Dr. Vaibhav S. Narwane
@kjsce.somaiya.edu
Associate Professor and Department of Mechanical Engineering
K.J Somaiya College of Engineering
Dr. Vaibhav S. Narwane is working as an Associate Professor in the Mechanical Engineering Department at Somaiya Vidyavihar University, Mumbai. He received his Ph.D. in the Production Engineering Department, from University Mumbai. He has 19 years of teaching and 1 year of industrial experience. He has more than forty peer-reviewed publications in reputed journals such as Journal of Environmental Management, Industrial Management & Data Systems, Renewable Energy, Annals of Operations Research, Journal of Cleaner Production, Benchmarking, etc. His research work has more than 1450 Google Scholar citations with h-index and i10-index of 19 and 24 respectively. He is a fellow member and active member of the Indian Institution of Industrial Engineering (IIIE). He guided more than 20 master’s project. His current area of research includes AI/ML for Manufacturing, Big data analytics, Smart Manufacturing, and Supply Chain Analytics.
EDUCATION
Ph.D., Department of Production Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Mechanical Engineering, Management Science and Operations Research, Artificial Intelligence, Industrial and Manufacturing Engineering
Scopus Publications
Scopus Publications
Riddhi Thavi, Rujuta Jhaveri, Vaibhav Narwane, Bhaskar Gardas, and Nima Jafari Navimipour
Emerald
Purpose This paper aims to provide a literature review on the cloud-based platforms for the education sectors. The several aspects of cloud computing adoption in education, remote/distance learning and the application of cloud-based design and manufacturing (CBDM) have been studied and theorised. Design/methodology/approach A four-step methodology was adopted to analyse and categorise the papers obtained through various search engines. Out of 429 research articles, 72 papers were shortlisted for the detailed analysis. Findings Many factors that influence cloud computing technology adoption in the education sector have been identified in this paper. The research findings on several research items have been tabulated and discussed. Based on the theoretical research done on cloud computing for education, cloud computing for remote/distance learning and CBDM, cloud computing could enhance the educational systems in mainly developing countries and improve the scope for remote/distance learning. Research limitations/implications This study is limited to papers published only in the past decade from 2011 to 2020. Besides, this review was unable to include journal articles published in different languages. Nevertheless, for the effective teaching and learning process, this paper could help understand the importance and improve the process of adopting cloud computing concepts in educational universities and platforms. Originality/value This study is a novel one as a research review constituting cloud computing applications in education and extended for remote/distance learning and CBDM, which have not been studied in the existing knowledge base.
Manoj A. Palsodkar, Madhukar R. Nagare, and Vaibhav S. Narwane
World Scientific Pub Co Pte Ltd
Purpose: Global competition, individualized customer requirements, and volatile market conditions create an environment conducive to agile new product development (ANPD). This research seeks to identify the key factors that influence ANPD adoption along with the development of a conceptual framework for the identified factors. Design/methodology: Through a literature review, eight factors having 47 sub-factors pertinent to ANPD adoption and its performance improvement were identified. Considering all of these factors, the development of conceptual framework and research hypotheses was carried out. A structured questionnaire was used to collect 118 online responses from both domestic and foreign subject matter experts. The structural equation modeling (SEM) approach was used for validation of the conceptual framework along with the research hypotheses testing. Findings: This study supported six hypotheses: “Technology management competencies”, “Product development competencies”, “Organizational management competencies”, “Human resource competencies”, “Software management competencies”, and “Policy management competencies”. These supported hypotheses influence ANPD adoption significantly. However, the analysis did not support the two more positive factors, namely “Integrated system competencies” and “Supply chain competencies”, showcasing the necessity for a better understanding of them among the product development experts. Research limitations: As the proposed methodology relies on qualitative data, it is somewhat complex and time-consuming. While SEM can verify the linear relationship, a hybrid approach involving the SEM-MCDM technique can be employed to comprehend the impact of ANPD adoption and performance improvement. Practical implications: The findings of this study will assist product development experts, manufacturing executives, and managers in developing effective ANPD adoption policies. It will help in improving the new product development success rate and highlighting the causes of poor performance. Originality/value: This is a one-of-a-kind and highly beneficial structural modeling-based decision-making tool. This framework can be effective across multiple domains, and incidents of ANPD adoption failure can be mitigated.
Vaibhav S. Narwane, Rakesh D. Raut, Sachin Kumar Mangla, Bhaskar B. Gardas, Balkrishna E. Narkhede, Anjali Awasthi, and Pragati Priyadarshinee
Springer Science and Business Media LLC
Prashant Jain, Dhanraj P. Tambuskar, and Vaibhav S. Narwane
World Scientific Pub Co Pte Ltd
Purpose : In this age, characterized by the incessant generation of a huge amount of data in social and economic life due to the widespread use of digital devices, it has been well established that big data (BD) technologies can bring about a dramatic change in managerial decision-making. This work addresses the challenges of implementation of big data analytics (BDA) in sustainable supply chain management (SSCM). Design/methodology : The barriers to the implementation of BDA in SSCM are identified through an extensive literature survey as per PESTEL framework which covers political, economic, social, technological, environmental and legal barriers. These barriers are then finalized through experts’ opinion and analyzed using DEMATEL and AHP methods for their relative importance and cause-and-effect relationships. Findings : A total of 13 barriers are identified out of which the lack of policy support regarding IT, lack of data-driven decision-making culture, compliance with laws related to data security and privacy, inappropriate selection and adoption of BDA technologies, and cost of implementation of BDA are found to be the key barriers that have a causative effect on most of the other barriers. Research limitations : This work is focused on the Indian manufacturing supply chain (MSC). It may be diversified to other sectors and geographical areas. The addition of missed-out barriers, if any, might enrich the findings. Also, the fuzzy or grey versions of MCDM methods may be used for further fine-tuning of the results. Practical implications : The analysis presented in this work gives hierarchy of the barriers as per their strength and their cause-and-effect relationships. This information may be useful for decision makers to assess their organizational strengths and weaknesses in the context of the barriers and fix their priorities regarding investment in the BDA project. Social implications : The research establishes that the successful implementation of BDA through minimizing the effect of critical causative barriers would enhance the environmental performance of the supply chain (SC) which in turn would benefit society. Originality/value : This is one of the first studies of BDA in SSCM in the Indian manufacturing sector using PESTEL framework.
Vaibhav S. Narwane, Rakesh D. Raut, Sachin Kumar Mangla, Manoj Dora, and Balkrishna E. Narkhede
Springer Science and Business Media LLC
Harsh M. Shah, Bhaskar B. Gardas, Vaibhav S. Narwane, and Hitansh S. Mehta
Emerald
PurposeThis paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.Design/methodology/approachThe papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.FindingsThe previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.Practical implicationsAI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.Originality/valueThe paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.
Arman Firoz Velani, Vaibhav S. Narwane, and Bhaskar B. Gardas
Emerald
Purpose This paper aims to identify the role of internet of things (IoT) in water supply chain management and helps to understand its future path from the junction of computer science and resource management. Design/methodology/approach The current research was studied through bibliometric review and content analysis, and various contributors and linkages were found. Also, the possible directions and implications of the field were analyzed. Findings The paper’s key findings include the role of modern computer science in water resource management through sensor technology, big data analytics, IoT, machine learning and cloud computing. This, in turn, helps in understanding future implications of IoT resource management. Research limitations/implications A more extensive database can add up to more combinations of linkages and ideas about the future direction. The implications and understanding gained by the research can be used by governments and firms dealing with water management of smart cities. It can also help find ways for optimizing water resources using IoT and modern-day computer science. Originality/value This study is one of the very few investigations that highlighted IoT’s role in water supply management. Thus, this study helps to assess the scope and the trend of the case area.
Vaibhav S. Narwane, Angappa Gunasekaran, Bhaskar B. Gardas, and Pinyarat Sirisomboonsuk
Informa UK Limited
Manoj A. Palsodkar, Madhukar R. Nagare, Rajesh B. Pansare, and Vaibhav S. Narwane
Springer Science and Business Media LLC
Bhaskar B. Gardas, Angappa Gunasekaran, and Vaibhav S. Narwane
Informa UK Limited
Smita Abhijit Ganjare, Sunil M. Satao, and Vaibhav Narwane
Emerald
PurposeIn today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.Design/methodology/approachThis research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.FindingsThe papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.Practical implicationsThe research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.Originality/valueThis study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.Highlights A comprehensive understanding of Machine Learning techniques is presented.The state of art of adoption of Machine Learning techniques are investigated.The methodology of (SLR) is proposed.An innovative study of Machine Learning techniques in manufacturing supply chain.
Rajesh B. Pansare, Madhukar R. Nagare, and Vaibhav S. Narwane
Emerald
Purpose A reconfigurable manufacturing system (RMS) can provide manufacturing flexibility, meet changing market demands and deliver high performance, among other benefits. However, adoption and performance improvement are critical activities in it. The current study aims to identify the important factors influencing RMS adoption and validate a conceptual model as well as develop a structural model for the identified factors. Design/methodology/approach An extensive review of RMS articles was conducted to identify the eight factors and 47 sub-factors that are relevant to RMS adoption and performance improvement. For these factors, a conceptual framework was developed as well as research hypotheses were framed. A questionnaire was developed, and 117 responses from national and international domain experts were collected. To validate the developed framework and test the research hypothesis, structural equation modeling was used, with software tools SPSS and AMOS. Findings The findings support six hypotheses: “advanced technologies,” “quality and safety practice,” “strategy and policy practice,” “organizational practices,” “process management practices,” and “soft computing practices.” All of the supported hypotheses have a positive impact on RMS adoption. However, the two more positive hypotheses, namely, “sustainability practices” and “human resource policies,” were not supported in the analysis, highlighting the need for greater awareness of them in the manufacturing community. Research limitations/implications The current study is limited to the 47 identified factors; however, these factors can be further explored and more sub-factors identified, which are not taken into account in this study. Practical implications Managers and practitioners can use the current work’s findings to develop effective RMS implementation strategies. The results can also be used to improve the manufacturing system’s performance and identify the source of poor performance. Originality/value This paper identifies critical RMS adoption factors and demonstrates an effective structural-based modeling method. This can be used in a variety of fields to assist policymakers and practitioners in selecting and implementing the best manufacturing system. Graphical abstract
Dhanraj P. Tambuskar, Prashant Jain, and Vaibhav S. Narwane
Emerald
PurposeWith big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).Design/methodology/approachThe factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).FindingsA total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.Research limitations/implicationsThis research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.Practical implicationsThis research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.Social implicationsThe result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.Originality/valueThis research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.
Mahesh Kavre, Vaibhav S. Narwane, Bhaskar B. Gardas, and Vivek Sunnapwar
Informa UK Limited
ABSTRACT This study aims to identify and analyze human factors (HFs) affecting cloud manufacturing (CM) adoption across manufacturing micro, small and medium enterprises (MSMEs). Ranking of the identified HFs as per their importance was done using the AHP method. Further, the DEMATEL was used to get contextual interrelation and cause-effect relationships among the HFs. Results of the AHP method showed that factors vision of top management (HF4), collaborative human-robotsystem (HF1), real-time decision making (HF6), customer-centric servitization (HF8) and Skillset of employees (HF3) are the high priority factors. These factors also belong to the causal factor (most significant factor) group, determined using the DEMATEL approach. Thus, to enhance CM adoption, decision and policymakers of the manufacturing firm should provide more attention to improvize these factors. The study’s findings will be helpful to human resource managers, decision-makers and policymakers of the manufacturing firm to formulate effective short-term and long-term implementation plans and strategies. Further, the study’s outcomes can also be helpful for CM service providers to improve their marketing strategies. This is one of the preliminary research which analyzed human aspects of CM adoption across Indian manufacturing MSMEs using a hybrid AHP-DEMATEL approach.
Vaibhav S. Narwane, Irfan Siddavatam, and Rakesh D. Raut
CRC Press
Vaibhav S. Narwane, Angappa Gunasekaran, and Bhaskar B. Gardas
Elsevier BV
Riddhi Rajendra Thavi, Vaibhav S. Narwane, Rujuta Hemal Jhaveri, and Rakesh D. Raut
Emerald
PurposeThe paper focuses on reviewing and theorizing the factors that affect the adoption of cloud computing in the education sector narrowing the focus to developing countries such as India.Design/methodology/approachThrough an extensive literature survey, critical factors of cloud computing for education were identified. Further, the fuzzy DEMATEL approach was used to define their interrelationship and its cause and effect.FindingsA total of 17 factors were identified for the study based on the literature survey and experts' input. These factors were classified as causes and effects and ranked and interrelated. “Required Learning Skills and Attitude,” “Lack of Infrastructure,” “Learners' Ability” and “Increased Investment” are found to be the most influential factors.Practical implicationsThe resultant ranking factors can be used as a basis for managing the process of cloud adoption in several institutions. The study could guide academicians, policymakers and government authorities for the effective adoption of cloud computing in education.Originality/valueThe study investigates interdependency amongst the factors of cloud computing for education in context with developing economy. This is one of first study in higher education institutes of India.
Kirti Nayal, Rakesh Raut, Pragati Priyadarshinee, Balkrishna Eknath Narkhede, Yigit Kazancoglu, and Vaibhav Narwane
Emerald
PurposeIn India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).Design/methodology/approachThis study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.FindingsThis study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.Originality/valueThis study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.
Shashank Kumar, Rakesh D. Raut, Vaibhav S. Narwane, Balkrishna E. Narkhede, and Kamalakanta Muduli
Emerald
PurposeIn the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.Design/methodology/approachThis study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.FindingsThe study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.Practical implicationsThis study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.Originality/valueThe literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.
Mahesh Kavre, Bhaskar Gardas, Vaibhav Narwane, Nima Jafari Navimipour, and Senay Yalcin
Institute of Electrical and Electronics Engineers (IEEE)
The purpose of the study is to explore and analyze human factors that influence big data analytics and the cloud of things adoption across Indian micro, small, and medium enterprises (MSMEs). The human factors were identified through a literature survey and experts’ opinions. In order to develop a hierarchical structural model of identified human factors indicating the mutual relationship and classify the factors into cause-effect groups, a hybrid ISM-DEMATEL approach has been employed. Results of the study stated that “Lack of training and development programs” (HF11), “Lack of vision of top management and ineffective corporate governance” (HF13), and “Communication barrier between management and workforce” (HF4) are the most significant factors. The study's findings would be helpful to human resource managers and decision-makers of the firm to understand human-related factors responsible for technology adoption. Further, results can be validated with the investigation in other emerging economies.
Mohammad Reza Marjani, Mohammad Habibi, and Arash Pazhouhandeh
Inderscience Publishers
Vaibhav S. Narwane, Rakesh D. Raut, Bhaskar B. Gardas, Balkrishna E. Narkhede, and Anjali Awasthi
Informa UK Limited
ABSTRACT Smart manufacturing is transforming the industry, and its adoption offers significant benefits to manufacturers, suppliers and customers, such as enhanced organisational performance, competitive advantage, mass customisation, improved sustainability and better collaboration. The objective of this research is to identify the implementation barriers of smart manufacturing and to explore the cause–effect relationship between them using a fuzzy-based Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. Through expert opinions and literature review, 20 barriers to smart manufacturing adoption were identified. The results showed that ‘lack of product digitisation’, ‘lack of ubiquitous design and manufacturing setup’, ‘resistance to change’, ‘lack of data synchronisation’ and ‘lack of high tech infrastructure support’ were the significant barriers in the Indian Micro, Small and Medium Enterprises (MSMEs). The paper intends to guide the managers, governmental organisations and smart manufacturing service providers to formulate effective smart factory adoption strategies in the manufacturing domain.
Prashant Jain, Dhanraj P. Tambuskar, and Vaibhav Narwane
Emerald
Purpose The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose. Design/methodology/approach Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis. Findings The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain. Research limitations/implications The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust. Practical implications The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan. Social implications The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society. Originality/value This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.
Parthiban Srinivas, Vidyadhar V. Gedam, Balkrishna E. Narkhede, Vaibhav S. Narwane, and Ashwini Gotmare
Inderscience Publishers
Ankit Maroli, Vaibhav S. Narwane, and Bhaskar B. Gardas
Elsevier BV