Somayya Madakam, Rajeev Kumar Revulagadda, Vinaytosh Mishra and Kaustav Kundu
One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the…
Abstract
One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.
Details
Keywords
B.S. Patil and M.R. Suji Raga Priya
The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components…
Abstract
Purpose
The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.
Design/methodology/approach
A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.
Findings
Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.
Research limitations/implications
Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.
Originality/value
Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.
Details
Keywords
The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to…
Abstract
The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.
Details
Keywords
Akansha Mer and Avantika Srivastava
Introduction: The Covid-19 pandemic wreaked havoc on the organisations in the form of increased job demands which manifested through increased workload, time pressure, etc…
Abstract
Introduction: The Covid-19 pandemic wreaked havoc on the organisations in the form of increased job demands which manifested through increased workload, time pressure, etc. Similarly, stress and burnout engulfed the employees. Remote work became the new normal post-pandemic. Remote workers require more engagement. This has brought Artificial Intelligence (AI) to the forefront for engaging employees in the new normal.
Purpose: With limited studies on AI-enabled employee engagement in the new normal, this study investigates and proposes a conceptual framework of employee engagement in the context of AI and its impact on organisations.
Methodology: A systematic review and meta-synthesis method is undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes.
Findings: Since many organisations are turning to remote work post-pandemic and remote work requires more engagement, organisations are investing in AI to boost employee engagement in the new normal. Several antecedents of employee engagement such as quality of work life, diversity and inclusion, and communication are facilitated by AI. AI helps enhance the quality of work life by playing a major role in providing fair compensation, safe and healthy working conditions, immediate opportunity to use and develop human capacities, continued growth and security, work and total life space, and social relevance of work life. This has led to positive organisational outcomes like increased productivity, employee well-being, and decreased attrition rate. Furthermore, AI helps in measuring employee engagement. The various tools of AI, such as wearable technology, digital biomarker, neural network, data mining, data analytics, machine learning (ML), natural language processing (NLP), etc., have gone a long way in engaging employees in the new normal.
Details
Keywords
Mahesh S. Shinde, Kishor Mahadeorao Ashtankar, Abhaykumar M. Kuthe, Sandeep W. Dahake and Mahesh B. Mawale
This review paper aims to provide an overview of applications of direct rapid manufacturing assisted mold with conformal cooling channels (CCCs) and shows the potential of this…
Abstract
Purpose
This review paper aims to provide an overview of applications of direct rapid manufacturing assisted mold with conformal cooling channels (CCCs) and shows the potential of this technique in different manufacturing processes.
Design/methodology/approach
Key publications from the past two decades have been reviewed.
Findings
This study concludes that direct rapid manufacturing technique plays a dominant role in the manufacturing of mold with complicated CCC structure which helps to improve the quality of final part and productivity. The outcome based on literature review and case study strongly suggested that in the near future direct rapid manufacturing method might become standard procedure in various manufacturing processes for fabrication of complex CCCs in the mold.
Practical implications
Advanced techniques such as computer-aided design, computer-aided engineering simulation and direct rapid manufacturing made it possible to easily fabricate the effective CCC in the mold in various manufacturing processes.
Originality/value
This paper is beneficial to study the direct rapid manufacturing technique for development of the mold with CCC and its applications in different manufacturing processes.
Details
Keywords
Shalini M. Patil, C.V. Vinay and Dinesh P.A.
The purpose of this paper is to study the amalgamated consequences of nonNewtonian fluid and permeability for nonporous journal spinning with constant tangential velocity inside a…
Abstract
Purpose
The purpose of this paper is to study the amalgamated consequences of nonNewtonian fluid and permeability for nonporous journal spinning with constant tangential velocity inside a rough porous bearing.
Design/methodology/approach
The flow is assumed to have developed under low Reynolds number, and the flow is governed by reduced Navier–Stokes equations. Based on Stokes theory for couple-stress fluid, a closed form of nonNewtonian Reynolds equation is obtained. Finite difference based multigrid method is adopted to study the various parameters of journal bearings.
Findings
It is found that bearing attributes such as pressure distribution and weight carrying capacity are commanding for nonNewtonian couple-stress fluid compared to the classical Newtonian case.
Originality/value
The multigrid method for the Reynolds equation is used, which accelerates the convergence rate of the solution and is independent of the grid size. The effects of couple-stress fluid promote the enhanced pressure distribution in the fluid. Both increased weight bearing capacity and delayed squeezing time reduce the skin-friction and hence take longer time to come in contact with each other.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2020-0051/
Details
Keywords
Swathi Kailasam, Sampath Dakshina Murthy Achanta, P. Rama Koteswara Rao, Ramesh Vatambeti and Saikumar Kayam
In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains…
Abstract
Purpose
In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.
Design/methodology/approach
In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.
Findings
In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.
Originality/value
The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.
Details
Keywords
For a thermal protection system (TPS) of long endurance hypersonic flight vehicle (HFV), its thermal insulation property not only determines by the manufactured morphology but…
Abstract
Purpose
For a thermal protection system (TPS) of long endurance hypersonic flight vehicle (HFV), its thermal insulation property not only determines by the manufactured morphology but also changes along time. A thermal conductivity prediction model for aerogel considering heat treatment effect is carried out and applied to solve the heat conduction problem of a TPS. The aim of this study is to provide theoretical and numerical references for further development of aerogels applying to TPSs.
Design/methodology/approach
A thermal conductivity prediction model for aerogel is established considering treatment effect. The heat conduction problem of a TPS is derived and solved by combining the differential quadrature method and the Runge–Kutta method. The prediction results of aerogel thermal conductivities are verified by comparing with those in literature, while the calculated temperature field of TPS is verified by comparing with that by ABAQUS.
Findings
Numerical results show that when applying the current prediction model, the calculated high temperature area in the aerogel layer is narrowed due to the decrease of the thermal conductivity during heat treatment process.
Originality/value
This study will be beneficial to carry out the precise design of TPS for long endurance HFVs.
Details
Keywords
Thamburaj Anthuvan, Kajal Maheshwari and Raghunath Dantu
This study conducts a comprehensive bibliometric analysis of pharmaceutical marketing and branding research, focusing on the transformative period of the last 15 years…
Abstract
Purpose
This study conducts a comprehensive bibliometric analysis of pharmaceutical marketing and branding research, focusing on the transformative period of the last 15 years (2009–2023), particularly during and after the COVID-19 pandemic. The purpose of this analysis is to highlight key shifts and trends in the field over this period.
Design/methodology/approach
This study examined 329 research papers sourced from JSTOR and PubMed. It explores the evolution of the field, emphasizing significant shifts and trends.
Findings
The analysis identified 2009 and 2014 as pivotal years for research activity, with The British Medical Journal and BioMed Central emerging as key publication platforms. Contributions from the USA, led by authors like Joel Lexchin, were particularly notable. The study also highlighted a shift toward AI-driven marketing, digital transformation and personalized medicine, especially in the post-COVID-19 period (2020 onwards). Co-authorship networks and keyword analysis emphasized the growing focus on regulatory compliance, patient engagement and the integration of digital tools in pharmaceutical marketing.
Originality/value
This study charts the historical and current state of pharmaceutical marketing and branding and sets the foundation for future research, emphasizing the increasing role of digitalization and AI.