In recent years, governments have sought to find sustainable solutions that would have a positive impact economically, environmentally, and socially. Remanufacturing is a promising solution as remanufactured products help sustainability by saving resources, like using less raw materials, cutting emissions from traditional manufacturing, lowering the amount of landfill waste, and offering a cost-effective alternative product. This paper studies the preferences of people in the Kingdom of Saudi Arabia between new and remanufactured products across three categories: electronics, car parts, and furniture. The products were evaluated based on four factors: quality, price, availability, and warranty. This research used the Best-Worst Method and Technique for Order Preference by Similarity to Ideal Solution together for the analysis. For all the product categories, the findings showed that warranty is the most weighted criteria consumers will rely on to select between the new and remanufactured products. However, consumers prefer new products over the remanufactured ones for all the product categories. Supply chain decision-makers are required to optimize the pricing of these products to increase the popularity of these products.

Human migration is based on pull factors that individuals evaluate when it comes to moving to a different territory. Likewise, employee attrition is a phenomenon that represents the tendency to a reduction in employees within an organization. This research paper aims to develop and evaluate machine learning algorithms, namely Decision Tree, Random Forest, and Binary Logistic Regression, to predict employee attrition using the IBM dataset available on Kaggle. The objective is to provide organizations with a proactive approach to employee retention and human resource management by creating accurate predictive models. Employee attrition has significant implications for an organization's reputation, profitability, and overall structure. By accurately predicting employee attrition, organizations can identify the factors contributing to it and implement data-driven human resources management practices. This study contributes to improving decision-making processes, including hiring and firing decisions, and ultimately enhances an organization's capital. The IBM dataset used in this study consists of anonymized employee records and their employment outcomes. It provides a comprehensive HR data representation for analysis and prediction. Three machine learning algorithms, Decision Tree, Random Forest, and Binary Logistic Regression, were utilized in this research. These algorithms were selected for their potential to improve accuracy in predicting employee attrition. The Logistic Regression model yielded the highest accuracy of 87.44% among the tested algorithms. By leveraging this study's findings, organizations can develop predictive models to identify factors contributing to employee attrition. These insights can inform strategic decisions and optimize human resource management practices.