Dr. Sunil Luthra
@aicte-india.org
Director
All India Council of Technical Education, New Delhi
Dr. Sunil Luthra is working as the Director, All India Council for Technical Education, New Delhi, India. Prior to that, he worked as the Director-Principal at Ch. Ranbir Singh State Institute of Engineering & Technology, Jhajjar, Haryana, India. He is also Visiting Professor at the ‘Centre for Supply Chain Improvement, University of Derby, Derby, United Kingdom. He has contributed over 240 research papers in inter-national referred and national journals and conferences at international and national level. He has an excellent research track record (over 1000 cumulative research impact factor points; received more than 12300 citations on Google Scholar; H-index–57 on Google Scholar). He has received many Awards and Honours for the research and teaching. He published many research articles in highly reputed journals like Transportation Research Part E Logistics and Transportation Review, Renewable and Sustainable Energy Reviews, Journal of Cleaner Production, Journal of Business Research
RESEARCH INTERESTS
Sustainability, Production and Operations Management, Supply Chain Management, Industrial Engineering, Industry 4.0/Industry 5.0, Green/Sustainable/Circular Supply Chains, Circular Economy, Cleaner Technologies, Sustainable Societies
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
K.E.K. Vimal, Pooja Goel, Nitika Sharma, K. Mathiyazhagan, and Sunil Luthra
Elsevier BV
Sanjeev Yadav, Ashutosh Samadhiya, Anil Kumar, Sunil Luthra, and Krishan Kumar Pandey
Elsevier BV
Manu Sharma, Deepak Kaushal, Sudhanshu Joshi, Anil Kumar, and Sunil Luthra
Elsevier BV
Sachin Kumar Mangla, Sunil Luthra, Jose Arturo Garza-Reyes, Charbel Jose Chiappetta Jabbour, and Alexander Brem
Elsevier BV
Yiğit Kazançoğlu, Nazlican Gozacan, Sunil Luthra, and Anil Kumar
Springer Science and Business Media LLC
Ashutosh Samadhiya, Anil Kumar, Jose Arturo Garza-Reyes, Sunil Luthra, and Francisco del Olmo García
Elsevier BV
Gyan Prakash, Sahiba Sharma, Anil Kumar, and Sunil Luthra
Elsevier BV
Yuvika Gupta, Farheen Mujeeb Khan, Anil Kumar, Sunil Luthra, and Maciel M. Queiroz
Emerald
PurposeWith the emergence of big data analytics and the importance of analytics-driven decisions, the travel industry is swiftly jumping on and adopting the bandwagon. However, research in this domain is limited. Accordingly, the present research seeks to understand how big data analytics capabilities (BDAC) add value to tourism supply chains (TSCs) and can dynamic capabilities (DC) improve the triple bottom line.Design/methodology/approachData from 218 valid responses were collected from different Indian tourism industry units. Confirmatory factor analysis (CFA) was applied to confirm the constructs, followed by partial least squares structural equation modelling (PLS-SEM) to check the mediating effect of DC on TSCs performance.FindingsThe findings show that BDAC significantly influence the performance of TSCs and that DC plays a critical role in strengthening the impact of BDAC on TSCs' economic performance. These results corroborate that DC plays a key moderating role.Research limitations/implicationsThis study contributes significantly to the tourism sector in India, where tourism is a key contributor to the country's gross domestic product. Theoretically, this study contributes to the resource-based view (RBV) and practically encourages professionals in the tourism sector to promote the use of BDAC to enhance the performance of TSCs.Originality/valueThe originality of the study is that it has tried to comprehend the moderating role of dynamic capabilities which impact BDAC to improve TSC performance.
Melisa Ozbiltekin-Pala, Yigit Kazancoglu, Anil Kumar, Jose Arturo Garza-Reyes, and Sunil Luthra
Emerald
PurposeThe manufacturing sector is highly competitive and operationally complex. Therefore, the strategic alignment between operational excellence methodologies and Industry 4.0 technologies is one of the issues that need to be addressed. The main aim of the study is to determine the critical factors of strategic alignment between operational excellence methodologies and Industry 4.0 technologies for manufacturing industries and make comparative analyses between automotive, food and textile industries in terms of strategic alignment between operational excellence methodologies and Industry 4.0 technologies.Design/methodology/approachFirst, determining the critical factors based on literature review and expert opinions, these criteria are weighted, and analytical hierarchy process is run to calculate the weights of these criteria. Afterward, the best sector is determined by the grey relational analysis method according to the criteria for the three manufacturing industries selected for the study.FindingsAs a result of AHP, “Infrastructure for Right Methodology, Techniques and Tools, is in the first place,” Organizational Strategy, is in the second place, while the third highest critical factor is “Capital Investment”. Moreover, based on grey relational analysis (GRA) results, the automotive industry is determined as the best alternative in terms of strategic alignment between operational excellence (OPEX) methodologies and I4.0 technologies.Originality/valueThis study is unique in that it is primarily possible to obtain the order of importance within the criteria and to make comparisons between three important manufacturing industries that are important for the economies of the world.
Sudhanshu Joshi, Manu Sharma, Sunil Luthra, Jose Arturo Garza-Reyes, and Ramesh Anbanandam
Emerald
PurposeThe research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.Design/methodology/approachThe authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.FindingsThe study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).Research limitations/implicationsThis research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.Originality/valueThis may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.
Sudhanshu Joshi, Manu Sharma, Sunil Luthra, Rajeev Agarwal, and Rajeev Rathi
Springer Science and Business Media LLC
Suneet Singh, Akhilesh Barve, Kamalakanta Muduli, Anil Kumar, and Sunil Luthra
Institute of Electrical and Electronics Engineers (IEEE)
Nowadays, freight logistics industries are seeking to adopt green freight practices in their transport systems to reduce environmental concerns; efficient green freight practices lead to reductions in greenhouse gas emissions while using less energy and material. The freight logistics industry, despite its significant contribution to a country's monetary development and economic welfare, is not well regarded because of its role in contaminating the climate. However, the freight logistics industry is trying to implement a green freight transportation system to improve their image with buyers. However, they are facing a lot of obstacles. Therefore, this article seeks to identify the barriers to the implementation of green freight in first world and third world nations and formulate them into a single numeric index. The interval-valued intuitionistic fuzzy set based graph theory and matrix approach technique has been used in this research to derive the green freight barrier impact index value. The PERMAN algorithm is used to compute the permanent function of matrices. Findings suggest that the freight logistics industries in third world nations should pay more attention to societal and managerial barriers for successful implementation of green freight. This research will assist policymakers and managers of freight logistics industries to create strategies to overcome the hurdles in properly implementing green freight practices.
Pratibha Wasan, Ashwani Kumar, and Sunil Luthra
Institute of Electrical and Electronics Engineers (IEEE)
The need to increase green developers’ access to finance is critical and urgent for emerging economies that are currently facing an annual green financing gap of $2.5 trillion. This article investigates barriers and strategies for promoting green finance (hereafter GF) in India which is one of the fastest emerging economies globally. The article uses a two-phase methodology. In the first phase, an exhaustive literature survey followed by a three-round modified Delphi method is used to extract significant barriers and solution strategies for GF adoption. In the second phase, the best worst method is used to rank the barriers using their relative weights, and the solution strategies by utilizing global weights as input. The article finds that policy, economic, and knowledge barriers are the top three barriers for GF adoption. Clear green policies and risk assessment frameworks; credit enhancement mechanisms for green developers; low-cost refinancing and securitization markets for green technology and products; and combining directed finance with incentivized finance, public finance with private finance, and financial markets with technology are some of the most important strategies obtained from this article for promoting GF adoption.
Rohit Agrawal, Abhijit Majumdar, Anil Kumar, and Sunil Luthra
Springer Science and Business Media LLC
Sumanta Das, Abhiram Yadav Myla, Akhilesh Barve, Anil Kumar, Naresh Chandra Sahu, Kamalakanta Muduli, and Sunil Luthra
Wiley
Ashutosh Samadhiya, Anil Kumar, Sanjeev Yadav, Sunil Luthra, Charbel Jose Chiappetta Jabbour, and Rajat Agrawal
Elsevier BV
Ashutosh Samadhiya, Sanjeev Yadav, Anil Kumar, Abhijit Majumdar, Sunil Luthra, Jose Arturo Garza-Reyes, and Arvind Upadhyay
Elsevier BV
Radha Yadav, Dharmendra Kumar, Anil Kumar, and Sunil Luthra
Wiley
Sanjeev Yadav, Ashutosh Samadhiya, Anil Kumar, Abhijit Majumdar, Jose Arturo Garza-Reyes, and Sunil Luthra
Elsevier BV
Sumanta Das, Akhilesh Barve, Naresh Chandra Sahu, Kamalakanta Muduli, Anil Kumar, and Sunil Luthra
Wiley
Claudio Sassanelli, Jose Arturo Garza-Reyes, Yang Liu, Diego Augusto de Jesus Pacheco, and Sunil Luthra
Elsevier BV
Jagriti Singh, Krishan Kumar Pandey, Anil Kumar, Farheen Naz, and Sunil Luthra
Springer Science and Business Media LLC
Anil Kumar, Farheen Naz, Sunil Luthra, Rajat Vashistha, Vikas Kumar, Jose Arturo Garza-Reyes, and Deepak Chhabra
Elsevier BV
Surajit Bag, Pavitra Dhamija, Sunil Luthra, and Donald Huisingh
Emerald
PurposeIn this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.Design/methodology/approachThe hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.FindingsIt is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.Practical implicationsThe findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.Originality/valueTo the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.
The performance of the Alumina/water and copper oxide/water nanofluids in a heat exchanger is experimentally investigated for particle weight concentrations ranging from 0.02 wt% to 0.5 wt%. The alumina/water and copper oxide/water nanofluids were prepared using two-step methods in an aqueous solution with 0.01 wt% CTAB (Cetyl Trimethyl Ammonium Bromide) as a surfactant at different concentrations and were characterized using HRTEM (High-Resolution Transmission Electron Microscopy) technique. Laminar forced convective heat transfer analysis using alumina and copper oxide nanoparticles suspended in water in a circular horizontal tube under constant heat flux boundary conditions was performed. The effect of various flow conditions and weight concentrations in the local heat transfer coefficient and pressure drop of both the nanofluids were investigated. Reynolds number varied from 1275 to 2200. Results show that 12.7 & 14.5 % thermal performance enhancement was observed with 0.5 wt% of Al2O3 and CuO nanofluids. Maximum, 50.62 % enhancement was observed in the average heat transfer coefficient by Al2O3 nanofluids, whereas 52.74 % was observed using CuO nanofluids using 0.5 wt% concentrations of the nanofluids and at Reynolds number of 2200. Correlations were proposed for thermal conductivity, viscosity, and Nusselt number for both the nanofluids with the maximum and minimum deviations of ±9 % and ±10 %, respectively.