Search results

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Strategic orientation is one of the critical principles affecting the marketing and strategy selection of an organization’s activities, reflecting the strategic tendencies implemented by the organization to create behaviours, leading to organizational efficiency and better employee performance. Therefore, this paper aims to study the relationship between strategic orientation and performance in Qazvin teaching hospitals through green supply chain management (SCM) to improve employees' performance.

This was a descriptive– analytical and cross-sectional study. The total number of employees in medical centres was 2,256 people. According to Morgan’s table, the required number of samples was 328, including 10% of the sample loss. The questionnaire was given to 365 staff in different hospitals in proportion to the number of staff. Two statistical software, SPSS24 and AMOS23, were used to evaluate the results.

All three variables were relatively moderate in the hospitals. The results of evaluating the structural model of the research showed that all the studied hypotheses were significant, except for the relationship between organizational performance and strategic orientation hypothesis. The results also show that this model had a good fit.

The findings can lead to saving the environment, creating a better social image and reducing costs and profitability, allowing managers to know the strategic orientations rather than depending on institutional pressure issues and monitoring guidelines.

Considering the significant relationship between the dimensions of green SCM and the hospitals’ performance, the authors suggest: establishing multilateral co-operation in environmental issues, improving the environmental status of hospitals, reducing waste rates and paying attention to social responsibility of hospitals. In addition, the findings help hospital management to comprehensively understand the strategies for implementing green SCM procedures.