Digitalization of BSR (buyer supplier relationship) is generally one of the effective tools to strengthen the supply chain. This study aims to establish the correlations between the perceived importance of BSR, investment in BSR digitalization and ER (economic result) change. Data collecting was realized in the form of a questionnaire survey. This survey was carried out in Czech enterprises of different focuses and sizes. Hypotheses are tested using Pearson's Chi-squared test. The study confirmed that correlation between the perceived importance of BSR and investment to BSR digitalization is stronger for producers than for service providers. The investment in BSR digitalization brings about ER development in the short term only for services providers. Enterprises do not associate the development of ER and the importance of BSR. BSR is considered an important area as part of the SCM whole, but ER development is not associated with this area, nor does investment in this area have a clear economic effect in a recession. Thus, the logical triad of "importance of the process- investment in process development – process effect" is unprovable in the case of BSR digitalization in a recession.

Smart robotic wheelchairs have emerged as promising assistive devices to enhance mobility and independence for individuals with mobility impairments. The successful integration of advanced sensor systems plays a critical role in improving the functionality and safety of these wheelchairs. This paper presents a comprehensive review of the design and implementation of advanced sensor systems for smart robotic wheelchairs. Through an extensive literature review, the limitations of existing sensor technologies are identified, and the potential of advanced sensors is explored. Vision-based sensors, range and proximity sensors, force and pressure sensors, inertial sensors, and environmental sensors are discussed in detail. Furthermore, this review highlights the design considerations, hardware components, software development, and calibration procedures involved in implementing advanced sensor systems. Evaluation and performance analysis metrics are discussed to assess the effectiveness of the sensor systems. The research findings indicate that advanced sensor systems have the potential to significantly enhance the functionality and safety of smart robotic wheelchairs. However, challenges such as sensor integration, data fusion, and user feedback must be addressed. This review paper concludes by discussing the implications of advanced sensor systems in improving wheelchair functionality and user experience, and proposes future directions for research in this domain.

The previous research relating to CSR and mindfulness leadership, for the purposes of this study, provides an intersection where both backgrounds come together to invite an opportunity for collaboration. The most critical factor in the implementation of CSR is the development of leaders for a sustainable global society as mindfulness should be relevant to the achievement of organizational and leadership objectives. It is here that we find the inspiration for the current study and the relevance of the research query endeavoring to discover if the framework of mindfulness leadership could prove an effective driver for CSR? For this reason, 20 in-depth interviews were conducted from the middle level managers working in an emerging economy - Pakistan. The conceptual framework of leadership theory guided this study’s investigation as it brought to the forefront certain challenges facing organizational leaders endeavoring to facilitate CSR objectives; and explored if the application of mindfulness leadership could prove an effective leadership framework for successful CSR engagement. This supporting data coupled with the study’s interview of executive leaders, across industry lines, familiar with CSR initiatives, lent to certain analytical insight that identified mindfulness leadership as a framework for organizations to facilitate the accomplishment of CSR objectives.

Additive Manufacturing (AM) is an automated process of fabricating three-dimensional (3D) physical objects from a 3D-CAD data by adding layers of materials one upon another through a print head or nozzle without using any tooling components or machining environments. Due to freedom in design, any complex shape can be produced using this process. Fused Deposition Modeling (FDM) is one such AM technology that is commonly used for its simplicity, environment friendliness and low requirement for process monitoring. However, this technology is limited only to small-scale production due to high cost and high build time. The present work focuses on the development of a framework for parametric optimization of the FDM process using multi-objective optimization based on ratio analysis (MOORA). A CAD model of the cam follower mechanism has been prepared in the Solidworks platform and used in this experiment for optimization of build time and cost which have been considered as response variables of the experiment. The experiment has been conducted following the full factorial design of experiment (DoE) method.

Suppliers can use different strategies to distribute their products, Among the most common complex optimization problems related to the transportation of products is the traveling salesman problem. In the traveling-salesman problem, a route is chosen that visits each node exactly once, taking into account the shortest travel time, and finally returns to the original node. In this problem, all nodes must be visited. If we consider the application of this problem in logistics, we can study the necessity of this problem in transportation means such as trucks or drones. The upcoming paper is thoroughly studied and researched considering the related articles published in the last three decades, and bibliometric analysis is used for the details of this problem. This paper aims to statistically evaluate the influence and importance of the traveling salesman on logistics without considering financial risk by presenting an analysis of the works published between 1983 and 2023. As part of our comprehensive literature review table with analysis of export, we will conduct a comprehensive review of the most relevant articles in the field from 2020 to 2023 to better understand the trend in the subject in the last few years. Data were obtained from the Web of Science and focused on metrics such as the total number of publications, citations, average citations per publication, and trending countries. Graphical and statistical analysis was performed using Excel and R-Studio. China, the USA, and Germany are the countries with the most publications. Laporte is the most prolific author with 8 publications. Much research has been done on this topic, especially in the Journal of transportation research part E-logistic with 43 articles, and the main application areas are logistics, vehicles, and drones. These data may prove useful to researchers seeking an overview of the traveling salesman problem to determine future research directions.

Training and productivity have been found positively correlated in theory and practice. The pattern of the relationship, however, was not explored in pre - and post-regulation in the conceived sector. This paper aims to observe the effects of regulation and training expenses along with other covariates on employee productivity in the Microfinance sector of Bangladesh. Using annual data of MRA-licensed MFIs, we have estimated both panel and cross-sectional regression models. The regression results confirm the theoretical relationship between training expenses and employee productivity. The regulation also worked positively in enhancing employee productivity. However, in the early stage of regulation, the average productivity gain due to regulation was substantial and was showing an increasing trend but then it declined and reached a constant level of about 4% - 5% each year. Between 2008 and 2011, both regulation and training positively contributed to the gains in average productivity of the employees. After 2012, there was a positive trend of average productivity elasticity of training expenses but there was a flat effect of regulation after 2012. Regulation was found to short-run shifter in the average productivity of employees while training expenses had a positive trend effect.

The main focus of this research paper is to shed light on the challenges encountered by the chair production department employees when it comes to identifying and locating glue residue after the assembly process. This issue arises due to the transparent nature of the glue, making it difficult to detect and subsequently negatively impacting the overall quality of the chairs in the polishing department. To tackle this problem, various experiments and tests were conducted, which are thoroughly discussed in this paper. The findings of this study are not only applicable to the specific manufacturing company under investigation but also to other companies within the industry that face similar difficulties in detecting defects caused by transparent glue. As a result, a new method for detecting glue defects is proposed, which can be adopted by various industries encountering similar challenges.

Effective application of tools and techniques is critical for successful Total Quality Management (TQM) implementation. The influence of organization profile on the application of TQM tools and techniques and outcomes is investigated in Indian organizations. Correlation analysis and ANalysis Of VAriance (ANOVA) are utilized for the research. Tools and techniques are found having significant relation to the outcomes of implementation. Organization’s age, size, and turnover significantly influenced the TQM results. Organization’s size and turnover significantly influenced the application of tools and techniques. The study suggests taking the organization profile into consideration for effective implementation. Further research can be conducted in other countries.

The COVID-19 surge has mostly affected people and wreaked havoc on multiple sectors of the global economy. This study uses artificial neural networks (ANN) to develop COVID-19 prediction models to minimize the perilous situation. With positive infection data, these hybrid artificial neural network models looked at COVID-19 cases in Andhra Pradesh, India. Then, COVID-19 data were divided into training and testing for simulation. The developed model that takes the previous 14 days into account outperforms the others, depending on the results. According to the developed ANN models for Andhra Pradesh districts, the prediction model that works well and yields positive results is the one that receives lower values of error metrics like MSE, RMSE, MAE, and MAPE and higher values of R2. The hybrid neural network model that considers the previous 14 days for prophecy has suggested anticipating daily positive suffering, notably in areas of Andhra Pradesh, as a result of the collected data. Linear regression, ARIMA, and LSTM have been used for model assessment. The proposed 14-day model statistically surpasses all metrics in RMSE, MAE, MAPE, and R2. This study showed that an ANN-based model can predict the COVID-19 outbreak as well as other epidemics.

This study explores the impacts of participatory design (PD) on data-driven decision-making (DDDM) in organisations. Despite the extensive examination of PD and DDDM individually, there is a dearth of research in understanding their integration and their impact on decision-making processes in organisations. This research aims to fill this gap by investigating the potential impacts, challenges, benefits, and critical success factors associated with the incorporation of PD activities into DDDM. The study employs a systematic literature review methodology to provide a comprehensive understanding of the topic. The paper provides a research agenda for future researchers as well as discussing best practices for organizations seeking to optimise their data driven decision-making processes in a participatory manner. The research also discussed the ethical implications of data-driven decision-making. Ultimately, this research advances our understanding of how PD and DDDM can be effectively combined to achieve better decision-making outcomes.