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Article
Publication date: 11 November 2019

Vaibhav S. Narwane, Rakesh D. Raut, Bhaskar B. Gardas, Mahesh S. Kavre and Balkrishna E. Narkhede

The purpose of this study is to determine the significant factors affecting the adoption of Cloud of Things (CoT) by Indian small and medium-sized enterprises, using exploratory…

Abstract

Purpose

The purpose of this study is to determine the significant factors affecting the adoption of Cloud of Things (CoT) by Indian small and medium-sized enterprises, using exploratory and confirmatory factor analysis.

Design/methodology/approach

Significant factors that impact CoT implementation were identified through a detailed literature survey. A conceptual framework and hypotheses were proposed for linking the significant factors so identified, namely, cost saving, relative advantage, sharing and collaboration, reliability, security and privacy, technical issues and adoption intention. The data were collected from 270 Indian SMEs using an online survey. Structural equation modelling (SEM) was used to test the proposed model.

Findings

It was observed that factors such as “sharing and collaboration”, “cost saving” and “relative advantage” had a positive influence on CoT adoption. Findings of the study also supported the hypothesis that “security and privacy” were the prime concerns for CoT adoption.

Research limitations/implications

Sample coverage across different geographical areas with qualitative data can be helpful. The SEM methodology is only capable of verifying linear relationships; to counter this, a hybrid approach with tools such as artificial neural network and multiple linear regression can be used.

Practical implications

This study intends to guide the managers of SMEs, cloud service providers and regulatory organisations for formulating an effective strategy to adopt CoT. It may be noted that CoT is the prime building block of Industry 4.0 and SMEs will benefit from government support for the same.

Originality/value

This paper highlights the influence of factors on the adoption intention of CoT with a focus on the SMEs of a developing country like India.

Article
Publication date: 12 February 2019

Rakesh D. Raut, Bhaskar B. Gardas, Balkrishna E. Narkhede and Vaibhav S. Narwane

The purpose of this paper is to identify the critical factors influencing the cloud computing adoption (CCA) in the manufacturing micro, small and medium enterprises (MSMEs) by…

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Abstract

Purpose

The purpose of this paper is to identify the critical factors influencing the cloud computing adoption (CCA) in the manufacturing micro, small and medium enterprises (MSMEs) by employing a decision-making trial and evaluation laboratory (DEMATEL) methodology.

Design/methodology/approach

Through literature review and expert opinions, 30 significant factors were identified, and then a DEMATEL approach was applied for exploring the cause–effect relationship between the factors.

Findings

The results of study highlighted that five factors, namely, “hardware scalability and standardisation”, “cost (subscription fees, maintenance cost and implementation cost (CS1)”, “innovation”, “installation and up gradation (CS28)”, and “quality of service” were the most significant factors influencing the CCA in the case sector.

Research limitations/implications

The DEMATEL model was developed by considering expert inputs, and these inputs could be biased which can influence the reliability of the model. This study guides the organisational managers, cloud service providers and governmental organisations in formulating the new policies/strategies or modifying the existing ones for the effective CCA in the case sector.

Originality/value

For the first time. interdependency between the critical factors influencing CCA was discussed by employing the DEMATEL approach in the Indian manufacturing MSMEs context.

Details

Benchmarking: An International Journal, vol. 26 no. 3
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 6 April 2021

Shashank Kumar, Rakesh D. Raut, Vaibhav S. Narwane, Balkrishna E. Narkhede and Kamalakanta Muduli

In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and…

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Abstract

Purpose

In 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/approach

This 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.

Findings

The 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 implications

This 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/value

The 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.

Details

International Journal of Productivity and Performance Management, vol. 71 no. 3
Type: Research Article
ISSN: 1741-0401

Keywords

Article
Publication date: 21 July 2023

Rajesh B. Pansare, Madhukar R. Nagare and Vaibhav S. Narwane

A reconfigurable manufacturing system (RMS) can provide manufacturing flexibility, meet changing market demands and deliver high performance, among other benefits. However…

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Abstract

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

Article
Publication date: 12 October 2021

Vaibhav S. Narwane, Rakesh D. Raut, Vinay Surendra Yadav, Naoufel Cheikhrouhou, Balkrishna E. Narkhede and Pragati Priyadarshinee

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for…

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Abstract

Purpose

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

Design/methodology/approach

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Findings

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

Research limitations/implications

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

Originality/value

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Details

Journal of Enterprise Information Management, vol. 34 no. 5
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 19 August 2021

Riddhi Rajendra Thavi, Vaibhav S. Narwane, Rujuta Hemal Jhaveri and Rakesh D. Raut

The 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…

Abstract

Purpose

The 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/approach

Through 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.

Findings

A 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 implications

The 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/value

The 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.

Details

Kybernetes, vol. 51 no. 11
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 2 July 2020

Sachin K. Mangla, Rakesh Raut, Vaibhav S. Narwane, Zuopeng (Justin) Zhang and Pragati priyadarshinee

This study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge…

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Abstract

Purpose

This study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.

Design/methodology/approach

A sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.

Findings

The result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.

Practical implications

This study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.

Originality/value

For the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.

Details

Journal of Enterprise Information Management, vol. 34 no. 1
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 27 February 2023

Dhanraj P. Tambuskar, Prashant Jain and Vaibhav S. Narwane

With 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…

Abstract

Purpose

With 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/approach

The 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).

Findings

A 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/implications

This 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 implications

This 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 implications

The 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/value

This 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.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 14 February 2022

Arman Firoz Velani, Vaibhav S. Narwane and Bhaskar B. Gardas

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…

Abstract

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.

Article
Publication date: 30 December 2021

Harsh M. Shah, Bhaskar B. Gardas, Vaibhav S. Narwane and Hitansh S. Mehta

This 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…

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Abstract

Purpose

This 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/approach

The 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.

Findings

The 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 implications

AI 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/value

The 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.

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