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Condition monitoring of squirrel cage induction motors (SCIMs) is indispensible for achieving fault-free working environment. As broken rotor bars (BRBs) are one of the more frequent faults in a SCIM especially where direct-on-line starting is indispensible, as in underground mines, a priori knowledge of fault severity in terms of the number of BRBs assists in effective fault monitoring. In this regard, this paper aims to propose a unique empirical relation to facilitate the determination of number of BRB.

Fast Fourier transform is used to obtain fault sideband amplitudes under varying number of BRBs and load torque for 5.5 kW, 7.5 kW, 10 kW, three-phase, 415 V, 50 Hz SCIMs in MATLAB/Simulink. The nature of variation is decided by an appropriate curve fitting technique for comprehending a unique empirical relation. The proposed empirical relation is validated by bootstrapping and z-test. Furthermore, hardware validation is done using 1 kW laboratory prototype with Labview interface.

The analytical study reveals the dependence of lower and upper sideband amplitudes on the number of BRBs, load torque and machine rating. Therefore, fault severity in terms of number of BRBs is accurately calculated using the proposed empirical relation if load torque, machine rating and amplitudes of lower and upper sidebands are known.

The unique empirical relation proposed in the present work provides accurate knowledge of fault severity in terms of the number of BRBs. This facilitates maintenance scheduling which shall reduce effective downtime and improve production.

Sustainability is an integrating concept and demands strategic attention in developing countries like India. Due to strict environmental regulations and ongoing government sustainable policies such as “Namami Gange,” leather industries are extensively facing challenges to conform themselves toward these sustainable policies. The major challenge faced by leather industries is the exponentially increasing cost of adaptation to sustainable product and process. Under these operational constraints, survival of Indian leather industries has become a major challenge. In this context, this paper aims to identify and evaluate sustainable manufacturing policies. The key performance indicators (KPIs) based on triple bottom line of sustainability can assist leather industries that are about to initiate adopting sustainable practices.

This paper demonstrates the role of KPIs for evaluating sustainable manufacturing policies for leather industries in India. Initially, an in-depth literature review analysis has been carried out to identify indicators for evaluation of sustainable manufacturing policies. In this work, an integrated methodology has been developed to refine the priority map of the aforementioned KPIs based on consensus building among experts using Kappa analysis. Total interpretive structural modeling (TISM) has been used to demonstrate relationships which explain the significance of the KPIs. Further, Matriced Impact Croises Multiplication Applique analysis has been carried out to explore the relationships amongst KPIs.

Based on above analysis, identified interactive relationships among the KPIs will assist managers and decision-makers to incorporate effective sustainable policies in leather industries.

It is expected that these identified interactive interrelationships between KPIs will certainly facilitate the leather industry to achieve higher sustainable performance and competitiveness.

This study carries out an in-depth literature review analysis of sustainable manufacturing policies in leather industry. The author proposes an integrated methodology using kappa analysis, consensus building and TISM for evaluation of sustainable policies based on the literature review analysis and expert opinion.

This study aims to propose a comprehensive framework among Key Performance Indicators (KPIs) for analyzing the Information Facilitated Product Recovery System (IFPRS) on the basis of feedback captured from the industry experts and researchers.

Total Interpretive Structural Modeling (TISM) methodology interspersed with fuzzy MICMAC is used to extract the interrelationships and develop a hierarchical structure among the identified KPIs. Further, the Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) method has been enforced to determine the intensity of these relationships and identify the most influential KPIs among identified KPIs from literature review and expert opinions. The outcome indicates that “information sharing,” “technology capacity” and “technology standards such as EDI, RFID” are the KPIs that have attained highest driving power.

This study has identified 15 KPIs of IFPRS and developed an integrated model using TISM and the fuzzy MICMAC approach, which is helpful to describe and organize the important KPIs and reveal the direct and indirect effects of each KPI on the IFPRS implementation. The integrated approach is developed, as the TISM model provides only binary relationship among KPIs, while fuzzy MICMAC analysis provides explicit analysis related to driving and dependence power of KPIs.

Structural Equation Modeling (SEM) analysis can be performed based on the adequate number of responses collected using structured questionnaire. More qualitative techniques like ELECTRE, TOPSIS, etc. can be used to establish the strength of relationship among the KPIs and ranking them to focus on the few critical KPIs.

The proposed modeling could empower various governmental and non-governmental regulatory bodies in formulation of policies to effectively tackle the problem related to product recovery systems. This study has strong practical implications, for both practitioners as well as academicians. The practitioners need to concentrate on identified KPIs more cautiously during IFPRS implementation in their organizations and the top management could formulate strategy for implementing these KPIs obtained.

There is a lack of studies related to the modeling of KPIs of IFPRS. As vast information is essential about the products returned during different product recovery stages, this study bridges the gap in literature by providing a framework for KPIs related to IFPRS. It is expected that the results originated will assist the experts to relevantly identify the significant and drop insignificant KPI for successful product recovery implementation and performance improvement of IFPRS.

Information-facilitated product recovery system (IFPRS) has captivated industry attention and has developed into a matter of consideration among the researchers because of enhanced climate concerns, jurisdictive logics and societal liabilities. Although IFPRS implementation has become an essential aspect in manufacturing industries functional in the developed nations, still, limited consideration has been given in the literature to analyze the issues to IFPRS implementation for a circular economy (CE) in emerging and developing nations. Therefore, the objective of this study is to recognize issues to implementing IFPRS for a CE in context of select manufacturing industries in India.

In this study, 24 potential issues are established from the literature and from suggestions from the experts. The issues are clubbed under five different perspectives of technical, government, organization, policy and knowledge. Further, fuzzy VIKOR technique is applied on the results obtained to prioritize the identified issues. A sensitivity analysis has been carried out to check the robustness of the framework.

The present study shows that lack of skills and expertise in IFPRS implementation for a CE (I2), deficient capital to implement a CE in IFPRS (I₉), inadequate in adopting recent IT technology (I₁₈), feasibility of IFPRS employment for a CE (I₆) and no efficient training and program to CE adoption (I₂₁) are the top five potential issues in implementing IFPRS practices for a CE in Indian manufacturing industries.

In literature, limited study has been observed on determining issues to implementation of IFPRS for a CE. A more systematic method and statistical confirmation is necessary to establish further new confronting issues. This study is limited to Indian manufacturing industries.

The main contribution of this study includes identification of issues and later prioritizing them to reflect their severity. This would help the industry practitioners to formulate strategies for handling the issues conveniently.

This paper aims to gain insight on the perception and role of the local government officials on climate change and resilience in Uttarakhand, India. Uttarakhand, being a sensitive mountainous region in India, faces the brunt of frequent climate-related disasters and their severe impacts. Therefore, it is crucial to understand how authorities perceive the issue of climate-related disasters and their level of commitment toward mitigation and adaptation programs.

The literature review method was used for a holistic understanding of the impact of climate change and consequential disasters. A questionnaire survey method, comprising open- and closed-ended questions, was also used on officials of different departments.

Among the noteworthy findings of the study include the understanding of the perceptions of authorities and their role in decision-making on mitigating impacts of climate change-related disasters, their support or lack of it, for measures toward capacity building and spreading awareness of the intervention programs by the government. The study analyzes the perception of decision-making officials at state and district levels and infers that the variation on opinions may be attributable to multiple factors, including their past experiences of dealing with disasters.

This study offers insights into the role of perception of local government officials concerning climate change-related disasters and alleviation of their consequences through related programs. The findings have the unique potential to serve as a guide for the government at state and district levels to assess various aspects of different disaster mitigation measures based on sectors and departments.

E-commerce refers to the facilitation and delivery of goods and services to the customers employing an electronic arrangement. For an e-commerce firm, the customer service level provided by its suppliers can make or break the firm. The purpose of this research is to help e-commerce enterprises in addressing the vast challenge of complex supplier selection and evaluation process that must be performed vigilantly.

The present study utilizes a three-pronged approach that integrates supplier management practices with the operational business practices of an e-commerce enterprise. In the first step, key performance factors for e-commerce capable suppliers are identified through an expert opinion and existing supplier management literature. Further, Data Envelopment Analysis (DEA) is employed to obtain the efficiency score for each supplier that enables their ranking on various performance parameters. Lastly, the suppliers are classified into different categories based on their performance and efficiency.

Under the proposed classification scheme, top five suppliers, i.e. supplier 1, 7, 9, 11 and 17 are categorized as HE (High Performance and Efficient). It is suggested that e-commerce enterprises must build long-term relationship with the identified top performing suppliers. The study also provides real insights into supplier's performance on a number of objective criteria. Further, the present study enhances the overall performance and productivity of an e-commerce firm by achieving input cost minimization and output quality maximization, simultaneously.

The results are valid for e-commerce enterprises in general. However, the present DEA model can be further evolved when applied in case of any particular e-commerce enterprise depending upon the internal capabilities of that firm. The nuances related to a firm's own supply capability development can be further explored by practitioners and researchers.

The proposed approach is expected to motivate decision-makers to consider using more sophisticated approached like DEA in supplier evaluation processes. Also, as a benchmarking technique, the proposed supplier classification approach is expected to be highly useful for practitioners in real-life settings.

The novel contribution of this study includes the supplier evaluation, ranking and classification for e-commerce enterprises based on the real-life data. The insights would help the practitioners to formulate novel strategies for appropriately investing in supplier relationships.

The purpose of this study is to empirically investigate the variables having an impact on profitability of public private partnerships (PPPs) in India using a balanced panel data of 171 unlisted PPPs from different infrastructure sectors such as road, power generation, real estate and ports.

Estimations were done using Arellano–Bond dynamic panel data estimation and seemingly unrelated regression models on a balanced panel data of 855 firm-years for 171 unlisted PPPs in India. To further test the estimation robustness, panel-corrected standard errors model was used.

The study findings indicate that in firm-specific factors, leverage, size, non-debt tax shield, growth and risk have significant positive impact on PPPs’ profitability, whereas in macroeconomic factors, only inflation has significant positive relationship. Although the relationship of all determinants is in sync with various theories and approaches, but these are not significant. Using the robustness test, the results are found to be robust and consistent with resource-based view and strategy-structure-performance approaches.

As PPPs are gaining prominence in the development of infrastructural resources, their profitability is of significant importance to drive private investments in infrastructure development, the identification of factors which determine profitability is critical for researchers, practitioners, policymakers and fund providers such as equity investors and debt providers.

The empirical literature on profitability determinants is focused on various sectors including small and mid-size enterprises (SMEs) and micro firms, but to the best of the authors’ knowledge, this is the first study, in both developed and developing economies, to empirically investigate the determinants of profitability for PPPs.

The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.

The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).

The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.

The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

The execution of product recovery strategies and the definition of an adequate system to manage its performance are crucial to move toward the employment of a successful circular economy (CE) concept. Defining strategies for the efficient management of product recovery requires product data that is difficult to obtain, making it harder to handle. However, efficient product recovery management can play a key role in shifting companies from a linear economy model to a more sustainable CE model, providing economic benefits and increasing customer satisfaction by recovering and adding value to the discarded product. Therefore, this study aims to provide better models to support decision-making and to evaluate product recovery performance.

The present study highlights a comprehensive two-stage decision approach to identify and examine the relevant key performance indicators (KPIs) for performance improvement of an information facilitated product recovery system (IFPRS) in a CE context. In the first phase, a structural equation modeling (SEM) methodology is adopted to categorize the KPIs by employing exploratory factor analysis and measurement of the model fit is obtained using the confirmatory factor analysis. Further, in the second phase, the KPIs are ranked and prioritized on the basis of expert’s recommendations adopting fuzzy-technique for order of preference by similarity to ideal solution (FTOPSIS).

Empirical investigation is conducted by compiling data from an association of six decision-makers (DMs) and two DMs from a respective prospect. The results highlight that “Technology Capacity” is ranked as the highest and is the most prominent KPI for successful employment of IFPRS practices. The results of the study would benefit policy makers and company directors in the selection of KPIs based on their importance in a context of high competition and greater pressure to adopt sustainable practices in the management of their companies.

As far as the authors know, no study has been performed till date to identify and construct a structural KPIs model for IFPRS performance improvement in the context of CE. The paper, therefore, proposes a two-phase SEM-TOPSIS technique to measure the impact of KPIs which is a new integration in the existing literature. The results of the study would benefit policy makers and company directors in the selection of KPIs based on their importance in a context of high competition and greater pressure to adopt sustainable practices in managing their organizations.