Dr. Vaibhav S. Narwane

Scopus Publications

An exploration of factors influencing the adoption of Digital Twin technology in predictive maintenance
Seema Nagrani, Vaibhav S. Narwane
Journal of Quality in Maintenance Engineering, 2026
Purpose A Digital Twin (DT) is an advanced technology used in predictive maintenance (PdM) to enhance efficiency and reduce downtime. However, adopting DT in PdM is challenging. The focus of this study is to identify the key factors influencing the adoption of DT in PdM and to validate the conceptual and structural models. Design/methodology/approach The factors affecting the adoption of DT for PdM are identified through a literature review and finalized with the help of experts. Additionally, factor analysis is used to make the conceptual and structural model. Findings The seven factors were identified, and based on these factors, seven hypotheses were developed. Top management supports system monitoring, sustainable practices, safety and risk management and privacy and security concerns, which are supported by hypotheses. However, technical complexities and parameter optimization are not supported hypotheses. Research limitations/implications The sample size was finalized using the Cochran formula, and 100 responses were gathered for the study. Practical implications This study helps managers and policymakers to adopt DT for PdM effectively. The results may help management allocate the required funds and provide training programmes to adopt DT technology. Originality/value This study shows the influence of factors on the adoption of DT for PdM and determines an effective structure-based modeling method. This can be used in the manufacturing industry to select and implement the DT for PdM.
An interpretive analysis of influential drivers for control tower adoption in supply chains
Magesh kumar M. Nadar, Angappa Gunasekaran, Vaibhav S. Narwane
Supply Chain Analytics, 2026
A Supply Chain Control Tower (SCCT) provides real-time information, analytics, and decision support for supply chain management, helping organizations manage disruptions and inefficiencies before they occur. The complexity of contemporary supply chains is characterized by various influential factors that significantly affect the performance of Supply Chain Control Towers (SCCT). Interpreting the interactions among these factors is the key for supply chain managers in their efforts to improve decision quality and performance. Factor analysis is used to identify, prioritize, and rank the influential success factors that help accomplish SCCT effectiveness. This study investigates the influential drivers that shape SCCT adoption by applying Total Interpretive Structural Modeling (TISM) to evaluate how they relate and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis to determine their relative importance. The results illustrate that SC visibility and transparency are the principal factors, while the sustainable growth strategy is the least important factor influencing SCCT. This study delivers valuable practical understanding to supply chain managers regarding expediting efforts and effectively applying SCCT, ultimately boosting supply chain performance. • Identify key success factors for control tower implementation in supply chains. • Analyze factor relationships using interpretive structural modeling methods. • Highlight visibility and transparency as the most influential control tower driver. • Redevelop a hierarchical model to guide control tower deployment strategies. • Provide practical insights for enhancing decision-making in supply chain operations.
Designing a framework for a data-driven digital twin model to predict remaining useful life
Seema Nagrani, Vaibhav S. Narwane, Swati V. Narwane
Journal of Quality in Maintenance Engineering, 2026
Purpose Digital twin (DT) is a technology used in Industry 4.0 to enhance the efficiency of the industry. Therefore, data-driven DT should be implemented to determine the remaining useful life (RUL) of the component/system to prevent sudden breakdowns. Design/methodology/approach This study uses a secondary dataset to develop a data-driven DT model to predict the RUL of mechanical systems, such as turbo engines. Principal component analysis technique was used to reduce the features of the given dataset. Additionally, different machine learning algorithms such as Random Forest (RF), Convolutional Neural Network (CNN), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting and Long Short-Term Memory (LSTM) were used to develop an accurate data-driven DT model. A conceptual framework was discussed to develop the data-driven DT model for predicting RUL. Findings This study shows that LSTM is the most suitable model for predicting turbo engine RUL on the secondary dataset, achieving the lowest root mean squared error. Additionally, the receiver operating characteristic curve shows 98% accuracy for LSTM. Whereas AdaBoost, RF and CNN are not very accurate for the given dataset. Practical implications This study helps industry professionals to understand the role of a data-driven DT model in determining the RUL of mechanical components/systems to reduce downtime. It is helpful to implement a data-driven DT model for RUL prediction within a conceptual framework. Originality/value This study used a secondary dataset to develop a data-driven DT model for RUL prediction, accompanied by a conceptual framework for such a model.
A Simulation Framework for Surgical Subtask Automation
Ahaan Kotian, Rugved Palodkar, Vaibhav Narwane
Lecture Notes in Electrical Engineering, 2026
Investigation of unbalance fault using signature analysis and the taguchi method
Indrajeet Mahto, Seema Nagrani, Vaibhav S. Narwane
Noise and Vibration Worldwide, 2026
Mechanical systems are suddenly breaking down in the industry, reducing productivity. This happened due to various faults such as unbalance, bearing, misalignment, etc. Primarily, an unbalanced fault occurs in mechanical systems. This study aims to investigate unbalanced faults in mechanical systems to reduce the sudden stoppage in the industry. This study is carried out on the mechanical system for an unbalanced fault. The factors affecting the unbalanced system frequency and mass are investigated using Signature Analysis (SA) and the Taguchi Method. It is observed that the maximum amplitude of vibration occurs at the running frequency of the system when an unbalanced fault is present. Furthermore, it is observed that frequency dominates the unbalanced system more than the unbalanced mass. This study helps the industrialist and researcher implement SA in the industry to identify the type of fault easily. It also suggests that controlling the running frequency is more important than the unbalanced mass. This study is one of the very few investigations that represent unbalanced fault through SA and the Taguchi method.
Repurposing product development after COVID-19 by integrating Industry 4.0 and agile new product development practices in emerging economies
Manoj A. Palsodkar, Rajesh Pansare, Madhukar R. Nagare, Vaibhav Narwane
Journal of Engineering Design and Technology, 2025
Purpose After the COVID-19 pandemic, companies from a variety of sectors began repurposing their product development and manufacturing activities. To be successful, repurposing requires a framework that illustrates Agile New Product Development (ANPD) and Industry 4.0 practices. The current study aims to focus on developing a framework that managers and decision-makers can use to successfully adopt ANPD-Industry 4.0 practices and decision-making activities. Design/methodology/approach Initially, a literature review is conducted to identify practices related to ANPD and Industry 4.0. Similarly, performance metrics are identified through a review of the literature. To compute the weights of the shortlisted practices, the Pythagorean fuzzy Analytical Hierarchy Process is used and the Pythagorean fuzzy Combined Compromise Solution (PFCoCoSo) method is used to rank the shortlisted performance metrics. Findings According to the findings, ANPD practices (ADP) are the most prominent among shortlisted practices. Following that are Technology Adoption Practices, Organizational Management Practices (OMP), Human Resource Management Practices and System Integration Practices. Customer requirement analysis, for example, is an ADP practice that has a significant impact on the successful repurposing of product development activities. Practical implications The identified practices can make a significant contribution during repurposing product development activities. Practices that promote sustainable product development, as well as the use of advanced technologies, will be beneficial in improving organizational performance. Managers can evaluate performance using performance metrics that have been prioritized. Originality/value After the COVID-19 pandemic, this could be the first of its kind to develop an RPD framework.
Investigating determinants of digital twins for predictive maintenance
Seema Nagrani, Vaibhav S. Narwane
Journal of Quality in Maintenance Engineering, 2025
PurposeIn Industry 4.0, different technologies are used to improve the efficiency and reduce the downtime of the processes in the organization. It can be achieved by using predictive maintenance (PdM) technique to avoid the sudden breakdowns in the industry. It is important to implement digital twin (DT) for PdM. DT for PdM is in the nascent stage. This study is focused on the identification of determinants of the DT for PdM for real-life implementation.Design/methodology/approachThis study has focused on the determinants of DTs for predictive maintenance for implementation. To analyse these determinants, multi-criteria decision-making (MCDM) techniques were used by applying Decision-Making Trail and Evaluation Laboratory (DEMATEL) and interpretive structural modelling (ISM) approaches.FindingsIn this study, 13 determinants were found out through the literature survey. These determinants were classified into cause and effect in the DEMATEL approach. Similarly, ISM methodology was applied to these determinants to categorized determinants into different levels. DEMATEL and ISM results were compared, and it was found that real-time analysis, decision-making, self-monitoring and diagnosis are the most important.Practical implicationsThis study is useful for the academic researcher as well as the industrialist to implement DT in PdM. Therefore, it can be implemented in real-life application by considering the important determinants.Originality/valueThis is one of the first studies that represent the investigation of DT for PdM using the DEMATEL and ISM approaches.
Examining the effect of AI-BDA on manufacturing firm performance: An Indian approach
Vaibhav S. Narwane, Pragati Priyadarshinee
International Journal of Information Management Data Insights, 2025
Manufacturing firms face an uncertain and continuosly changing environment because of innovations, technological changes, and globalization. To cope with this quick and uncertain environment, firms need to be flexible. Artificial Intelligence (AI) and Big Data Analytics (BDA) are must for manufacturing firms to achieve the flexibility in procurement to manufacturing to marketing. This study explores role of AI-BDA played between Supply Chain Flexibility (SCF) and Supply chain firms performance(SCFP) through six hypothesis. A sample data of 297 responses from forty Indian manufacturing firms were collected. Exploratory and confirmatory factorial analysis were used to analyse the collected data. Out of six hypothesis, four hypothesis are supported. The results show positive impact of AI, BDA and SCF on supply chain firm performance. Also AI positively impacts on BDA . However two hypothesis not supported are positive effect of AI and BDA on SCF needs further investigated. The study can guide decision makers to understand role of AI and BDA to improve supply chain performance.
An adoption framework for agile new product development using hybrid RBWM-ISM-Fuzzy MICMAC approach
Manoj A. Palsodkar, Madhukar R. Nagare, Rajesh B. Pansare, Vaibhav S. Narwane
Journal of Modelling in Management, 2025
Purpose Agile new product development (ANPD) attracts researchers and practitioners by its ability to rapidly reconfigure products and related processes to meet the needs of emerging markets. To increase ANPD adoption, this study aims to identify ANPD enablers (ANPDEs) and create a structural framework that practitioners can use as a quick reference. Design/methodology/approach Initially, a comprehensive literature review is conducted to identify ANPDEs, and a structural framework is developed in consultation with an expert panel using a hybrid robust best–worst method interpretive structural modeling (ISM). During the ISM process, the interactions between the ANPDEs are investigated. The ISM result is used as input for fuzzy Matrice d’Impacts croises-multiplication appliqúean classment means cross-impact matrix multiplication applied to classification (MICMAC) analysis to investigate enablers that are both strong drivers and highly dependent. Findings The study’s findings show that four ANPDEs are in the low-intensity cluster and thus are excluded during the structural frame development. ISM output shows that “Strong commitment to NPD/top management support,” “Availability of resources,” “Supplier commitment/capability” and “Systematic project planning” are the important ANPDEs. Based on their driving and dependence power, the clusters formed during the fuzzy MICMAC approach show that 16 ANPDEs appear in the dependent zone, one ANPDE in the linkage zone and 14 ANPDEs in the driving zone. Practical implications This research has intense functional consequences for researchers and practitioners within the industry. Industry professionals require a conservative focus on the established ANPDEs during ANPD adoption. Management has to carefully prepare a course of action to avoid any flop during ANPD adoption. Originality/value The framework established is a one-of-a-kind study that provides an integrated impression of important ANPDEs. The authors hope that the suggested structural framework will serve as a blueprint for scholars working in the ANPD domain and will aid in its adoption.
Quantum machine learning a new frontier in smart manufacturing: a systematic literature review from period 1995 to 2021
Vaibhav S. Narwane, Angappa Gunasekaran, Bhaskar B. Gardas, Pinyarat Sirisomboonsuk
International Journal of Computer Integrated Manufacturing, 2025
Quantum machine learning can play an essential role in smart manufacturing applications. This paper aimed to understand the state of the art of quantum computing in machine learning and its role in smart manufacturing. A systematic literature review of 45 articles from 34 reputed journals from 1995–2021 was carried out. The study grouped documents into different categories and sub-categories for detailed analysis. The four broad categories, namely quantum neural network, quantum regression, quantum clustering, and quantum for smart manufacturing technologies, were studied. However, the analysis revealed that most studies belonged to the quantum neural network. Quantum for smart manufacturing is gaining the attention of researchers and practitioners, and developed countries such as the USA and China are leading towards the implementation of quantum machine learning for smart manufacturing. This study proposed a framework of quantum-integrated smart manufacturing and specified significant research gaps for future trends and directions. Also, valuable insights into quantum machine learning and its adoption for smart manufacturing have been offered.
Preface and Acknowledgements
Technologies for Energy Agriculture and Healthcare, 2025
Technologies for Energy, Agriculture, and Healthcare: International Conference on Technologies for Energy, Agriculture, and Healthcare (ICTEAH 2024)
Shailesh R. Nikam, Makarand G. Kulkarni, Vaibhav S. Narwane, Ninad D. Mehendale, Nilkamal P. More
Technologies for Energy Agriculture and Healthcare International Conference on Technologies for Energy Agriculture and Healthcare Icteah 2024, 2025
Systematic literature review of machine learning for manufacturing supply chain
Smita Abhijit Ganjare, Sunil M. Satao, Vaibhav Narwane
TQM Journal, 2024
Implementation of blockchain – IoT-based integrated architecture in green supply chain
Shivangi Viral Thakker, Santosh B. Rane, Vaibhav S. Narwane
Modern Supply Chain Research and Applications, 2024
A Framework for Adoption of Circular Economy Practices for Performance Improvement of Agile New Product Development
Manoj A. Palsodkar, Madhukar R. Nagare, Rajesh B. Pansare, Vaibhav S. Narwane
Circular Economy and Sustainability, 2024
Exploratory Analysis of Factors In°uencing Agile New Product Development Adoption in Manufacturing Industries
Manoj A. Palsodkar, Madhukar R. Nagare, Vaibhav S. Narwane
International Journal of Innovation and Technology Management, 2024
Identification of critical factors for big data analytics implementation in sustainable supply chain in emerging economies
Prashant Jain, Dhanraj P. Tambuskar, Vaibhav Narwane
Journal of Engineering Design and Technology, 2024
An exploration into the factors influencing the implementation of big data analytics in sustainable supply chain management
Dhanraj P. Tambuskar, Prashant Jain, Vaibhav S. Narwane
Kybernetes, 2024
Exploring the significant factors of reconfigurable manufacturing system adoption in manufacturing industries
Rajesh B. Pansare, Madhukar R. Nagare, Vaibhav S. Narwane
Journal of Modelling in Management, 2024
Role of cloud computing technology in the education sector
Riddhi Thavi, Rujuta Jhaveri, Vaibhav Narwane, Bhaskar Gardas, Nima Jafari Navimipour
Journal of Engineering Design and Technology, 2024
Unlocking factors of digital twins for smart manufacturing: a case of emerging economy
Bhaskar B. Gardas, Angappa Gunasekaran, Vaibhav S. Narwane
International Journal of Computer Integrated Manufacturing, 2024
Mediating role of cloud of things in improving performance of small and medium enterprises in the Indian context
Vaibhav S. Narwane, Rakesh D. Raut, Sachin Kumar Mangla, Bhaskar B. Gardas, Balkrishna E. Narkhede, Anjali Awasthi, Pragati Priyadarshinee
Annals of Operations Research, 2023
Is the Implementation of Big Data Analytics in Sustainable Supply Chain Really a Challenge? The Context of the Indian Manufacturing Sector
Prashant Jain, Dhanraj P. Tambuskar, Vaibhav S. Narwane
International Journal of Innovation and Technology Management, 2023
Risks to Big Data Analytics and Blockchain Technology Adoption in Supply Chains
Vaibhav S. Narwane, Rakesh D. Raut, Sachin Kumar Mangla, Manoj Dora, Balkrishna E. Narkhede
Annals of Operations Research, 2023
The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review
Harsh M. Shah, Bhaskar B. Gardas, Vaibhav S. Narwane, Hitansh S. Mehta
Kybernetes, 2023
Contribution of Internet of things in water supply chain management: A bibliometric and content analysis
Arman Firoz Velani, Vaibhav S. Narwane, Bhaskar B. Gardas
Journal of Modelling in Management, 2023
Role of human factors in cloud manufacturing adoption across manufacturing micro, small and medium enterprises
Mahesh Kavre, Vaibhav S. Narwane, Bhaskar B. Gardas, Vivek Sunnapwar
International Journal of Computer Integrated Manufacturing, 2023
Cloud-based manufacturing service selection using simulation approaches
Vaibhav S. Narwane, Irfan Siddavatam, Rakesh D. Raut
Smart Manufacturing Technologies for Industry 4 0 Integration Benefits and Operational Activities, 2022
Unlocking adoption challenges of IoT in Indian Agricultural and Food Supply Chain
Vaibhav S. Narwane, Angappa Gunasekaran, Bhaskar B. Gardas
Smart Agricultural Technology, 2022
To determine the critical factors for the adoption of cloud computing in the educational sector in developing countries – a fuzzy DEMATEL approach
Riddhi Rajendra Thavi, Vaibhav S. Narwane, Rujuta Hemal Jhaveri, Rakesh D. Raut
Kybernetes, 2022
Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic
Kirti Nayal, Rakesh Raut, Pragati Priyadarshinee, Balkrishna Eknath Narkhede, Yigit Kazancoglu, Vaibhav Narwane
International Journal of Logistics Management, 2022
Implementation barriers of smart technology in Indian sustainable warehouse by using a Delphi-ISM-ANP approach
Shashank Kumar, Rakesh D. Raut, Vaibhav S. Narwane, Balkrishna E. Narkhede, Kamalakanta Muduli
International Journal of Productivity and Performance Management, 2022
Sustainable implementation drivers and barriers of lean-agile manufacturing in original equipment manufacturers: a literature review study
Parthiban Srinivas, Vidyadhar V. Gedam, Balkrishna E. Narkhede, Vaibhav S. Narwane, Ashwini Gotmare
International Journal of Business Excellence, 2022
Evaluating the Effect of Human Factors on Big Data Analytics and Cloud of Things Adoption in the Manufacturing Micro, Small, and Medium Enterprises
Mahesh Kavre, Bhaskar Gardas, Vaibhav Narwane, Nima Jafari Navimipour, Senay Yalcin
IT Professional, 2022
Examining smart manufacturing challenges in the context of micro, small and medium enterprises
Vaibhav S. Narwane, Rakesh D. Raut, Bhaskar B. Gardas, Balkrishna E. Narkhede, Anjali Awasthi
International Journal of Computer Integrated Manufacturing, 2022
Mathematical programming model to optimise an environmentally constructed supply chain: a genetic algorithm approach
Mohammad Reza Marjani, Mohammad Habibi, Arash Pazhouhandeh
International Journal of Operational Research, 2022
Applications of IoT for achieving sustainability in agricultural sector: A comprehensive review
Ankit Maroli, Vaibhav S. Narwane, Bhaskar B. Gardas
Journal of Environmental Management, 2021
The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries
Vaibhav S. Narwane, Rakesh D. Raut, Vinay Surendra Yadav, Naoufel Cheikhrouhou, Balkrishna E. Narkhede, Pragati Priyadarshinee
Journal of Enterprise Information Management, 2021
Unlocking causal relations of barriers to big data analytics in manufacturing firms
Rakesh Raut, Vaibhav Narwane, Sachin Kumar Mangla, Vinay Surendra Yadav, Balkrishna Eknath Narkhede, Sunil Luthra
Industrial Management and Data Systems, 2021
A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India
Parth Rameshbhai Gabani, Umang Bipin Gala, Vaibhav S. Narwane, Rakesh D. Raut, Usharani Hareesh Govindarajan, Balkrishna E. Narkhede
Iet Collaborative Intelligent Manufacturing, 2021
Big data analytics: Implementation challenges in Indian manufacturing supply chains
Rakesh D. Raut, Vinay Surendra Yadav, Naoufel Cheikhrouhou, Vaibhav S. Narwane, Balkrishna E. Narkhede
Computers in Industry, 2021
Sustainable development challenges of the biofuel industry in India based on integrated MCDM approach
Vaibhav S. Narwane, Vinay Surendra Yadav, Rakesh D. Raut, Balkrishna E. Narkhede, Bhaskar B. Gardas
Renewable Energy, 2021
Analyzing the Obstacles to Sustainable Packaging in the Context of Developing Economies: A DEMATEL Approach
Bhaskar B. Gardas, Vaibhav S. Narwane, Nilesh P. Ghongade
Environmental Footprints and Eco Design of Products and Processes, 2021
Barriers in sustainable industry 4.0: a case study of the footwear industry
Vaibhav S. Narwane, Rakesh D. Raut, Vinay Surendra Yadav, A. R. Singh
International Journal of Sustainable Engineering, 2021
Framework for the implementation of an Internet of Things (IoT)-based water distribution and management system
Ankit Anilkumar Maroli, Vaibhav S. Narwane, Rakesh D. Raut, Balkrishna E. Narkhede
Clean Technologies and Environmental Policy, 2021
Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains
Rakesh D. Raut, Sachin Kumar Mangla, Vaibhav S. Narwane, Manoj Dora, Mengqi Liu
Transportation Research Part E Logistics and Transportation Review, 2021
The influence of internal-external factors on the cloud computing adoption
Vaibhav S. Narwane, Bhaskar B. Gardas, Rakesh D. Raut, Pragati Priyadarshinee, Balkrishna E. Narkhede
International Journal of Business Information Systems, 2021
Applications of industry 4.0 to overcome the COVID-19 operational challenges
Mr Shashank Kumar, Dr Rakesh D. Raut, Dr Vaibhav S. Narwane, Dr Balkrishna E. Narkhede
Diabetes and Metabolic Syndrome Clinical Research and Reviews, 2020
Mediating effect of big data analytics on project performance of small and medium enterprises
Sachin K. Mangla, Rakesh Raut, Vaibhav S. Narwane, Zuopeng (Justin) Zhang, Pragati priyadarshinee
Journal of Enterprise Information Management, 2020
A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018
Zeeshan Inamdar, Rakesh Raut, Vaibhav S. Narwane, Bhaskar Gardas, Balkrishna Narkhede, Muhittin Sagnak
Journal of Enterprise Information Management, 2020
Relevance of database and information retrieval system in mechanical engineering program: A case study
Chithra Biju Menon, Vaibhav Narwane
Journal of Engineering Education Transformations, 2020
Comparative analysis of pid and fuzzy logic controller: A case of furnace temperature control
Vaibhav S. Narwane
Journal of Current Science and Technology, 2020
Cloud computing in healthcare - A vision, challenges and future directions
Somnath Debnath
International Journal of Business Information Systems, 2020
Factors affecting the adoption of cloud of things: The case study of Indian small and medium enterprises
Vaibhav S. Narwane, Rakesh D. Raut, Bhaskar B. Gardas, Mahesh S. Kavre, Balkrishna E. Narkhede
Journal of Systems and Information Technology, 2019
A novel approach to determine the cell formation using heuristics approach
Shruti Shashikumar, Rakesh D. Raut, Vaibhav S. Narwane, Bhaskar B. Gardas, Balkrishna E. Narkhede, Anjali Awasthi
Opsearch, 2019
Linking big data analytics and operational sustainability practices for sustainable business management
Rakesh D. Raut, Sachin Kumar Mangla, Vaibhav S. Narwane, Bhaskar B. Gardas, Pragati Priyadarshinee, Balkrishna E. Narkhede
Journal of Cleaner Production, 2019
To investigate the determinants of cloud computing adoption in the manufacturing micro, small and medium enterprises: A DEMATEL-based approach
Rakesh D. Raut, Bhaskar B. Gardas, Balkrishna E. Narkhede, Vaibhav S. Narwane
Benchmarking, 2019
Intelligent unmanned aerial vehicles
Parth Thakkar, Anand Balaji, Vaibhav S. Narwane
Lecture Notes in Mechanical Engineering, 2019
Literature review on employment of unmanned aerial vehicles for disaster management
Proceedings of the International Conference on Industrial Engineering and Operations Management, 2019
Improvement in the food losses in fruits and vegetable supply chain - a perspective of cold third-party logistics approach
Rakesh D. Raut, Bhaskar B. Gardas, Vaibhav S. Narwane, Balkrishna E. Narkhede
Operations Research Perspectives, 2019
To understand the applications of cloud computing adoption in various sectors
Proceedings of the International Conference on Industrial Engineering and Operations Management, 2019
To identify the determinants of the CloudIoT technologies adoption in the Indian MSMEs: Structural equation modelling approach
Vaibhav S. Narwane, Balkrishna E. Narkhede, Rakesh D. Raut, Bhaskar B. Gardas, Pragati Priyadarshinee, Mahesh S. Kavre
International Journal of Business Information Systems, 2019
Adoption of cloud computing in manufacturing: SWOT analysis
Proceedings of the International Conference on Industrial Engineering and Operations Management, 2018
Assessing the determinants of cloud computing adoption for educational sector
Proceedings of the International Conference on Industrial Engineering and Operations Management, 2018

Dr. Vaibhav S. Narwane

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Scopus Publications