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The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.

An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.

The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.

Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

Molecular dynamics is an emerging simulation technique in the field of machining in recent years. Many researchers have tried to simulate different processing methods of various materials with the theory of molecular dynamics (MD), and some preliminary conclusions have been obtained. However, the application of MD simulation is more limited compared with traditional finite element model (FEM) simulation technique due to the complex modeling approach and long computation time. Therefore, more studies on the MD simulations are required to provide a reliable theoretical basis for the nanoscale interpretation of grinding process. This study investigates the crystal structures, dislocations, force, temperature and subsurface damage (SSD) in the grinding of iron-nickel alloy using MD analysis.

In this study the simulation model is established on the basis of the workpiece and single cubic boron nitride (CBN) grit with embedded atom method and Morse potentials describing the forces and energies between different atoms. The effects of grinding parameters on the material microstructure are studied based on the simulation results.

When CBN grit goes through one of the grains, the arrangement of atoms within the grain will be disordered, but other grains will not be easily deformed due to the protection of the grain boundaries. Higher grinding speed and larger cutting depth can cause greater impact of grit on the atoms, and more body-centered cubic (BCC) structures will be destroyed. The dislocations will appear in grain boundaries due to the rearrangement of atoms in grinding. The increase of grinding speed results in the more transformation from BCC to amorphous structures.

This study is aimed to study the grinding of Fe-Ni alloy (maraging steel) with single grit through MD simulation method, and to reveal the microstructure evolution within the affected range of SSD layer in the workpiece. The simulation model of polycrystalline structure of Fe-Ni maraging steel and grinding process of single CBN grit is constructed based on the Voronoi algorithm. The atomic accumulation, transformation of crystal structures, evolution of dislocations as well as the generation of SSD are discussed according to the simulation results.

In response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.

Based on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.

When the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.

Most existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.

This paper aims to analyze how decision support systems manage Big data to obtain value.

A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.

The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.

As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

The main aim of this study is to highlight the significance of fostering social capital and improving the quality of work life (QWL) for the well-being of healthcare workers. The second objective of this research is to address a notable gap in the current knowledge by examining the mediating influence of QWL on the relationship between work-related social capital and life satisfaction within the healthcare profession.

This study used a cross-sectional research methodology to examine the complex relationships among the variables and included a sample of 330 individuals who are employed full-time in the healthcare profession in the North Indian Region.

The study confirms all research hypotheses, showing that social capital improves work life. Thus, work-life quality improves life satisfaction significantly. The mediation analysis in this study used bootstrapping to show that work-life quality mediates the association between social capital and life satisfaction.

Addressing social support issues and using effective human resource management tactics can improve employees’ work life and satisfaction. The findings are essential in collectivistic cultures because strong workplace relationships improve professional welfare.

This study differentiates itself by analysing social capital and QWL as multi-dimensional constructs inside the workplace, ensuring the results’ correctness and validity. This study provides a distinct viewpoint for scholars and practitioners, enhancing comprehension of the correlation between life satisfaction and work-related social capital within the healthcare industry.

This editorial article introduces and analyzes a variety of new organizational forms that rapidly emerged in Africa, Asia, Latin America and Eastern Europe in the latest two decades. Among the others, these include: business model partnerships, business platforms, incubators and hubs, public–private partnerships, agribusiness companies' foundations and spin-offs, short supply chains, community-supported agriculture and other community self-organizing experiences. Building upon the recent literature and the five selected papers in this special issue, the authors discuss what is novel in these organizations and why, when and how they emerge and evolve over time.

The authors identify three elements that, when considered together, explain and predict the emergence and evolution of these new organizational forms: institutions, strategies and learning processes.

The authors demonstrate that societal actors seeking to (re)design these new organizational forms need to consider these three elements to combine the pursuit of their interests of their own constituencies with the sustainable development goals (SDGs).

Taking stock from the literature, the authors invite future research on new organizational forms to take explicitly the pursuit of the SDGs into consideration; to build upon a process ontology; and to deeply reflect on our positionality of scientists studying and sometimes engaging in these organizations.

This study aims to explore the relationship between work–life balance and employee retention by examining the mediating role of psychological empowerment among software firms based in India.

The study collected 283 responses by using a structured questionnaire and interview method. Structural equation modeling (SEM) was used to validate the hypothesized research model for examining the consistency and sturdiness of the study variables by applying AMOS 20.

The result reveals that psychological empowerment partially mediates the relationship between work–life balance and the retention of professionals. This indicates that a high degree of psychological empowerment strengthens the relations between work–life balance and the retention of professionals.

The research outlined a best-fit model of psychological empowerment as a partial mediator among work–life balance and the retention of professionals. The study presents a set of sensible and practical aspects where work–life balance and retention of professionals can aid in developing and generating commitment to the organization which could offer new insights for software professionals, managers and practitioners.

This study emphasized that psychological empowerment helps in enhancing dedication, loyalty, integrity, allegiance and trustworthiness among employees, thus playing a role between work–life balance and the retention of professionals.