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The present study aims to demonstrate the performance assessment of flexible pavement structure in probabilistic framework with due consideration of spatial variability modeling of input parameter.

The analysis incorporates mechanistic–empirical approach in which numerical analysis with spatial variability modeling of input parameters, Monte Carlo simulations (MCS) and First Order Reliability Method (FORM) are combined together for the reliability analysis of the flexible pavement. Random field concept along with Cholesky decomposition technique is used for the spatial variability modeling of the input parameter and implemented in commercially available finite difference code FLAC for the numerical analysis of pavement structure.

Results of the reliability analysis, with spatial variability modeling of input parameter, are compared with the corresponding results obtained without considering spatial variability of parameters. Analyzing a particular three-layered flexible pavement structure, it is demonstrated that spatial variability modeling of input parameter provides more realistic treatment to property variations in space and influences the response of the pavement structure, as well as its performance assessment.

Research is based on reliability analysis approach, which can also be used in decision-making for quality control and flexible pavement design in a given environment of uncertainty and extent of spatially varying input parameters in a space.

Hospital activities restore health and save lives; yet they produce infectious waste that might harm people and the environment if not handled carefully. The sudden increase in bio-medical waste (BMW) generation was caused due to the outbreak of Coronavirus disease (COVID-19) at the beginning of year 2020. There is a huge potential of businesses from BMW generated in the developing economies. The document destruction process would prove to be beneficial in determining the strategies and policies for the business from BMW. Therefore, the purpose of this study was to identify and evaluate the factors that influence the business prospects from BMW in developing economies.

The study focuses on identifying 18 factors based on an extensive literature review, questionnaire, theoretical foundations and interviews with experts in the context of developing economies. These factors are categorized into four groups namely “Culture, education and microeconomics”, “Infrastructure, social provision, and technology”, “Policy and institution” and “Integrated waste management system”. Consequently, these factors are prioritized to assist the decision-maker in comprehending the most critical aspects of accomplishing the best business practices. A framework based on the Best–Worst Method (BWM) is applied to evaluate and determine the significance of each identified factor.

The major contribution of this paper is to identify key factors that are responsible for successful implementation of the business practices from the generated BMW. The study also explains their nature, significance and inter-relationship among them using BWM. Current study applied a distinctive modeling approach to examine the contextual relationship among the key factors and to rank them as per their importance. The findings of the study suggest that “Setup for collection and segregation is the best alternative for business from BMW produced”.

The paper also presents strategies, which can be adopted as a guiding step by the decision-makers to formulate policies for the effective business practice from BMW. This research is expected to stimulate future applications of the BWM to facilitate long-term decision-making.

The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness.

Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters.

Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results.

The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms.

Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process.

This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.

The COVID-19 pandemic has had serious and far-reaching consequences on every aspect of human life. As various countries went into lockdown, hospitality and tourism was badly affected and this has had an impact on hospitality and tourism education too. This paper aims to assess the impact of the pandemic on hospitality and tourism education in India and the effectiveness of mitigation strategies in support of the industry. The study also proposes recommendations for resilience-building.

A pilot survey using semi-structured interviews was conducted and followed by a detailed questionnaire-based survey of industry representatives and educators. Selected industry leaders were also interviewed to understand the industry perspective on the pandemic, the “new normal” and the expectations of post pandemic graduates. The study focuses on four main areas – (1) existing interrelationships between industry and academia, (2) the efficiency of Internet-based education during the pandemic, (3) the “new normal” and the expectations of graduates and (4) recommendations for resilience-building.

There is a perceived gap between academia and industry that needs to be bridged. We found that the pandemic is seriously affecting education with challenges ranging from technological issues, disparities and reduced enrolments to student behavioural issues. Most impacted was the practical training component in operational areas and especially in food production and services. It is now clear that education needs a shift in focus from the more traditional, operational aspects of hospitality and tourism to technological awareness, knowledge and soft skills development. The research also identified a need for better two-way communication and information exchange between industry and academia. In fact, the lessons learnt from the pandemic need to be communicated from industry to academia. This is important given that industry expects graduates to be adaptive, compassionate, able to multi-task, familiar with the principles of crisis management and proficient with technology.

The study analyses possible long-term effects and implications for the workforce of the future as the industry struggles to respond to the pandemic. The study also suggests possible avenues and methods for industry and academia to form more symbiotic relationships and become more resilient.

This paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.

In order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.

Finally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.

Based on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.

Even though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

Scarcity of resources and ever-changing customer demand has driven the manufacturing organization to opt for an integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS). The purpose of this study is to identify and rank the various drivers for the adoption of ISGLSAMS for the more sustainable development of the industries.

Using a survey questionnaire, data were collected for the 14 ISGLSAMS drivers, identified through the literature review, from 108 Indian manufacturing industries. The sign test is used to check the relevance and significance of drivers. ISGLSAMS drivers are ranked based on the median and standard deviation. A multiple regression model was employed to investigate the effects of other drivers on the highest-ranked driver to strengthen the understanding of the drivers.

Competition, customer demand, technological changes, supply chain pressure, cost benefits, incentives, top management commitment and future legislation are found the strongest drivers for ISGLSAMS. For administering the competition for ISGLSAMS policymakers must focus on the availability of organization resources, customer demand, future legislation, incentives, supply chain pressure and technological developments.

The study contributes to a better understanding of ISGLSAMS drivers. Through this study government, stakeholders and policymakers may plan the policy, roadmap and strategies to drive the manufacturing organizations for the adoption of ISGLSAMS for more sustainable development of industries in India.

This work contributes to the identification and prioritization of the drivers for the more sustainable manufacturing system, i.e. ISGLSAMS (7 Rs based, i.e. 6 Rs of sustainability with 7th R, reconfiguration). The research also contributes to the (1) ISGLSAMS literature (2) the awareness and demand of (a) ISGLSAMS (b) reconfigurable, sustainable and modular products (c) redesign, recovery and refurbishing of the product to increase the product life cycle.

The purposes of this paper are (1) to identify and rank the various enablers for an integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS), and (2) to study their correlations and their impact on organizational performance.

Three tiers methodology is used to analyze the enablers for the successful adoption of ISGLSAMS. First, a total of 32 ISGLSAMS enablers are identified through a comprehensive literature review. Then, data are collected with a structured questionnaire from 108 Indian manufacturing industries. Then, an analytic approach is used to analyze (1) the relevance and significance of enablers and (2) their correlations (1) with each other, and (2) with the organizational performance outcomes, to strengthen the understanding of ISGLSAMS.

The findings suggest that top management commitment, sustainable reconfigurable manufacturing system, organization resources for 6 Rs, customers' and stakeholders' involvement, corporate social responsibility (CSR), customers and stakeholders-focused strategic alliances, dynamic manufacturing strategies, use of information and communication technology, concurrent engineering, standardized tasks for continuous improvement, virtual network of supply chain partners, real-time monitoring and control, training and education, employees' involvement and empowerment enablers are the higher level enablers for the adoption of ISGLSAMS. Findings also suggest that there is a scope for research in the incorporation of lot size reduction, Keiretsu-Kraljic supply chain relationship strategy, external collaborations with the stakeholders other than supply chain members, matrix flatter organization structure, employees' career development, justified employees' wages, government support for research fund and subsidies and vendor-managed inventory practices for ISGLSAMS. Top management commitment, sustainable reconfigurable manufacturing system, organization resources for 6 Rs, corporate social responsibility (CSR), dynamic manufacturing strategies, use of information and communication technology, concurrent engineering, virtual network of supply chain partners, real-time monitoring and control, training and education, employees' involvement and empowerment have a significant effect on (1) sustainable product design, (2) sustainable production system, (3) improvement in the sale, (4) improvement in market responsiveness, (5) improvement in the competitive position and (6) improvement in the global market image.

Through this study of ISGLSAMS enablers and their interdependence, and their impact on ISGLSAMS performance outcomes government, organizations, stakeholders, policymakers and supply chain partners may plan the policy, roadmap and strategies for the successful adoption of (1) ISGLSAMS in the organizational value chain, and (2) Industrial ecology and industrial symbiosis in India. The study also contributes to the industrial managers, and value chain partners a better understanding of ISGLSAMS.

This study is the first attempt to understand (1) the ISGLSAMS enablers and their correlations, and (2) the effect of ISGLSAMS enablers on ISGLSAMS performance outcomes to get the competitive and sustainability advantage. The study contributes to the practitioners, policymakers, organizations, government, researchers and academicians a better understanding of ISGLSAMS enablers and its performance outcomes.