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This paper aims to explore the impact of metaverse technology on the hospitality and tourism industry. The introduction of metaverse technology has revolutionised the way the hospitality and tourism industry works. In the present study, the authors have investigated the role of social media marketing in the adoption of metaverse technology in hotel booking in India.

An extended technology acceptance model was proposed for an empirical investigation in the Indian context. Sample of 344 respondents was collected across India using a purposive sampling technique for the purpose of data analysis. The structural model analysis is used to analyse the data collected from the respondents using the SmartPLS software to check the structural and the measurement fit of the model.

The adoption intentions were largely influenced by the utility, attitude (ATT) and ease of use of the technology, and social media marketing plays a major role in influencing the perceived usefulness (PU) and ease of use (PEU). The study finds positive ATTs of the customers for using metaverse technology for booking their hotels. PU and PEU significantly influence the ATT of the consumer indicating the traveller’s perception of the usefulness and ease of metaverse technology influence their ATTs towards adoption.

Influence of metaverse technology is at a nascent stage in India specifically for hotel booking and tourism. The authors have used discriminant validity by using the criteria for both the square root of the average variance extracted and heterotrait–monotrait ratio tests, and the results suggest that the constructs in the research are distinct from other.

The purpose of this paper is to investigate the performance of powder-mixed electric discharge machining (PMEDM) using three different powders which are aluminium (Al), silicon carbide (SiC) and aluminium oxide (Al₂O₃). Besides that, the influence of different tool materials was also studied in this experimental investigation. Hence, the work material selected for this purpose was AISI P20 steel and tool materials were copper, brass and tungsten. The performance measures considered in this work were material removal rate (MRR), tool wear rate and radial over cut (ROC).

The process variables considered in this study were powder types, powder concentration, tool materials, peak current and pulse on time. The experimental design, based on Taguchi’s L₂₇ orthogonal array, was adopted to conduct experiments. Significant parameters were identified by performing the analysis of variance on the experimental data.

Based on the analysis of results, it was observed that copper tool combined with Al powder produced maximum MRR (58.35 mm³/min). Similarly, the Al₂O₃ powder combined with tungsten tool has resulted least ROC (0.04865 mm). It was also observed that wear rate of tungsten tool was very low (0.0145 mm³/min).

The experimental investigation of PMEDM involving three different powders (Al, SiC and Al₂O₃) was not attempted before. Moreover, the study of influence of different tool materials (Cu, brass and W) together with the different powders on the electric discharge machining performance was very limited.

The purpose of this paper is to propose an integrative approach for improving failure modes and effects analysis (FMEA).

An extensive literature review on FMEA has been performed. Then, an integrative approach has been proposed based on literature review. The proposed approach is an integration of FMEA and quality function deployment (QFD). The proposed approach includes a two-phase QFD. In the first phase, failure modes are prioritized based on failure effects and in the second phase, failure causes are prioritized based on failure modes. The proposed approach has been examined in a case example at the blast furnace operation of a steel-manufacturing company.

Results of the case example indicated that stove shell crack in hot blast blower, pump failure in cooling water supply pump and bleeder valves failed to operate are the first three important failure modes. In addition, fire and explosion are the most important failure effects. Also, improper maintenance, over pressure and excess temperature are the most important failure causes. Findings also indicated that the proposed approach with the consideration of interrelationships among failure effects, failure mode and failure causes can influence and adjust risk priority number (RPN) in FMEA.

As manufacturing departments are mostly dealing with failure effects and modes of machinery and maintenance departments are mostly dealing with causes of failures, the proposed model can support better coordination and integration between the two departments. Such support seems to be more important in firms with continuous production lines wherein line interruption influences response to customers more seriously. A wide range of future study opportunities indicates the attractiveness and contribution of the subject to the knowledge of FMEA.

Although the literature indicates that in most of studies the outcomes of QFD were entered into FMEA and in some studies the RPN of FMEA was entered into QFD as importance rating, the proposed approach is a true type of the so-called “integration of FMEA and QFD” because the three main elements of FMEA formed the structure of QFD. In other words, the proposed approach can be considered as an innovation in the FMEA structure, not as a data provider prior to it or a data receiver after it.

Today, proficient practices are required to stimulate along various boundaries of the supply chain (SC) to exploit manufacturing resources economically, effectually and gracefully for retaining operational excellence. Accordingly, varieties of paramount practices, i.e. Lean, Agile, Resilient and Green practices, are integrated in present study with the objective to develop a Decision Support Framework (DSF) to select robust supplier under the extent of Lean-Agile-Resilient-Green (LARG) practices for a manufacturing firm. The framework is developed and validated in the Indian automotive sector, where the primary data is collected based on perceptions of the respondents working in an automotive company.

LARG metrics can ponder ecological balance, customer satisfaction, associations, effectiveness and sustainability and thus, the study consolidated LARG practices in one umbrella to develop a DSF. The analytical approach under DSF is developed by the integration AHP, DEMATEL, ANP, Extended MOORA and SAW techniques in present study to evaluate a robust supplier under the aegis of LARG practices in SC. DSF is developed by scrutinizing and categorizing LARG characteristics, where the selected LARG characteristics are handled by fuzzy sets theory to deal with the impreciseness and uncertainty in decision making.

The study has identified 63 measures (15 for Lean, 15 for Agile, 14 for resilient and 19 for Green) to support the robust supplier selection process for manufacturing firms. The findings of study explicate “Internal communication agility”, “Interchangeability to personnel resources”, “Manufacturing flexibility”, “degree of online solution”, “Quickness to resource up-gradation”, “Manageability to demand and supply change”, “Overstocking inventory practices” as significant metrics in ranking order. Additionally, “Transparency to share information”, “Internal communication agility”, “Manufacturing Flexibility”, “Green product (outgoing)” are found as influential metrics under LARG practices respectively.

A technical DSF to utilize by the managers is developed, which is connected with knowledge-based theory and a case of an automobile manufacturing firm is presented to illustrate its implementation. The companies can utilize presented DSF to impose service excellence, societal performance, agility and green surroundings in SC for achieving sustainable outcomes to be welcomed by the legislations, society and rivals. The framework represents an important decision support tool to enable managers to overcome imprecise SC information sources.

The study presented a proficient platform to review the most significant LARG alternative in the SC. The study suggested a cluster of LARG metrics to support operational improvement in manufacturing firms for shifting gear toward sustainable SC practices. The present study embraces its existence in enrolling a high extent of collaboration amongst clients, project teams and LARG practices to virtually eradicate the likelihood of absolute project failure.

Process parameters in Fused Filament Fabrication (FFF) can affect mechanical and surface properties of printed parts. Numerous studies have reported parametric studies of various materials using full factorial and Taguchi design of experiments (DoEs). However, a comparison between the two are not well-established in literature. The purpose of this study is to compare full factorial and Taguchi DoEs to determine the effects of FFF process parameters on mechanical and surface properties of Nylon 6/66 copolymer. In addition, perform in-depth failure mechanism analysis to understand why the process parameters affect the responses.

A full factorial DoE was used to determine the effects of FFF process parameters, such as infill density, infill pattern, layer height and raster angle on responses, such as compressive strength, impact strength, surface roughness and manufacturing time of Nylon 6/66. Micro-computed tomography was used to analyse the impact test samples before and after impact and scanning electron microscope was used to understand the failure mechanism of infill and top layers. Differential scanning calorimetry (DSC) scans of infill and top layers were then taken to determine if a variation in crystallinity existed in different regions of the build.

Analysis of variance and main effects plots reveal that infill density has the greatest effect on mechanical and surface properties while manufacturing time is most affected by layer height for the polymer used. A 20% reduction in infill increased impact strength by 19% on average, X-ray images of some of the samples before and after impact tests are presented to understand the reason behind the difference. Moreover, DSC revealed a difference in the degree of crystallinity between the infill and top layers for 80% infill density samples. In addition, Taguchi DoE is realized to be a more efficient technique to determine optimum process parameters for responses that vary linearly as it reduces experimental effort significantly while providing mostly accurate results.

To the author’s knowledge, no published paper has reported a comparison between predictive DoE method with full factorial DoE to verify their accuracy in determining the effects of FFF process parameters on properties of printed parts. Also, a theory was developed based on DSC results that as the infill is printed faster, it cools slowly compared to the top layers, and hence the infill is in a less crystalline state when compared to the top layers. This increased the ductility of the infill (of 80% infill samples) and thus improved impact absorption.

The coronavirus disease 2019 (COVID-19) pandemic has subjected a considerable strain on the healthcare (HC) systems around the world. The most affected countries are developing countries because of their weak HC infrastructure and meagre resources. Hence, building the resilience of the HC system of such countries becomes essential. Therefore, this study aims to build a resilience-based model on the HC sector of Pakistan to combat the COVID-19 and future pandemics in the country.

The study uses a novel hybrid approach to formulate a model based on resilient attributes (RAs) and resilient strategies (RSs). In the first step, the multi-criteria decision-making (MCDM) technique, i.e. full consistency method (FUCOM) is used to prioritize the RAs. Whereas, the fuzzy quality function deployment (QFD) is used to rank the RSs.

The findings suggest “leadership and governance capacity” to be the topmost RA. Whereas “building the operational capacity of the management”, “resilience education” and “Strengthening laboratories and diagnostic systems” are ranked to be the top three RSs, respectively.

The model developed in this study and the prioritization RAs and RSs will help build resilience in the HC sector of Pakistan. The policymakers and the government can take help from the prioritized RAs and RSs developed in this study to help make the current HC system more resilient towards the current COVID-19 and future pandemics in the country.

A new model has been developed to present a sound mathematical model for building resilience in the HC sector consisting of FUCOM and fuzzy QFD methods. The main contribution of the paper is the presentation of a comprehensive and more robust model that will help to make the current HC system of Pakistan more resilient.

This research aims to identify lean tools and techniques that are needed to be implemented to improve the performance in the fabrication industry. The objective is to find the wastes in manufacturing processes using value stream mapping (VSM) and prioritize the lean tools suitable to enable the attainment of leanness and streamline the processes.

VSM tool is applied in the industry to construct the current state map, identify improvement proposals and implement in future state. Fuzzy technique for order performance by similarity to ideal solution (TOPSIS), a multi-criteria decision-making technique (MCDM), is used to prioritize the identified improvement proposals. This study observed that mistake-proof processing and layout organization are the two techniques with the top priority that needs further improvement to enhance the leanness of the organization.

Upon successful implementation, the cycle time is reduced by 14.97%, and total inventory is reduced by 45.67% which leads to the improvement of value addition from 5.88 to 9.21%. Although lean has been adopted for many years, implementation of lean in the fabrication industry has been limited.

This study addresses the challenges in terms of implementing lean in fabrication industries and practical implications of lean tools and techniques and the prioritization of lean concepts against various lean criteria to enable leanness.

The deployment of improvement prioritization tool integrated with VSM in the context of a fabrication industry is the original contribution of the authors.