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The purpose of this paper is to understand the market for various e-learning platforms existing globally. The global pandemic transformed the traditional education sector into an e-learning industry and impacted the digital education sector proportionally. The wide impact on people to maintain a social distance shortened the space and enhanced student engagement with digital screens. The merchandise of electronic gadgets and e-learning platforms boosted global revenues ever before.

A digital data analytical tool retrieved the data for e-learning companies. While screening companies, the authors came up with more than 150 and later narrowed it to 71. Furthermore, revenues and market capitalisation data were collected from open-source websites of respective e-learning platforms at national and international stock exchanges. The data from the year 2018 to 2021 was used to predict the compound annual growth rate (CAGR) of the 2022–2027 market value of e-learning companies. The demographics of the statistical data were plotted using GraphPad Prism and Python Plotly. Also, the study attempted to underpin the association of annual revenue with market capitalisation for e-learning companies using Statistical Package for the Social Sciences (SPSS).

The financials of the e-learning platforms were compared from 2018 to 2021 based on the stock market exchange, which showed a gradual increase in revenues and influenced the market capitalisation by the 2022 financial year. Also, simultaneously validating the CAGR of 13.16%, the expected revenues from 2022 to 2027 were $830bn. The data plotted on the atlas choropleth exhibiting the e-learning users increased globally by 2022. The Web traffic data validated by the browsing gadget helped to validate the increase in revenues of the electronic gadget companies.

To the best of the authors’ knowledge, this is one of the pioneer studies postulating the study of e-learning demographics comparing the e-learning influence on the global digital market, Web user traffic and the education sector. The demographics of e-learning users by age, gender and gadget distribution validate the e-learning induction globally.

In certain industrial operations, workers are required to stand for a prolonged duration. This leads to muscular fatigue in the legs, posing a threat to the productivity and well-being of the workers. This paper aims to address this problem of women in the clothing industry with an exoskeleton designed for lower extremities and improve productivity.

Ulrich’s product design approach has been followed with suitable modifications. The methodology involves a study to justify the need for this product and terminating at the physical and virtual evaluations of the product. Required anthropometric parameters are considered along the design process.

The exoskeleton discussed in this paper is an innovative product made of Aluminium 6061 alloy. During the simulation phase of the product, total von-mises stresses to a part bearing 1 leg were 31.5 MPa, 94.7 MPa and 284 MPa for aluminium, SS308 and springs, respectively. These values are below the yield limit by a great margin. Based on a user survey of this product, 72% of the targeted customers were interested in buying. Also, comparing electromyography (EMG) mean value of the voltage between workers’ leg with and without exoskeleton revealed that there was an improvement in the voltage by 2.5% when exoskeleton was used.

This paper emphasizes, for the first time – the necessity of an exoskeleton indigenized for the Indian population and the process of realizing it by designing an exoskeleton.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.