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The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.

The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.

The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.

This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.

The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

The purpose of this study is to understand the different types of risks affecting the demand for the automotive sector in India. The study is further trying to illustrate an approach for analyzing the relative intensities of these risks in the present uncertain business environment.

Risk on the overall demand is assessed by a combined Bayesian – multi-criteria decision-making approach. Data related to the different factors, affecting their product demand is collected from major automobile firms. Then, weights for these factors are evaluated by applying the analytic hierarchy process approach. Further, these weights are used in the Bayesian analysis network to evaluate the risk intensity for different subgroups, namely, political, economic, social, technological and environmental.

From the literature and experts’ opinion, total 16 risk factors have been finalized and these are further grouped into 5 categories i.e. political, economic, social, technological and environmental. It is observed that the demand for organizations functioning in the automotive sector is more vulnerable to economic risk as compared to other risks considered in the study.

Managers and decision makers of associated organizations can use the proposed framework to assess the demand risks so as to pre-evaluate their demand corresponding to future changes. Factors can be added or removed and importance could be assigned to different risk factors according to the prevailing business environment for an organization or sector. This will also help the organizations to conduct a more effective risk management in an uncertain business environment.

The study will help in better understanding of the various demand risks prevalent in the Indian auto sector. The methodology used, provides a novel approach for assessing the macroeconomic demand risks and can be used by the firms working in the automotive sector. The proposed methodology could be used for assessing supply chain risk or any other business initiative risk. The suggested approach will help managers in devising flexible management techniques so as to mitigate the risk.

The circular economy concepts are useful for resource conservation, eliminating waste and enhancing the efficiency of production to improve the sustainability of the system. The application of CE in Indian manufacturing industry is in nascent stage. India’s manufacturing sector significantly contributes to the economic development of the nation; therefore, this study aims to identify and analyze the sustainability related challenges faced during the implementation of the circularity concept.

Comprehensive survey of literature and the use of Pareto analysis yield ten significant challenges which are further analyzed using fuzzy-Decision-Making Trial and Evaluation Laboratory approach.

Findings revealed that noncompliance of environmental laws, revenue generation, design issues owing to technological limitations and less preference to refurbished and reused product are some of the major challenges to the CE practices in the manufacturing industry.

The results will help the researchers and practitioners in strategic decision-making for the improved application of circularity in the production process.

This paper contributes to the identification and prioritization of sustainability-related challenges faced during the implementation of a novel concept by a developing economy.

The purpose of this paper is to identify and analyze the challenges associated with the implementation of the concept of Maintenance 4.0 in industries.

The challenges in the implementation of Maintenance 4.0 are identified through a literature survey and interaction with professionals from the industry and academia. A structural hierarchy framework that integrates the methodologies of ISM and MICMAC is used for the analysis of Maintenance 4.0 implementation challenges. The framework establishes the interrelationship among challenges and segregates them into driving, linkage, dependent and autonomous groups.

A novel concept of Maintenance 4.0 under the aegis of Industry 4.0 is gaining appreciation worldwide. However, there are challenges in the adaptation of Maintenance 4.0 concepts among industries. The various challenges as well as their impact on the objective of implementation of Maintenance 4.0 are identified.

The practicing engineers, academicians, researchers and the concerned industries can infer from the results to improve upon the causes of such challenges and promote the implementation of Maintenance 4.0 most efficiently and effectively.

This paper is a novel, unique and first of its kind that addresses the most contemporary challenges in the implementation of Maintenance 4.0 concepts in industries.

Waste can be converted to a high-value asset if treated properly with smart solutions. The purpose of this research is to identify critical barriers hindering smart waste management (SWM) implementation in developing economies using comparative analysis and a mixed-method approach. The objective of this work is to provide exhaustive insight including the smart cities projects to discuss the deferring parameters toward IoT-enabled waste management systems.

To accomplish the objective, the present study followed mixed-method approach consisting of two phases: In the first qualitative phase, barriers in the adoption of IoT (Internet of Things) for SWM were identified using extensive literature review and discussion with selected experts. In the second phase, the quantitative analysis using the Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) method was performed on the selected barriers. The fuzzy DEMATEL methodology helps in prioritizing the most significant causal barrier by separating them into the cause-effect group. The comparative analysis was used to understand two different perceptions. To provide more detailed insight on the problems faced while implementing SWM in developing economies.

The results disclose that “Lack of government strict regulatory policies,” “Lack of proper financial planning” and “Lack of benchmarking processes” are the most critical causal barriers toward IoT-enabled SWM implementation that are hindering the vision of efficient and effective waste management system. Also, “Difficulty in implementing innovative technologies” and “Absence of Dynamic Scheduling and Routing” fall under the potential causal category. The effect barriers include “Lack of awareness among the community,” “Lack of source segregation and recycling commitment” and “Lack of service provider” as concluded in results considering the comparative analysis. The results can aid the policy-makers and stakeholders to identify the significant barriers toward a sustainable circular economy and mitigate them when implementing IoT-enable waste practices. Also, it assists to proactively build programs, policies, campaigns and other measures to attain a zero-waste economy.

The research is focused on the context of India but it provides new details which can be helpful for other developing economies to relate. The research addresses the call for studies from public-sector and citizen’s perspectives to understand the acknowledgment of SWM systems and critical success factors using qualitative and exploratory method analysis.

The practical implications of the study include strict regulatory policies and guidelines for SWM acceptance, proper financial administration and benchmarking waste-recycling practices (prominent causal barriers). The practical implication of the results includes assistance in smart city projects in handling barriers proactively. The “Lack of Benchmarking processes” provides a critical application to standardized recycling practices in developing economies to improve the quality of the recyclable material/product. The comparative analysis also provides in-depth reflection toward the causal barriers from both the perspective which can help the government and stakeholders to work in a unified manner and establish an efficient waste management system. The results also conclude the need for targeted training programs and workshops for field implementation of innovative technologies to overcome the causal barrier. Moreover, policy-makers should focus to improve source segregation and recycling practices and ensure dedicated communication campaigns like Swachh Bharat Abhiyan to change the behavioral functioning of the community regarding waste. Lastly, developing economies struggle with the adequacy of resources to establish SWM systems, hence the authors conclude that proper financial planning is required at the ground level for smart city projects to overcome the spillover effects.

The social implications of the study include a reduction in pollution and efficient handling of waste resulting in a healthier and cleaner environment using IoT technology. Also, the results assist decision-makers in developing economies like India to establish smart city projects initiatives effectively to improve the quality of life. It proposes to establish standardized recycling processes for the better quality of recyclables and help in attaining a sustainable circular economy.

The research is novel as it provides comprehensive and comparative information regarding the barriers deferring SWM including the field barriers. To our consideration, the present study serves the first to address the comparative analysis of barriers in IoT-enabled waste systems and establish the relationship from both the perspective in middle-lower income economies. The study also suggests that the effect barriers can be overcome automatically by mitigating the causal barriers in the long run.

The increase in automobile usage across the world has fortified the opportunities of maintenance service garages. However, there are significant numbers of challenges in front of maintenance service providers at all stages of the business. This paper identifies, analyzes and prioritizes various challenges associated with the establishment and survival of garages specific to Indian context.

In this paper, challenges for automotive service garage are identified through expert opinion, garage survey and literature. A structural hierarchical framework of the identified challenges is established through structural models, including interpretive structural modeling and analytic hierarchy process.

This paper has identified nine challenges, namely proliferation of new models and variants; technological advancements in automobile systems; demand of better service quality; space and ambience requirements; labor requirements; requirement of modern support equipments, tools and spares; safety requirements and prevention of occupational hazards; environmental norms and concerns; proper documentation requirements. The drivers and dependent variables have been identified. A hierarchical framework of challenges has been established.

This paper provides a comprehensive list of challenges and their priority in establishing an automobile maintenance garage business in Indian context. This will help the budding entrepreneurs and existing maintenance organizations to focus on the challenges that necessitate immediate attention and corrective actions.

This paper provides a significant contribution in the literature of garage maintenance services, which is established on the viewpoint of different collaborators associated with this business. This study will be a foundation to investigate further in this domain.

The objective of the study is to develop a methodology to strategically rank store locations using criteria such as population, store site characteristics, economic considerations, competition and so on to select the most optimal retail convenience store location.

A case of National Capital Region, India, for a 24-h convenience store was considered for the study and the major criteria that affect the performance of a convenience store are identified, such as population characteristics, economic criteria, competition, consumer accessibility, store size, total cost, site attractiveness and security. Fuzzy AHP is utilized to find the weightage for each criteria and a combination of fuzzy TOPSIS and grey relational analysis (GRA) is applied to rank the alternative using these criteria weight. Further, results obtained are compared with results from fuzzy TOPSIS and fuzzy VIKOR methods. Sensitivity analysis is also performed for ensuring the robustness of the framework.

It is observed that outcomes do not change under various settling coefficient values, demonstrating that the methodology is very robust. The developed framework will be quite useful to diverse retailers looking to expand and generate substantial profits.

A large sample size of number of locations encourages generalization of results. Strategic ranking of the selected locations is carried out on a few selected criteria. The study was limited by the designated geographical area.

The study contributes to the few available articles on convenience store selection using combination of fuzzy AHP, fuzzy TOPSIS and GRA for a developing country.

Industry 4.0 and circular economy are the two major areas in the current manufacturing industry. However, the adoption and implementation of Industry 4.0 and circular economy worldwide are still in the nascent stage of development. To address this gap, the purpose of this article is to conduct a systematic literature review on integrating Industry 4.0 and circular economy. Further, identify the research gaps and provide the future scope of work in this area.

Content-based analysis was adopted for reviewing the research articles and proposed a transition framework that comprises of four categories, namely, (1) Transition from Industry 3.0 to Industry 4.0 and integration with circular economy; (2) Adoption of combined factors and different issues; (3) Implementation possibilities such as front-end technologies, integration capabilities and redesigning strategies; (4) Current challenges. The proposed study reviewed a total of 204 articles published from 2000 to 2020 based on these categories.

The article presents a systematic literature review of the last two decades that integrates Industry 4.0 and circular economy concepts. Findings revealed that very few studies considered the adoption and implementation issues of Industry 4.0 and circular economy. Moreover, it was found that Industry 4.0 technologies including digitalization, real-time monitoring and decision-making capabilities played a significant role in circular economy implementation. The major elements are discussed through the analysis of the transition and integration framework. The study further revealed that a limited number of developing countries like India have taken preliminary initiatives toward Industry 4.0 and circular economy implementation.

The study proposes a transition and integration framework that identifies adoption and implementation issues and challenges. This framework will help researchers and practitioners in implementation of Industry 4.0 and circular economy.

Reviews of articles indicated that there are very few studies on integrating Industry 4.0 and circular economy. Moreover, there are very few articles addressing adoption and implementation issues such as legal, ethical, operational and demographic issues, which may be used to monitor the organization's performance and productivity.

Digital capabilities in operations and delivery through constant data acquisition and future predictions have accelerated digital servitization through reduced uncertainty. New flexibility in value-capture concepts like dynamic and value-based pricing is introduced, which was impossible before. This paper explores two things. Firstly, how embracing contractual flexibility of price-variance and contract lengths influences customer perceived value in artificial intelligence (AI) enabled digital offerings. Secondly, the role transparency plays in the perceived value of such offerings.

The paper uses an experiment-based survey and quantitative assessment within a business-to-business setup with 137 respondents across a couple of industrial manufacturers in the Nordic region.

The authors observations indicate that value-capture-related flexibilities introduced by digital offerings, namely price fluctuations and longer contract lengths, are perceived to deliver more value to customers than standard offerings with known conditions. The authors findings indicate that introduced flexibilities are perceived as opportunities rather than uncertainties leading to higher perceived value by customers. The increased value perception can be explained by the transparency of these offerings provided by data-driven digital technologies'.

The paper is an original work to understand the value-capture implication of digital servitization. The authors discuss the possibilities of different value-capture strategies that companies can adopt within digital business models.

The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system.

In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide area control signals, for the proposed controller, available in the power system are selected on the basis of eigenvalue sensitivity defined in terms of participation factor.

The effectiveness of the proposed controller with wide area signals is tested on two test cases, namely, two area network and IEEE 12 bus benchmark system. The comparative analysis of the proposed adaptive fuzzy controller is carried out with conventional STATCOM controller along with fuzzy-and neural-based supplementary controller all using selected wide area signals. The results show that neural network tuned fuzzy controller leads to better system identification and have enhanced damping characteristics over wide operating range.

In the available literature, numerous researchers have indicated the use of fuzzy logic controller and neural controller along with their hybrid schemes as STATCOM controller for improving the dynamics of the multimachine power system using local signals. The main contribution of the paper is in using the hybrid intelligent control scheme for STATCOM using wide area signals. The advantage of proposed scheme is that the performance of well-designed fuzzy system can be enhanced with the same training data that are used for designing a neural controller thus giving enhanced performance in comparison to individual intelligent control scheme.