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Discusses the importance of information flow within the relatively complex supply chain of a communications company with centralized European production centres and national sales and service organizations. Analyses the activities within the supply chain and illustrates the importance of the relationship between goods movement and the exchange of information. Information needs to be managed before a sale is made, while satisfying the sales order and during after‐sales maintenance. Concludes that responsiveness to customer demand, and overall customer satisfaction, cannot be achieved without proper management of both the goods movement and information flow throughout the supply chain.

Casting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.

A systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.

This study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.

This paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.

The systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

Rooting in the literature on training and laying on Kirkpatrick model, this paper aims to explore key drivers of corporate training to identify how they can be combined into an integrated framework of learning for human capital development.

By adopting the constructivist grounded theory, this contribution analyzes the experience carried out in the last ten years by Virvelle, an Italian corporate training firm.

Results show the rise of five core categories, g1iving rise to an integrated model of Kirkpatrick. Their dynamic interplay led to a new orientation of Kirkpatrick model giving rise to a metalearning ecosystem.

Managerial implications have identified key factors on which building and implementing appropriate corporate training programmes capable of triggering co-generative processes of value creation. Particularly, the essential role of learning quality culture, digital technology and personalization are detected in integrating not only hard but furthermore soft shades of learning. Concerning theoretical implications, the emergence of key structural and systems enabling dimensions for learning, and contextual mechanisms involved in reshaping training effectiveness and achieving integrated learning outcomes are detected. The main limitation of this study lies in the need to generalize results: the conceptualized framework needs to be empirically tested.

The value of this research is built along three main points. The first is the integration among the core categories that an integrated learning system can be built on, promoting learning quality culture through positive feedback loops. The second is represented by the chance to enhance an integrated mutual knowledge development among engaged actors, thereby shaping a more holistic and multidimensional learning model. The third is related to the transversal role that digital technology plays in all phases of the training process as it integrates and enriches them.

This paper aims to focus on the housing situation in Indian cities and the various stakeholders involved in the housing sector. This study addresses the conflict in expectations of the supply and demand side of the housing and establishes the factors crucial to making housing successful for all the stakeholders, essential for sustainable urban development.

This paper is developed using the content analysis of preceding independent reviews on housing sustainability by authors across the globe, a review of the current housing situation in Indian cities and a Pilot study of the desired qualitative aspects of housing by the consumer group.

This paper identifies the challenges and constraints of the various stakeholders involved in the housing sector. A comprehensive list of all the parameters, both qualitative and quantitative, essential for successful housing implementation, focused on the preferences of the demand side, also referred to as the “Critical Success Factors (CSFs)” is generated. The results advocate a strict consideration and integration of these CSFs with the housing policies for all future housing projects.

Most of the studies conducted in the past have reviewed the housing situation in India from the perspective of the government authorities and the initiatives undertaken by them to improve the housing crisis. However, a gap exists in achieving a balanced understanding of the supply-side constraints, with the demand-side expectations from the housing. Focusing only on the supply without adequately understanding the consumption can only lead to further chaos in the already turbulent housing situation.

To the best of author’s knowledge, this is among the first attempts to address the housing scenario in urban centers of India, simultaneously from the perspective of both the demand side and the supply side of the housing, giving additional impetus to the expectations of the demand side which are often ignored. This study becomes more relevant in the ongoing pandemic situation to be able to provide appropriate and acceptable housing solutions to all.

There have been numerous publications on human monkeypox since it was reported. With the help of bibliometric analysis, this study examined research hotspots and future trends related to human monkeypox. Science mapping was used in this study to identify influential monkeypox researchers, institutions, articles, keywords, thematic structures, and clusters of articles.

Based on a validated search query, bibliometric analysis of data collected from Web of Science from 1989 to September 2022 was conducted. Using the “Title-Keyword-Abstract” search option, the search query consisted of keywords “Monkeypox” OR “Monkeypox virus” OR “monkeypox” OR “monkey pox” OR “MPXV.” With the state-of-the-art tools Bibliometrix package of R Studio and VOSviewer, performance analysis and science mapping, as a part of standard bibliometric research of monkeypox research were conducted.

Researchers published 708 monkeypox papers from 1989 to September 2022, with American researchers publishing 460 papers. Further, USA had the highest international cooperation in terms of collaborative research output. Centers for Disease Control and Prevention (CDC) is a global leader in monkeypox research since it is the most prolific and collaborative organization. There have been the most published papers on monkeypox in the Journal of Virology. Damon Inger K is also the most prolific and influential researcher in monkeypox research, with the highest number of publications and citations. In total, 1,679 keywords were identified in the study. From the cluster analysis four themes were identified in monkeypox research. They are (1) clinical features, (2) monkeypox virus epidemiology, (3) monkeypox virus vaccine defense, and (4) monkeypox virus-related treatment measures.

Analysis of collaboration, findings, networks of research, and visualization separates this study from traditional metrics analysis. Currently, there are no similar studies with similar objectives based on the authors' knowledge.

Housing policies in India (last modified in 2017) define “affordable housing” on three main parameters – income of the target group, dwelling unit size and house price to income ratio. The Covid-19 pandemic has questioned the robustness of the defining parameters of affordable housing. This paper aims to study the impact of Covid-19 pandemic on housing affordability and adequacy for the urban poor and highlights how one pandemic has directly challenged the practicality of the affordable housing criteria in urban India.

The study is based on a pilot test conducted through interviews with the consumer group. Furthermore, the affordable housing policies of urban India were studied in conjunction with the pandemic guidelines laid down by the World Health Organization. The consumer responses were analyzed in relation to the policies and guidelines to arrive at the inferences. The secondary case examples of the Dharavi slums in Mumbai and the Savda Ghevra slum resettlement colony in Delhi, along with the findings of the primary survey in the economically weaker section category, are used to formulate the conclusions.

The pilot test conducted for the target consumer group clearly indicates that (i) the pandemic has severely affected the housing purchase capacity of the target consumer, thereby questioning the “affordability” of housing; (ii) proposed housing solutions are inadequate to enable livability, thereby questioning the “adequacy” of housing; and (iii) proposed housing solutions are inept to accommodate pandemic protocols. The Covid-19 pandemic and the conditions it imposed on the built environment clearly highlight the inadequacy of affordable housing parameters being followed in urban India.

Further research may be conducted on global best practices in housing, which may advise the housing policies in India.

The study suggests key areas that need intervention and modification to make the housing policies more robust and effective.

The study explores the social sustainability aspects of housing, which are often considered secondary in policies.

Because housing has a direct bearing on the physical, social and mental well-being of society, it is imperative to find housing solutions that are safe and resilient for a sustainable future. This paper is an original attempt by the author to question and highlight how the current affordable housing solutions adopted in urban India will continue to fail under any external adverse conditions unless modifications are considered in the existing housing parameters.

The main purpose of this research is to find an optimal allocation of marketing budgets which maximizes customer equity in an uncertain environment. Since markets are naturally uncertain environments, the aim is to incorporate uncertainty into the model.

Researchers have developed a mathematical programming model which employs customer equity as an objective function in order to allocate marketing budgets. The robust optimization approach is employed to tackle the proposed model, which deals with uncertainty.

The solution of the robust model is shown to be feasible and satisfactory in all uncertain situations. The robust solutions (of the presented model) are stable in volatile situations; while if the solution of deterministic models is used, it may be suboptimal or even infeasible. Sensitivity analysis of the deterministic solution only describes how stable is the suggested solution, but a robust optimization approach always provides a stable solution.

The presented model will be most effective where uncertainty is high; if uncertainty is not a matter of concern or estimates are reliable, deterministic models are also effective.

Companies periodically decide on marketing budgets in order to achieve predefined marketing targets in future periods. The results of this research may be useful and applicable in marketing departments for allocating marketing budgets, especially in uncertain environments.

The main contribution of this research lies in providing an approach to allocate marketing budgets in uncertain environments. Unlike previous studies, the presented method takes into account the uncertainty of parameters in a systematic way. Hence, in case of high degrees of uncertainty, the use of robust optimization is strictly recommended.

The literature identifies different sources of competitive advantages. The purpose of this paper is to suggest that an efficient articulation of a Voice behavioral model and an Exit behavioral model can result in a sustained competitive advantage for automobile manufacturers.

The paper builds on the analytical framework of the global value chain literature to analyze how the combination of hierarchy, relational linkages and market linkages creates additional assets. The case of Volkswagen‐Skoda in the Czech Republic is used to illustrate the combination of Voice and Exit relationships.

The paper suggests that the competitive advantage of automobile manufacturers crucially depends today on the combination, on the one hand, of the “relational rent” provided by the cooperation with global suppliers, and on the other hand, of the “switching rent” provided by the absence of implication in the relationship with lower‐tier suppliers.

The paper represents an original research initiative that considers the combination of three types of linkages as the source of competitive advantage. The paper uses a case study to exemplify the declination and articulation of the three types of linkages.