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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.

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.

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.

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.

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.

Multi-manned assembly lines are designed to produce large-sized products, such as automobiles. In this paper, a multi-manned assembly line balancing problem (MALBP) is addressed in which a group of workers simultaneously performs different tasks on a workstation. The key idea in this work is to improve the workstation efficiency and worker efficiency of an automobile plant by minimizing the number of workstations, the number of workers, and the cycle time of the MALBP.

A mixed-integer programming formulation for the problem is proposed. The proposed model is solved with benchmark test problems mentioned in research papers. The automobile case study problem is solved in three steps. In the first step, the authors find the task time of all major tasks. The problem is solved in the second step with the objective of minimizing the cycle time for the sub-tasks and major tasks, respectively. In the third step, the output results obtained from the second step are used to minimize the number of workstations using Lingo 16 solver.

The experimental results of the automobile case study show that there is a large improvement in workstation efficiency and worker efficiency of the plant in terms of reduction in the number of workstations and workers; the number of workstations reduced by 24% with a cycle time of 240 s. The reduced number of workstations led to a reduction in the number of workers (32% reduction) working on that assembly line.

For assembly line practitioners, the results of the study can be beneficial where the manufacturer is required to increased workstation efficiency and worker efficiency and reduce resource requirement and save space for assembling the products.

This paper is the first to apply a multi-manned assembly line balancing approach in real life problem by considering the case study of an automobile plant.

This paper aims to find the best tools to influence the improvement of sustainability in food supply chains (FSCs) by conducting a systematic review of articles. The reader will learn how the different industry 4.0 tools (I4.0T) benefit the FSC and the limitations of each tool.

A review of 436 articles published during the period 2019 to 2022 referenced in the Scopus and Web of Science databases was performed. The review was limited to articles published in English and directly related to Industry 4.0, circular economy and sustainability in the food supply chain.

The results show different contributions of I4.0, with some being more influential than others in improving sustainability in FSCs; for example, Internet of Things and Blockchain have been shown to contribute more toward transparency, traceability, process optimization and waste reduction.

The paper's contribution consisted of ranking according to their importance and the I4.0T that affect sustainability in FSCs by classifying the aspects of each tool and the sustainability factors through a categorization by the Analysis Hierarchy Process.

This study aims to examine a dual-pathway model that recognizes two distinct (formal and informal) but complementary mechanisms of knowledge exchanges – knowledge sharing and knowledge helping. It also investigates how team members use their limited human and psychosocial capital for prosocial knowledge effectiveness.

A survey-based approach was used to examine the hypotheses of the study. A moderated-mediation model was proposed and tested using bootstrap approach.

Knowledge sharing and knowledge helping were found to be the significant links through which human capital (capability) and psychosocial capital (motivation and efficacy) significantly predict prosocial knowledge effectiveness. Post hoc analysis suggests that human capital through knowledge sharing influences team learning, whereas the psychosocial capital through knowledge helping influences team leadership.

The present study found two distinct but complementary and yet necessary mechanisms of knowledge exchanges to be linked as the important outlay for the human and psychosocial capital to be effective in the prosocial knowledge behaviours.

The Indian manufacturing sector currently contributes 16–17% to gross domestic product (GDP) and gives employment to around 12% (2014) of the country's workforce. Among the various initiatives planned under Atma Nirbhar Bharat and “Make in India”, the Indian government aims to increase the share of the manufacturing sector to country's GDP to 25% by 2025. To ensure sustainable growth of the Indian manufacturing industries in global market, successful and implementation of strategies such as lean manufacturing, green manufacturing along with six sigma are crucial. This research aims at identification and analysis of barriers to successful implementation of integrated Lean Green Six Sigma (LG&SS) strategies.

Based on an evaluative literature review and expert's opinion, this research identifies 18 barriers to lean, green and six sigma implementation in the manufacturing industry in India. The inter-relationships among the barriers is identified using an integrated approach of ISM (interpretive structural modelling) and Fuzzy Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC).

ISM helps to derive key managerial insights and implementation plan based on the identified inter-relationships among the barriers. Fuzzy MICMAC analysis classifies the barriers into four categories, namely, autonomous, driver, dependent and linkage to understand their relative impact on the implementation of LG&SS practices in the Indian manufacturing industry. “Lack of cooperation and mutual trust between management and employees”, “The scarcity of time and work pressure”, “lack of continuous improvement work culture” and “lack of cooperation from suppliers” that forms the top most level of the model. “Weak legislation” is a highly significant barrier to LG&SS implementation in the Indian manufacturing industries.

It is expected that the findings of this research will help the Indian manufacturing industry to derive a sustainable competitive advantage through an effective implementation of LG&SS practices.

This study can be seen as the first attempt in investigating barriers to successful implementation of lean, green and six sigma strategies in the Indian manufacturing industries using ISM and fuzzy MICMAC analysis.

The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India.

The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India.

The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India.

The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue.

Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

Amidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.

Drawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.

The empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.

The research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.

The cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

The purpose of this paper is to identify and analyze the critical success factors (CSFs) behind the successful implementation of lean-green practices in Indian small- and medium-scale enterprises (SMEs).

The analysis employs two modeling approaches such as interpretive structural modeling (ISM) and interpretive ranking process (IRP) for examining the contextual relationships among the CSFs and ranks them with respect to key lean-green performance areas. A critical review of literature has led to the identification of total 25 CSFs for lean-green implementation. The list is reduced to 18 most relevant CSFs for the context of Indian SMEs based on experts’ (from industry and academia) opinions. The ISM is utilized to develop ten-level hierarchical model of CSFs. In order to gain greater insights into the nature of variables, the matrix of cross-impact multiplications applied to classification (MICMAC) analysis has been conducted.

The results show “Government support” as the most significant to the successful lean-green implementation in Indian SMEs and it is placed at the base of the ISM hierarchy. The CSF “Green disposal initiatives” is identified at the top level of the ISM hierarchy. Further, MICMAC analysis identifies five independent variables, eight linkages variables, four dependent variables and none autonomous variable. This may help SME managers to decide about resource priorities toward successful implementation of lean-green initiatives. Subsequently, the IRP has examined the dominance relationship and identifies “Organizational capabilities” as the most significant CSF.

This research intends to enable practitioners in prioritizing and managing strategic and tactical challenges involved in lean-green implementation in Indian SMEs.

This study makes an initial attempt in analyzing factors critical to successful implementation of integrated lean and green manufacturing practices in Indian manufacturing SMEs using ISM. The analysis is further enhanced by applying IRP to rank the CSFs by investigating the influence lean-green integration on various performance measures.

Improved performance in operational (lean) and environmental (green) dimensions has been extremely critical to the global competitiveness of organizations. As the performance of small- and medium-sized enterprises (SMEs) is influenced by various external and internal factors, the purpose of this paper is to analyze the lean–green performance of Indian manufacturing SMEs by investigating the influential relationships of various factors along with the set of lean and green practices adopted by the firms.

The study employs a holistic approach by integrating multiple case study and data envelopment analysis (DEA) in eight manufacturing SMEs to verify a set of five propositions relating issues such as organizational factors, quality and environmental management certifications, implementation of lean and green practices with operational and environmental performance in Indian SMEs. Within-case analysis and cross-case analysis are used for a qualitative investigation of cases while DEA with four input variables, two desirable output variables and one undesirable output variable, is used for quantitative investigation with returns to scale (RTS) and damages to scale (DTS) analysis.

The RTS/DTS results suggest that Indian SMEs exhibit decreasing RTS and increasing DTS, implying that they need to decrease their operational sizes in order to improve the operational and environmental performance. The possible alternative and more practical strategy could be to introduce new technology innovation and holistic adoption of manufacturing excellence initiatives such as lean and green.

The research findings provide insights into the lean and green performance enhancement approach in the context of SMEs. The study extends key managerial implications and policy-related guidelines.

Maintenance actions are initiated by the annual maintenance budget (AMB) allocated for a plant system. It ensures that the assets are kept in a healthy and reliable state. The purpose of this paper is to scientifically evaluate the AMB for an air conditioning (AC) plant system, which forms an integral part of facility management.

This paper estimates the AMB of an AC system for specific contextual conditions using incidence matrix of the budget influencing variables. The diagonal elements of the matrix represent the values of the budget variables, whereas the off-diagonal elements represent the degree of influence among these. The maintenance budget function is derived from this matrix and is used to evaluate the AMB for the AC system as a percentage of its asset replacement value (ARV).

The AMB for the AC plant system considered in the work is evaluated as 11.1 per cent of ARV for the conditions unique to its context. The actual maintenance expenditure is lower by 7.6 per cent than the estimated value of the budget.

The results of the methodology are a good guide for practicing facility managers. The shortcoming of the methodology is that it relies on fixed weights of the inter-relations among the budget variables, which may not be necessarily true.

This approach is expected to aid the maintenance managers in envisaging the AMB for their plant systems which operate under contextual conditions specific to their plant.

There is no evidence to indicate existence of maintenance budget evaluation methodology for an AC plant system based on its contextual conditions. This paper attempts to fill this gap.