Department of Mechanical engineering
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Item Risk assessment of electric vehicle supply chain in India using fuzzy analytical hierarchical process(Elsevier, 2025) Digalwar, Abhijeet K.The shift to electric vehicles (EVs) in India is a key step toward sustainable mobility as the transportation sector greatly contributes to greenhouse gas emissions. However, this transition demands a revised supply chain associated with various EV components such as batteries, motors, etc. Despite the ongoing efforts in the electrification of the transportation sector, numerous complexities and uncertainties in the EV supply chain have a bearing impact on its efficiency. The authors have conducted the research work to find out and rank the risk factors associated with the EV supply chain specific to India. A detailed literature review has been done and feedback from the industry stakeholders and academicians has been taken to identify the risk factors. The fuzzy analytical hierarchy process which is a multicriteria decision-making technique has been used to study the multidimensional effects of various decisions. The fuzzy analytical hierarchy process, a combined approach of analytical hierarchy process and fuzzy logic theory is effective in conducting risk assessment of a complex system that involves multiple uncertainties similar to the Indian EV supply chain.Item Driving synergy: can TOC, lean, and six sigma integration work without flexibility?(Academy of Management, 2025-06) Digalwar, Abhijeet K.This study aims to empirically assess the combined impact of Theory of Constraints (TOC), Lean Manufacturing (LM), and Six Sigma (SS) – collectively known as TLS – on business performance in Indian manufacturing companies. Importantly, it investigates the role of manufacturing flexibility in influencing this relationship. Using a structured survey, data were collected from a diverse sample of Indian manufacturing companies. The study employs statistical techniques including Partial Least Square-Structural Equation Modelling (PLS-SEM) for model measurement, multiple regression analysis to test the influence of TLS on various performance metrics and hierarchical regression analysis to explore the moderating role of manufacturing flexibility. The results indicate that the implementation of TLS significantly improves business performance across several dimensions, including productivity, quality, and operational efficiency. Companies that have effectively integrated TLS into their operations report greater performance gains compared to those using individual or partial methodologies. Moreover, manufacturing flexibility is found to strengthen the relationship between TLS implementation and business performance, suggesting that companies with higher flexibility can better leverage TLS for enhanced operational outcomes. This study is among the first to empirically validate the impact of TLS integration on business performance in the context of Indian manufacturing. It also contributes to the existing literature by exploring the role of manufacturing flexibility, a factor that has received limited attention in previous research. The findings provide a robust framework for manufacturing companies seeking to implement TLS and highlight the strategic importance of flexibility in achieving operational excellence.Item Towards sustainable transportation: factors influencing electric vehicle charging stations development(Elsevier, 2025-05) Digalwar, Abhijeet K.; Routroy, SrikantaThe Indian transportation sector, reliant on fossil fuels, is predominantly accountable for the emergence of critical challenges such as greenhouse gas emissions, reliance on foreign energy sources, economic strain, and persistent health repercussions. In order to mitigate these urgent challenges, electric vehicles (EVs) are conceptualised as a viable, sustainable and ecologically sound technological solution, capable of successfully transitioning towards a sustainable low-carbon emission transportation framework and preserving finite natural resources. EVs encounter significant challenges in achieving rapid assimilation into the commercial landscape, and one of the most frequently referenced impediments to the accelerated adoption of EVs is the insufficiency of charging infrastructure along with the resultant range anxiety. Nevertheless, expanding the charging infrastructure network is financially burdensome and necessitates careful and strategic planning. Despite identifying essential factors, the inquiry “In what manner do these factors engage and interact?” has predominantly remained unaddressed in empirical investigations. Examining the interactions between these variables will empower producers and regulatory authorities to participate in systematic planning and devise suitable measures to govern these variables. The prime objective of this research is to execute an exhaustive assessment and furnish insights into the multifaceted factors/criteria influencing the establishment and development of EV charging infrastructure within a developing nation such as India. Factors are extracted from previous studies through literature reviews and expert interviews. The study also validates the identified factors empirically. Subsequently, a mixed-method approach is utilised to implement a combination of Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL). This methodology enables a methodical exploration of the hierarchical structures and interconnections among the variables, thereby enhancing the comprehension of their influence on the implementation and efficacy of charging infrastructure. The study identifies technological, economic, political, geographical, environmental, geopolitical, and socio-technical factors as key drivers influencing EV charging infrastructure development, highlighting the interdependencies between critical variables and providing a structured framework to enhance accessibility, scalability, and sustainability in alignment with global Sustainable Development Goals (SDGs) 7 and 13.Item Assessment of optimal fuel and drive mix for automobile sector decarbonization in India: a scenario analysis of 2035(Springer, 2025-08) Digalwar, Abhijeet K.The rapid growth of predominantly fossil fuels powered automobiles in India results in harmful greenhouse gas emissions (GHG), environmental challenges like air pollution and health hazards. Hence, India is adopting alternate low-emission fuels like compressed natural gas (CNG), biofuels, promoting zero-emission technologies like fully electric vehicles (FEVs), pursuing options like hybrid vehicles (HEVs), and hydrogen-powered vehicles (HPVs). These solutions must encompass reliability, cost-effectiveness, circularity, and mainly optimality. This study addresses above challenges, aligns with India’s upcoming nationally determined contribution (NCD 3.0) and decarbonization policy till 2035 and derives an optimal alternate fuels/drive mix, It adopts a time-series forecasting and machine learning (ML) for vehicle inventory projections, constructs a scientific model, includes six relevant cost and benefit factors, evaluates eleven scenarios using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, derives an optimal mix and verifies its robustness through sensitivity analysis. The optimal mix for 2035 indicates a reduction in the share of fossil fuels (50%) with healthy improvement in the adoption of FEVs (40%), BFVs (8.4%), CNGVs (0.6%), HEVs (0.5%), and HFVs (0.5%). This shift toward cleaner solutions will enable reduction of around Rs 3.6 trillion in fuel imports and 54% of GHG emissions compared to current levels, enabling mitigating environmental challenges. Unlike energy sector, India lags in studies of optimal fuel / drive mix for automobile sustainability. This study addresses above gap, providing critical insights to policymakers, industry, and academia for fine tuning automotive decarbonization policies, toward achieving net zero by 2070.