A Cost-Benefit Analysis of Employee Layoffs versus Workforce Management Strategies: Assessing Financial Impacts and Organizational Profitability

Companies frequently use layoffs to minimize expenses during economic instability or organizational change, despite the possible detrimental impact on staff morale and business culture, so organizations consistently confront significant workforce management issues, particularly when deciding between employee layoffs and alternative measures like flexible staffing or retraining programs. This study investigates the financial consequences of these decisions, emphasizing how they influence profitability in organizational performance. The study leverages sophisticated algorithmic learning techniques those are Logistic Regression, Support Vector Machines (SVM), and Random Forests were chosen for their ability to forecast financial consequences, categorize workforce tactics, and provide accurate insights into short and long-term organizational performance to examine and compare the effects of employee layoffs to other workforce management measures. Logistic Regression calculates the likelihood of various financial consequences based on labor actions. SVM aids in categorizing and predicting the effectiveness of multiple tactics by determining the best decision boundaries between them. Random Forests improve forecast accuracy by merging several decision trees, providing a strong analysis of financial implications and operational precision. By providing a thorough examination of how layoffs and alternative methods influence financial performance and operational accuracy, the findings give practical insights that may help organizations implement more successful and sustainable managerial procedures. This, in turn, contributes to increased profitability and operational efficiency by aligning staff management decisions with overall organizational goals.