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This paper aims to examine the influence of the work–family interface on both work engagement and the psychological capital (PsyCap) of the liquid workforce. Also, drawing from the literature on consumer behaviour, the second objective of this paper is to investigate the impact of work engagement and PsyCap on customer advocacy.

A dyadic study was conducted, comprising 200 nurses and 200 patients from different healthcare service providers of India. Structural equation modelling was used to analyse the responses collected from nurses and the patients whom they served.

The results confirm that the home–work interface has a positive impact on work engagement and PsyCap. The findings also confirm a positive impact of PsyCap on customer advocacy, but the effect of work engagement on customer advocacy was not significant.

This study confirms that to keep liquid workers engaged in their work and to enhance their personal PsyCap, an organisation should provide the opportunity to maintain a balance between work and home needs. The findings also confirm that personal psychological resources (PsyCap) facilitate prosocial helping behaviour, which keeps customers closer and maintains them as true representatives of the organisation.

The present study is one of only a few preliminary studies examining the predictors of work engagement of liquid workers. Also, an inter-disciplinary approach was taken to understand the link between employee-level variables (home–work interface, work engagement and PsyCap) and a customer-level variable (customer advocacy).

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.