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Sustainable human resource management (SuHRM), which aims to achieve positive environmental, social and economic outcomes at the same time, has gained prominence across industries. However, the challenges of implementing SuHRM across industries are largely under-studied. The purpose of this study is to identify the grey areas in the field of SuHRM by using an unsupervised learning algorithm on the abstracts of 607 papers published in prominent journals from 1995 to 2023. Most of the articles have been published post-2018.

The analysis of the data (abstracts of the selected articles) has been done using topic modelling via latent Dirichlet algorithm (LDA).

The output from topic modelling-LDA reveals nine primary focus areas of SuHRM research – the link between SuHRM and employee well-being; job satisfaction; challenges of implementing SuHRM; exploring new horizons in SuHRM; reaping the benefits of using SuHRM as a strategic tool; green HRM practices; link between SuHRM and organisational performance; link between corporate social responsible and HRM.

The insights gained from this study along with the discussions on each topic will be extremely beneficial for researchers, academicians, journal editors and practitioners to channelise their research focus. No other study has used a smart algorithm to identify the research clusters of SuHRM.

By utilizing topic modeling techniques, the study offers a novel approach to analyzing and understanding trends and patterns in HRM research related to sustainability. The significance of the paper would be in its potential to shed light on emerging areas of interest and provide valuable implications for future research and practice in Sustainable HRM.

Military and unmanned aerial vehicles (UAV) applications like rocket motor casings, missile covers and ship hulls use components that are made of maraging steel. Maraging steel has properties that are superior to other metals, making it more suitable for the fabrication of such components. A grey relational analysis (GRA) that is based on the Taguchi method has been utilised in the current study to optimise a laser beam welding (LBW) process. Further aspects such as GRA's optimum ranges and percentage contributions were also estimated.

A Taguchi L16 orthogonal array is utilised to design and conduct the experiments. Laser power (LP), welding speed (WS) and focal position (FP) are the three parameters are chosen for the process of welding. The output responses are the upper width of the heat-affected zone (HAZup), the upper width of the fusion zone (FZup) and the depth of penetration (DOP). The effect of the above key parameters on the responses was examined using an analysis of variance (ANOVA).

The results of ANOVA reveal that the parameter that has the most influence on the overall grey relational grade (GRG) is the FP. Finally, metallographic characterisation and a microstructural analysis are conducted on the weld bead geometry to demarcate the zone of HAZ and fusion zone (FZ).

As the most important criteria for LBW of maraging steels is the provision of higher DOP, higher FZ width and lower heat-affected zone, the study intended to prove the applicability of GRA technique in solving multi-objective optimisation problems in applications like defence and unmanned systems.