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.

Most small and medium scale enterprises in Ghana neglect very critical performance indicators in their bossiness. As a result most SMEs do not survive till their fifth anniversary. The purpose of this study is to examine the relationship between prevention focus, promotion focus, resilience, and SME performance in Ghana. Furthermore, we investigated the moderation effect of entrepreneurial learning and Entrepreneurial orientation on the relationship between entrepreneurial resilience and SME performance in Ghana. Our results show that both promotion- and prevention-focused increase entrepreneurial resilience and SME performance significantly. This is an indication that there is a strong positive correlation between entrepreneurial resilience and success in both the individual and firm levels. This inference can therefore assist policy makers, politicians and various industry players in designing training programs on how to improve the resilience of SME owners in Ghana. Statistically our analysis shows that prevention focus (PreV) has a positive effect on entrepreneurial resilience (EntR) (β = 0.250; p<0.001), and promotion focus (PreM) has a positive effect on entrepreneurial resilience (EntR) (β = 0.268; p<0.001). Additionally, entrepreneurial resilience (EntR) has a positive effect on innovative performance (IP) (β = 0.547; p<0.001). Furthermore, the mediation results has shown that entrepreneurial resilience (EntR) mediates the relationship between prevention focus (PreV) and innovative performance (IP) (β = 0.136; p<0.001); as well as that of promotion focus (PreM) and firm performance (IP) (β = 0.146; p<0.001). Finally, the results with regards to moderation effects has shown that entrepreneurial orientation (EntO) partially moderates the relationship between entrepreneurial resilience (EntR) and innovative performance (IP) (β = 0.109; p<0.05), however, the result established that entrepreneurial learning (EntL) does not moderate the relationship between entrepreneurial resilience (EntR) and firm performance (IP) (β = -0.031; p<0.05). Using the Resource Based View theory, we concluded that, SME’s needs to adopted prevention (vigilant/avoidant strategies) by minimizing losses, and taking calculated risks with motivation from mentors to avoid loss as this would achieve resilience as well as promotion of strategies (eager/approach strategies) to achieve gains for their SMEs during economic disruption and economic downturn such as COVID-19. We concluded that SME’s could also achieve resilience when managers or owners of SMEs inspire the employees to work harder, and think outside the box to identify latent ideas and opportunities within their SME business environment.

The management of recycling and remanufacturing supply chains, which can help enterprises achieve low pollution, low energy consumption and sustainable development, has become a new strategy of modern enterprises. The factors of supply chain and government mechanisms will have an impact on enterprise decisions for recycling, remanufacturing and social welfare. In order to promote the sustainable operation of the supply chain, considering the coordination role of government mechanisms and supply chain, a recycling and remanufacturing supply chain model composed of a manufacturer, retailer and recycler is constructed. This paper discusses the pricing decision of new/remanufactured products, supply chain performance level, such as remanufacturing effort, publicity service efforts and profit, and social welfare in five models of three situations: centralized situation, including non-government mechanisms and non-supply chain coordination; manufacturer-led situation, including non-government mechanisms and non-supply chain coordination, government mechanisms and non-supply chain coordination, government mechanisms and supply chain coordination; government-led situation, including government mechanisms and non-supply chain coordination. It is found that under manufacturer-led situations, the government subsidy and bonus-penalty mechanisms can encourage manufacturer and retailer to actively participate in the recycling and remanufacturing activities. The supply chain coordination contract can further enhance the role of the consumer market and promote the implementation of government mechanisms. Manufacturer adopts a cost-sharing contract to encourage recyclers to carry out recycling activities. Under certain conditions, the contract can effectively improve the benefits and social welfare. The research conclusions have important theoretical and practical application value for the coordination and cooperation among enterprises in the supply chain and the formulation of government mechanisms.

Managing blood inventory is challenging due to the perishable and unstable nature of the product needed for transfusions in healthcare facilities. In this paper, we consider a periodic review blood inventory model with two priority demand classes, namely emergency and regular patients. We propose a dynamic programming model for determining the optimal ordering policy at the hospital given the uncertainty regarding received donated blood units. The optimal policy deals with placing orders for blood units that will expire within a fixed period. The objective is to minimize total expected costs within a planning horizon while maintaining a specified expected service level. Our model considers uncertain demands and donated blood units with discrete probability following known distributions. A tabu search algorithm is developed for large-scale problems. The performance of these ordering policies is compared against the optimal fixed order quantity and the order up-to-level policies using real-life data. The numerical results show the benefit of our model over the optimal fixed order quantity and the order up-to-level policies. We measure the total expected cost and the expected service level obtained from the optimal and near-optimal policies and provide a sensitivity analysis on parameters of interest.

This paper addresses the permutation flowshop scheduling problem with peak power consumption constraints (PFSPP). The real-time power consumption of the PFSPP cannot exceed a given peak power at any time. First, a mathematical model is established to describe the concerned problem. The sequence of operations is taken as a solution and the characteristics of solutions are analyzed. Based on the problem characteristics, eight heuristics are proposed, including balanced machine-job decoding method, balanced machine-job insert method, balanced job-machine insert method, balanced machine-job group insert method, balanced job-machine group insert method, greedy algorithm, beam search algorithm, and improved beam search algorithm. Similarly, the canonical artificial bee colony algorithm and iterated local search algorithm are modified based on the problem characteristics to solve the PFSPP. A large number of experiments are carried out to evaluate the performance of new proposed heuristics and metaheuristics. The results and discussion show that the proposed heuristics and metaheuristics perform well in solving the PFSPP.

This paper aims to investigate the commitment of health facilities in the Al-Kharj Province in Saudi Arabia to the dimensions of social responsibility and their disclosure impact on the improvement of the quality of health care. The study gains its importance from the importance of social responsibility which is one of the topics that contributes to the realization of the kingdom's Vision 2030. Partial Least Squares Structural Equation Modeling (PLS-SEM) is utilized in this study, to examine the relationship between dependent and independent variables. The study documents that the human dimension of social responsibility disclosure positively affects the quality of health care, while there is no clear effect of the legal dimension, the economic dimension, and the ethical dimension on the quality of health care institutions in Al-Kharj Province.