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The purpose of this article is to examine the applicability of design thinking to the strategic role of talent acquisition in organizations. While design thinking has become part of popular lexicon in contemporary design and engineering practice, as well as business and management, its principles can be seamlessly applied across multiple disciplines and industries. The premise is that by knowing about the process and the methods that designers use to ideate, and by understanding how designers approach problem solving, individuals and businesses will be better able to connect with and invigorate their ideation processes to take innovation to a higher level.

The methodology used was based on empirical research drawn from the authors > 20 years of experience in the industry as also secondary research, which has been appropriately referenced in the attached article.

The process of developing talent relationships forces managers to develop a more outward-looking view, staying on top of cutting-edge trends, building their company’s image and staying in sync with customer expectations. This is but the essence of the design thinking methodology – taking insights from people at the various stages, touch points of the process and build from the outside-in rather than from the inside-out.

The article is an attempt to articulate the challenges that confront organizations today as they compete for talent in the changing talent marketplace. Hopefully, the document will elevate some awareness and discourse on the subject and finally concretize on a roadmap that turns its talent acquisition into a strategic advantage with visible impact on the bottom-line. In essence, the article is about creating innovative efficiencies within the recruiting function.

The purpose of this paper is to examine how “Lean” principles from the manufacturing world can be adapted to create a best-in-class recruiting function and demonstrate the causal connection between the “value-added” recruiting activity and positive business results.

This concept paper is based on practitioner experience in leveraging Lean Six Sigma tools in improving the efficiency and effectiveness of the talent acquisition process.

Talent acquisition today is an activity fraught with risks – Did we hire the right person, the right skills the right fit? – and has the maximum impact on an organization bottom line. It is more than just posting a requisition and making an offer, but a series of sourcing activities, branding efforts, assessment processes and on-boarding activities and more – all designed to help an organization answer these key questions and find talent relevant to its business context. Appraising some of the evolving best practices in talent acquisition within the larger ambit of talent management issues facing organizations at large underscores the need for a new way of thinking about talent management.

Being more innovative in sourcing and recruiting can give organizations a sustainable competitive advantage with visible impact on the bottom line.

This paper aims to delve into the fast changing world of recruitment and throw light on some key challenges, trends and emerging practices that will continue to shape the future character and complexion of the recruiting discipline.

The paper draws on the authors over 20 years of professional experience in the talent space in both large corporate and professional services environment. It also draws from secondary research and review of available literature on the recruitment discipline.

The paper provides empirical insights on the key drivers and determinants of recruiting success in the VUCA (volatile, uncertain, complex and ambiguous) times organizations find themselves in today. It delineates the challenges and the opportunity proposition for talent acquisition as a discipline, as it negotiates its way from a business process to a critical business partner for organizations at large.

The paper is based on empirical research and secondary research of contemporary literature around the recruiting discipline.

The paper suggests implications for organizations to leverage the true value proposition of their recruiting function by embracing strategies and practices that elevate recruitment from a transactional, short-term focused activity to a strategic, integrated and long-term approach helping optimize their investments in people.

The paper provides seminal insights on the changing contours of the recruitment discipline and the implications for organizations at large in their quest to find the right talent to drive business success.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a large scale industrial size tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlet of a single strand bare tundish. The numerical prediction of the tracer concentration has been made with three different turbulence models; (a standard k‐ε, a k‐ε RNG and a Low Re number Lam‐Bremhorst model) which favorably compares with that of the experimental observation for a single strand bare tundish. It has been found that the overall comparison of k‐ε model with that of the experiment is better than the other two turbulence models as far as gross quantities like mean residence time and ratio of mixed to dead volume are concerned. However, it has been found that the initial transient development of the tracer concentration is best predicted by the Lam‐Bremhorst model and then by the RNG model. The k‐ε model predicts the tracer concentration much better than the other two models after the initial transience (t>40 per cent of mean residence time) and the RNG model lies in between the k‐ε and the Lam‐Bremhorst one. The numerical study has been extended to a multi strand tundish (having 6 outlets) where the effect of outlet positions on the ratio of mix to dead volume has been studied with the help of the above three turbulence models. It has been found that all the three turbulence models show a peak value for the ratio of mix to dead volume (a mixing parameter) when the outlets are placed 200 mm away from the wall (position‐2) thus signifying an optimum location for the outlets to get highest mixing in a given multi strand tundish.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a single strand bare tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlet of the tundish. The numerical prediction of the tracer concentration has been made with nine different turbulence models and has been compared with the experimental observation for the tundish. It has been found that the prediction from the standard k‐ε model, the k‐ε Chen‐Kim (ck) and the standard k‐ε with Yap correction (k‐ε Yap), matches well with that of the experiment compared to the other turbulence models as far as gross quantities like the mean residence time and the ratio of mixed to dead volume are concerned. It has been found that the initial transient development of the tracer concentration is best predicted by the low Reynolds number Lam‐Bremhorst model (LB model) and then by the k‐ε RNG model, while these two models under predict the mean residence time as well as the ratio of mixed to dead volume. The Chen‐Kim low Reynolds number (CK low Re) model (with and without Yap correction) as well as the constant effective viscosity model over predict the mixing parameters, i.e. the mean residence time and the ratio of mixed to dead volume. Taking the solution of the k‐ε model as a starting guess for the large eddy simulation (LES), a solution for the LES could be arrived after adopting a local refinement of the cells twice so that the near wall y⁺ could be set lower than 1. Such a refined grid gave a time‐independent solution for the LES which was used to solve the species continuity equation. The LES solution slightly over predicted the mean residence time but could predict fairly well the mixed volume. However, the LES could not predict both the peaks in the tracer concentration like the k‐ε, RNG and the Lam‐Bremhorst model. An analysis of the tracer concentration on the bottom plane of the tundish could help to understand the presence of plug and mixed flow in it.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a large scale industrial size tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlets of a six strand billet caster tundish. The numerical prediction of the tracer concentration has been made with six different turbulence models (the standard k‐ε, the k‐ε RNG, the Low Re number Lam‐Bremhorst model, the Chen‐Kim high Re number model (CK), the Chen‐Kim low Re number model (CKL) and the simplest constant effective viscosity model (CEV)) which favorably compares with that of the experimental observation for a single strand bare tundish. It has been found that the overall comparison of the k‐ε model, the RNG, the Lam‐Bremhorst and the CK model is much better than the CKL model and the CEV model as far as gross quantities like the mean residence time and the ratio of mixed to dead volume are concerned. However, the k‐ε model predicts the closest value to the experimental observation compared to all other models. The prediction of the transient behavior of the tracer is best done by the Lam‐Bremhorst model and then by the RNG model, but these models do not predict the gross quantities that accurately like the k‐ε model for a single strand bare tundish. With the help of the above six turbulence models mixing parameters such as the ratio of mix to dead volume and the mean residence time were computed for the six strand tundish for different outlet positions, height of advanced pouring box (APB) and shroud immersion depth. It was found that three turbulence models show a peak value in the ratio of mix to dead volume when the outlets were placed at 200 mm away from the wall. An APB was put on the bottom of the tundish surrounding the inlet jet when the outlets were kept at 200 mm away from the wall. It was also found that there exists an optimum height of the APB where the ratio of mix to dead volume and the mean residence time attain further peak values signifying better mixing in the tundish. At this optimum height of the APB, the shroud immersion depth was made to change from 0 to 400 mm. It was also observed that there exists an optimum immersion depth of the shroud where the ratio of mix to dead volume still attains another peak signifying still better mixing. However, all the turbulence models do not predict the same optimum height of the APB and the same shroud immersion depth as the optimum depth. The optimum height of the APB and the shroud immersion depth were decided when two or more turbulence models predict the same values.

The purpose of this study is a numerical analysis of steady-state heat transfer behavior of couple-stress nanofluid sandwiched between viscous fluids. It should be noted that this research deals with the development of a cooling system for the electronic devices.

Stokes model is used to define the couple-stress fluid and the single-phase nanofluid model is used to define the nanofluid transport processes. The fluids in all regions are assumed to be incompressible, immiscible and the transport properties in all the three layers are assumed to be constant. The governing coupled linear ordinary differential equations are made dimensionless by using appropriate fundamental quantities. The exact solutions obtained for the velocity and temperature fields are evaluated numerically for various model parameters.

The results are demonstrated using different types of nanoparticles such as copper, silver, silicon oxide (SiO₂), titanium oxide (TiO₂) and diamond. The investigations are carried out using copper–water nanofluid for different values of couple-stress parameter a with a range of 0 = a = 12, solid volume fraction ϕ with a range of 0.0 ≤ ϕ ≤ 0.05, Eckert number Ec with a range of 0.001 ≤ Ec ≤ 6 and Prandtl number Pr with a range of 0.001 ≤ Pr ≤ 6. It was found that the Nusselt number increases by increasing the couple stress parameter, Eckert number and Prandtl number and it decreases with a growth of the solid volume fraction parameter. It was also observed that using SiO₂–water nanofluid, the optimal Nusselt number is obtained. Further, using copper, silver, diamond and TiO₂, nanoparticles and water as a base fluid does not show any significant changes in the rate of heat transfer. The couple-stress parameter enhances the velocity and temperature fields whereas the solid volume fraction suppresses the flow field for both Newtonian and couple-stress fluid.

The originality of this work is to analyze the heat transfer behavior of couple-stress nanofluid sandwiched between viscous fluids. The results would benefit scientists and engineers to become familiar with the analysis of convective heat transfer and flow structures in nanofluids and the way to predict the heat transfer rate in advanced technical systems, in industrial sectors including transportation, power generation, chemical sectors, electronics, etc.

In 2010, Hero Honda (HH), the largest global two-wheeler manufacturing company (based on unit sales), terminated its 26 year old JV with Honda, effective 2014. In August 2011, HH, rebranded itself as “Hero”, with a nationwide campaign across media; over three months, the campaign was rolled out on 30 TV channels, leading websites, 200 radio stations, and 4, 000 cinema halls. Signages were changed in 4, 500 touchpoints over a weekend. The case documents the market and brand position of HH and its principal competitors, Bajaj and Honda in India, the rationale for ending the JV, the rebranding requirements, and the actions taken. Pedagogically, we evaluate the rebranding effort to sustain, create, and build consumer memories and emotions.