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The paper aims to clarify the relationship of various enablers of supply chain (SC) management like lean enablers, agile enablers and leagile enablers. It proposes modeling the enablers to find the most appropriate strategy or methodology for determining the lean enabled SC agility.

The paper proposed the fuzzy SWARA-WASPAS methodology for determining the role of lean in enabling the SC agility. Also in continuation the AHP methodology is applied to find the priority weightage and ranking of leagile enablers, and a comparative analysis is done to select the best approach among the above two methodologies so that it would be beneficial for all the stakeholders.

The paper provides the investigation and identification of 28 lean enablers, 11 agile factors which are highly responsible to affect any SC specially focusing of automobile sector. Apart from above 9 leagile enablers were also identified in the paper. Finally, the comparative analysis has been done in the results obtained by two methodologies – AHP & fuzzy SWARA-WASPAS – to determine the lean enabled SC agility, and also to which strategy should be adopted by the organizations as per the customized requirement of their SC.

The research limitation is that in future, there may be more number of lean, agile and leagile enablers which may be explored by different researchers in their findings, which may vary the output result accordingly. Though the research implications focus on having an advantage and impact on all aspects whether it is social, economic or commercial, there is a possibility of exploration of new and better decision-making tools and approaches in future. Also, the researchers are encouraged to test the proposed propositions further by taking case study of any automobile manufacturing organizations for the validation of the results.

The paper includes implications for the development of a powerful interrelationships between lean enablers, agile enablers and leagile enablers, which will help organization and the managers to take decisions regarding selection of best strategy appropriate to them to enhance their SC. This will also help new researchers of the field to take help of the research findings for exploring new and better optimization tools and techniques in future.

The findings of the research work will definitely help society, as the successful implementation of the lean, agile or leagile strategies in their SC system will leads to an increase in their efficiency and productivity, which will ultimately results in huge advantage to all the stakeholders directly or indirectly connected with the organization. The productivity dynamics cycle will also improve which will lead to more benefits to all in the market and achieving higher living index with better living standards.

This paper fulfills an identified need to study the various enablers of lean, agile and leagile SC, as well as their interrelationships. Also there is a need to understand the importance and effect of lean in enabling the SC agility.

Today the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.

In this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).

Further, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.

For lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.

This paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.

In the recent scenario, there has been an increasing trend toward lean practices and implementation in production systems for the improvement of an organization’s performance as its basic nature is to eliminate the wastes. The increasing interest of customers in customized products and the fulfillment of customers’ demand with good productivity and efficiency within time are the challenges for the manufacturing organization; that is why adopting lean manufacturing concept is very crucial in the current scenario.

In this paper, the authors considered three different methodologies for fulfilling the objective of our research. The analytical hierarchy process, best–worst method and fuzzy step-wise weight assessment ratio analysis are the three methods employed for weighting all the enablers and finding the priority among them and their final rankings.

Further, the best results among these methodologies could be used to analyze their interrelationships for successful lean supply chain management implementation in an organization. In this paper, 35 key enablers were identified after the rigorous analysis of literature review and the opinion of a group of experts consisting of academicians, practitioners and consultants. Thereafter, the brainstorming sessions were conducted to finalize 28 lean supply chain enablers (LSCEs).

For lean manufacturing practitioners, the result of this study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process.

This paper is the first of the research papers that considered deep literature review of identified LSCEs as the initial step, followed by finding the best priority weightage and developing the ranking of various lean enablers of supply chain with the help of various methodologies.

This study aims to investigate the greening practices adopted by micro, small and medium enterprises in India and recommend an empirically tested and validated green supply chain management (GSCM) practices construct. The study also aims at measuring the influence of pressures created by external and internal stakeholders on the GSCM score.

The study used a mixed-method approach. Qualitative methods, including literature review, expert opinion, focused group discussions and questionnaire-based surveys, were used for item generation and data collection. Quantitative methods included exploratory factor analysis (EFA) to find out the reasonable number of constructs to include in the scale of the GSCM practice, confirmatory factor analysis (CFA) to purify/confirm the scale, and finally, structural equation modelling (SEM) to select the best fit model and to test all causal relationships as portrayed in the base model. In the two-stage survey, convenience and purposive sampling methods were applied.

The study develops an empirically validated scale of GSCM practices, which has 6 dimensions and 16 items. This scale can measure the greening score of GSCM practices implementation. Further, findings confirm that the pressures from external and internal stakeholders are the significant forces for micro, small and medium enterprises to adopt GSCM practices. Finally, mediation function of external/internal pressures and green factors also does occur where GSCM is the mediator.

The study is an extension of available literature on GSCM practices utilizing the survey data from India. It contributes to the literature on micro, small and medium enterprises and GSCM practices implementation and expands the scope of theoretical applications.

For the managers, it provides useful insights about innovative practices to become eco-efficient. The second-order of scale reveals that managers need to equally focus on all factors rather than on any single one. Less GSCM practices score indicates the dearth of external and internal pressures on micro, small and medium enterprises. Therefore, managers need to collaborate with local customers, overseas customers, competitors and legislative agencies. Besides, training of employees, literacy of employees and support from top management also emerge as critical measures that help the industry implement GSCM practices.

GSCM as a topic had been extensively explored within different business areas and functions in the available literature. However, this study has some significant values. At prima facie, the study is the first that elucidates GSCM practices by incorporating the effects of pressures of different stakeholders in the context of Indian micro, small and medium enterprises which is considered as the most unorganized sector. Second, the proposed model of the study could provide useful direction for GSCM practices implementation in the unorganized sector in developing nations. Third, the study can also benefit academicians interested in the researched topic, organizations within different sectors and any other party interested in understanding more the concept of GSCM.

The purpose of this paper is to study and analyse the information communication technology‐based growth and diffusion of research using e‐journals for two universities belonging to different states in the western Himalayan region of India.

The paper uses an assessment period for the analysis of usage of e‐journals of 36 months from 2007‐2009. It demonstrates a relative comparison of the usage of e‐journals within the framework of a logistic model and the S‐curve. In the process, the status of internet users, from international level to national and university level, is also analysed.

Owing to the rapid technological growth and its phase‐wise development, the “Replacement hypothesis” does not permit the expectation of a smoother behaviour in the cumulative growth in internet users and consequently the usage of e‐journals in these universities. A simple statistical analysis of the usage was also undertaken. The coefficient of variation shows a wide fluctuation for four out of 12 publishing houses. Compound growth rate was also calculated and analysed for the two universities. A typical case of usage of e‐journals from one publishing house was also analysed more thoroughly with a likely future projection.

This analysis reflects the limitations inherent in the logistic model, particularly the linear growth rate and the appearance of the inflection point at Xs/2 in the S‐curve.

The paper presents an in‐depth analysis of growth and diffusion of usage of e‐journals in two universities located in the western Himalayan region of India, for the first time. It also projects the usage of e‐journals, reflecting research progress, until the time of saturation in the S‐curve of each of the universities in the region.

The purpose of this paper is to discuss the commercialisation mechanisms of the innovations that emerge from the informal sector of Indian economy. Also known as grassroots innovations, they are said to better fit with the local market demands and conditions in the developing nations of the world. The paper discusses the grassroots innovation ecosystem in India and the role that is played by the state in providing institutional support.

The paper is based on an exploratory study using both the primary and secondary sources of data. Primary data are taken from the interview of the innovators during the field work, whereas secondary data are acquired from research articles published in various journals indexed in Scopus and web of sciences, government publications and reports. The annual reports of National Innovation Foundation are analysed to gather information and to build the arguments for this paper. The secondary data are also collected and evaluated from the database of the grassroots innovators available on Grassroots Innovation Augmentation Network.

The paper provides insight into how the grassroots innovations are commercialised in India through different pathways such as social entrepreneurship, technology transfer and open source technology. It takes four case studies to discuss the institutional support to the grassroots innovator and the challenges in the diffusion of the grassroots innovations.

Due to the chosen research approach, the results cannot be generalised on all grassroots innovations. Researchers are encouraged to conduct a survey of more grassroots innovations in order to derive generalised outputs.

The paper includes implications for understanding the diffusion process of grassroots innovations that can be useful for all the emerging and developing nations.

The paper fulfils an identified need to study the diffusion modes of informal sector innovations and management of grassroots innovations.

Grassroots innovations, developed by local people using locally available resources, have shown the potential to provide low-cost technological solutions to the problems faced by underserved consumers in the global south. This paper aims to link the concept of grassroots innovations to energy use in the context of India.

The paper takes three case studies from the list of technologies scouted and nurtured by the National Innovation Foundation to critically discuss the potential of grassroots innovations for the dissemination and diffusion of urban sustainable energy uses. The data for this study has been collected from various secondary sources. It discusses the opportunities and challenges in promoting grassroots innovations for sustainable energy uses in urban settings.

The paper discusses the opportunities and challenges in promoting grassroots innovations for sustainable energy uses in urban settings.

Although the concept and understanding of grassroots innovations have well developed, its linkages with sustainable energy use in urban settings have received scarce or no attention in the literature.

The case study is based on field data, including in-depth semi-structured interviews with the main protagonist and related stakeholders of a large government hospital in Western India. After informed consent, the interviews with the stakeholders were conducted, transcribed and analyzed verbatim. In addition, secondary data from policy reports, newspaper articles and government websites was used to create the case. Since the protagonist works in the government system, her identity and other identifying information are disguised to maintain confidentiality.

The case study investigates the leadership challenges in a healthcare facility/hospital in public health. It traces the evolution of Dr Meena Sharma (Dr Meena), a leader in the government hospital ecosystem facing challenges such as infrastructural deficiencies, manpower deficit, healthcare bureaucracy and heavy patient load. This first-generation medical practitioner who transitioned from a private practice to a governmental one juggles balancing her demanding clinical practice, administrative responsibilities and teaching in the government hospital with her family responsibilities setup. However, in the wake of the upcoming LaQshya – Labour Room Quality Improvement Initiative by the Ministry of Health & Family Welfare, she strives to put together and motivate her team to work toward improving the quality of care during delivery and the immediate postpartum period at her hospital. Various issues arise in the organizational leadership for a woman leader such as adopting appropriate leadership style and using appropriate motivation and communication strategies for optimal performance.

The case study is aimed at teaching/training a) departmental heads of public and private hospitals, b) health program managers at higher and middle-level leadership roles, c) health policymakers at various levels in the government and other organizations and d) graduate and postgraduate students of public health, hospital management/administration. In addition to this, it can also be used for general management programs to teach organizational behavior, communication and leadership courses.

This study aims to include the diagnosis of an old concrete deck steel truss rural road bridge in the damaged and retrofitted state through vibration response signals.

The analysis of the vibration response signals is performed in time and time-frequency domains using statistical features-root mean square, impulse factor, crest factor, kurtosis, peak2peak and Stockwell transform. The proposed methodology uses the Hilbert transform in combination with spectral kurtosis and bandpass filtering technique for obtaining robust outcomes of modal frequencies.

The absence or low amplitude of considered mode shape frequencies is observed both before and after retrofitting of bridge indicates the deficient nodes. The kurtosis feature among all statistical approaches is able to reflect significant variation in the amplitude of different nodes of the bridge. The Stockwell transform showed better resolution of present modal frequencies but due to the yield of additional frequency peaks in the vicinity of the first three analytical modal frequencies no decisive conclusions are achieved. The methodology shows promising outcomes in eliminating noise and visualizing distinct modal frequencies of a steel truss bridge.

The findings of the present study help in analyzing noisy vibration signals obtained from various structures (civil or mechanical) and determine vulnerable locations of the structure using mode shape frequencies.

The literature review gave an insight into few experimental investigations related to the combined application of Hilbert transform with spectral kurtosis and bandpass filtering technique in determining mode frequencies of a steel truss bridge.

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure.

The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques.

The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge.

The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.