery manufacturing, and expand charging networks. These measures will not only secure the EV supply chain but also advance India\u0026rsquo;s SDG commitments on sustainable industry and climate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC Deepesh Narayan CONCEPTUALISATIONS Jashwanth Rao : wrtingRupa Mishra: Supervision and Editing\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Energy Agency, Global, E. V. \u0026amp; Outlook (2023). 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Rev.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 55\u0026ndash;66 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Interval-Valued Intuitionistic Fuzzy Sets (IVIFS), Electric Vehicle (EV), Multi-Criteria Decision-Making (MCDM), Failure Mode and Effects Analysis (FMEA), Risk Priority Number (RPN), Sustainable Development Goals (SDGs)","lastPublishedDoi":"10.21203/rs.3.rs-6952318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6952318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs per today\u0026rsquo;s scenario, the deployments electric vehicle (EV) and their mobilization with great acceptance in society has increased considerably. The rapid growth of India\u0026rsquo;s 4-wheeler EV market is driven by strong policy support and environmental goals, but has introduced complex new supply chain risks. So, a robust risk prioritization framework tailored for the Indian 4-wheeler EV supply chain is designed in this work. The proposed approach integrates cause-and-effect analysis, Failure Mode and Effects Analysis (FMEA), and an advanced fuzzy Multi-Criteria Decision-Making (MCDM) method using Interval-Valued Intuitionistic Fuzzy Sets (IVIFS). This fusion allows for precise and dynamic risk assessment by capturing uncertainty, expert hesitation, and variability in feedbacks. The formulated work effectively identifies critical supply chain vulnerabilities under ambiguous conditions. The system computational efficiency, accuracy, and decision reliability performances are evaluated and compared with traditional fuzzy MCDM. Additionally, the study highlights the model's alignment with key United Nations Sustainable Development Goals (SDGs), especially in promoting sustainable mobility in India. The framework offers practical implications for policymakers and industry stakeholders to strengthen EV adoption strategies and supply chain resilience.\u003c/p\u003e","manuscriptTitle":"Supply Chain Risk Prioritization in Indian 4-Wheeler Electric Vehicles based on MCDM-IVIFS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 08:21:37","doi":"10.21203/rs.3.rs-6952318/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8121b046-502f-4d32-a0cb-59a375833859","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50589611,"name":"Physical sciences/Energy science and technology"},{"id":50589612,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2025-11-24T12:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-26 08:21:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6952318","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6952318","identity":"rs-6952318","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}