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This paper aims to propose an elementary approach towards the measurement of a globoidal cam profile used in an automatic tool changer (ATC) of computer numerical control (CNC) machines.

A simple and unique online method has been designed for the profile metrology of the cam. This simple methodology will replace the traditional methodology of profile metrology of cam by coordinate measuring machine (CMM). A globoidal cam with an indexable turret and roller follower (rotating in an enclosed track) has been evaluated in our analysis. This analysis plays a significant role in the performance determination of the cam as well as the ATC of CNC machines.

A novel model has been designed and implemented to investigate the profile of a globoidal cam. The proposed methodology becomes an enhancement over the old methodology, i.e. measurement through CMM. Theoretical analysis and practical implementation prove the significance of the method.

An enhanced methodology to effectively measure the globoidal cam profile has been proposed. The practical implication of the proposed methodology remains for the CNC machine tool and ATC manufacturers. Finally, analytical explorations have been carried out to prove the validity of the proposal.

This paper aims to investigate the performance and positioning accuracy of computer numerical controlled (CNC) feed drive system using a ball screw-based pre-loading impact factor.

Initially, axial displacement of support bearings has been computed in relation to the different preload values. Among the computed values, a basic rule equation has been developed for the calculation of elongation in the bearings. The value of displacements computed from the developed equation has been considered as a pre-loading value, and its behavior on the feed drive system has been analyzed.

The elongation of bearings impacts the positioning accuracy and repeatability of the feed drive system and of CNC machines. Next, an analytical model for the rigorous assessment of CNC feed drive system has been designed and developed. The positioning accuracy of CNC machine in relation with different pre-loading values has been analyzed.

The results obtained from these investigations enhance the positioning accuracy of CNC machining centers. The optimum pre-loading value has been analyzed among the available ranges, and it has been proposed that optimal results have been achieved at 5 per cent of dynamic load rating.

This paper proposes improved explorations toward the performance of the CNC machines by optimizing the positioning accuracy through pre-loading. Finally, analytical estimations have been carried out to prove the validity of the proposal.

The purpose of the paper is to conduct a review of the literature on human-robot collaboration across different functions and activities of human resource management (HRM) and discuss its importance for change readiness in organizations.

A bibliometric analysis was conducted to identify emerging research themes in the fields of human resources (HR) and robotics, including change readiness. Based on the initial results of the bibliometric analysis, a systematic literature review was subsequently performed to gain a more specific understanding of research across various HRM functions and change readiness.

The results from bibliometric analysis and systematic review highlight that technological progression in HRM, such as AI-driven staffing and training techniques, improves effectiveness and personalization but raises concerns about privacy and job scrutiny. AI and robotics in performance evaluation enhance objectivity and reduce subjectivity, which can lead to disengagement. Generational differences, cultural factors and emotional quotient complicate readiness to adopt new technologies. The research emphasizes balancing technological effectiveness with employee involvement and meaningfulness to ensure successful implementation and engagement.

This paper synthesizes existing research, including literature, theoretical concepts and models, to identify best practices and successful strategies for implementing human-robot collaboration in HRM functions. It highlights gaps in the current literature and suggests areas for future research to advance the field of human-robot collaboration in HRM. By doing so, this paper enhances theoretical understanding while offering practical insights essential for effective change management.

This study explores the role of human resource management (HRM) practices in advancing sustainable development goals (SDGs) within emerging markets. Specifically, we examine how HRM practices in financial institutions in the emerging markets of India and China promote SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). We also propose a framework integrating these key SDG goals with core HRM functions.

Secondary data analysis was employed using data from sustainability reports of the top five Indian and Chinese banks listed in Forbes – the Global (2000) ranking for 2022–2023. These sustainability reports were analysed based on their reporting of indicators from the Global Reporting Initiative GRI 400 series, aligned with the SDGs 8, 10 and 13.

The result of the comparative analysis indicates that both Indian and Chinese banks use HRM practices of recruitment and selection, rewards and payments, workplace health and safety, and training and development to meet SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). Regarding the reporting and disclosure of HRM practices in diversity, equity and inclusion, Indian banks outperform Chinese banks, and these practices contribute significantly to SDGs 8, 10 and 13. The dominance of state-owned initiatives in China dictates the alignment of HRM strategies with economic priorities at the national level, highlighting the challenge of balancing global sustainability initiatives with a centralised management system.

The study provides a comprehensive examination of sustainability reports with a specific focus on HRM practices and their role in advancing SDGs. It applies institutional theory to understand the differences in the reporting and implementation of HRM practices that contribute to the achievement of SDGs.

This study focuses on accessing the impact of lockdown implemented to curb the pandemic of coronavirus disease 2019 (COVID-19) on prices of potato and onion crops using the time series analysis techniques.

The present study uses secondary price series data for both crops. Along with the study of percent increase or decrease, the time series analysis techniques of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), as well as machine learning; neural network autoregressive (NNAR) models were used to model the prices. For the purpose of comparison, the data from past years were taken as the period of normalcy. The behaviour of the forecasts for the normal periods and during the pandemic based on respective datasets was compared.

The results show that there was an unprecedented rise in prices during the months of lockdown. It could be attributed to the decline in arrivals due to several reasons like issues with transportation and labour availability. Also, towards the end of lockdown (May 2020), the prices seemed to decrease. Such a drop could be attributed to the relaxations in lockdown and reduced demand. The study also discusses that how some unique approaches like e-marketing, localized resource development for attaining self-sufficiency and developing transport chain, especially, for agriculture could help in such a situation of emergency.

A more extensive study could be conducted to mark the factors specifically that caused the increase in price.

The study clearly marks that the prices of the crops increased more than expectations using time series methods. Also, it surveys the prevailing situation through available resources to link up the reasons behind it.

Manufacturing companies are increasingly using green smart production (GSM) as a tactic to boost productivity since it has a number of advantages over conventional manufacturing methods. It costs a lot of money and takes a lot of work to create an SMS since it combines a lot of different technologies, including automation, data exchanges, cyber-physical systems (CPS), artificial intelligence, the Internet of things (IoT) and semi-autonomous industrial systems. Green smart manufacturing (GSM) activities provide the foundation for creating ecologically friendly and green products. However, there are a number of other significant barriers obstacles to GSM deployment. As a result, removing this identification of these hurdles in a systematic manner should be a top focus of this study.

This article seeks to identify and prioritize the nine barriers based on research and expert viewpoints on GSM challenges. The analytical hierarchy process (AHP) is used to prioritize the barriers.

The result depicts that, financial constraints is the most important barrier that followed by scarcity of dedicated suppliers, concern to data security lack of understanding of the surroundings, inadequate top management commitment, proper handling of data interfaces lack of support by government, employees' lack of training, concern to data security lack of environment knowledge, fear of change/resistance and constraints of technology.

The current research will help the manufacturing industry in Industry 4.0 to identify potential barriers to GSM implementation.

Green manufacturing (GM) entails the implementation of renewable production methods and eco-friendly procedures in manufacturing businesses. This study helps manufacturers come up with recycling and creative products, and manufacturers can give back to the environment by protecting natural areas by getting rid of the obstacles that get in the way.

Terrorism, an important component of Political risk as a possible determinant of ADRs (American Depository Receipts) returns have received little attention in academic literature. To address this issue and examine whether political risk is a major determinant of ADR returns of emerging market countries, this paper empirically examines market valuation of Indian ADRs around acts of terrorism. Using a sample of 52 such events in the sample period Jan 2003‐Dec 2003 we empirically analyze returns of Indian ADRs. The results from our study indicate a marginally negative significant effect, failing to indicate that event of terrorist attacks severely affect the Indian ADRs listed on the US stock market. This may be explained by a combined effect of; (a) the optimism of US investors towards emerging markets, and (b) market participants becoming more resilient and making informed choices around the “general” events of terrorism.