Allocating financial resources effectively remains a major concern for organizations working with limited budgets and multiple constraints. In many cases, decisions are still based on past experience or fixed budgeting practices, which may not produce consistent results when conditions change. This study presents a framework that combines Management Information Systems (MIS) with linear programming to support structured financial decision making. The framework uses data collected from MIS platforms, including financial records, cost information, and performance indicators. This data is organized into a mathematical model where allocation decisions are treated as variables and operational requirements are expressed as constraints. A linear programming model is then used to determine how resources should be distributed to maximize expected return while staying within budget limits. The results show that the proposed approach produces consistent and feasible allocation outcomes across different scenarios. When compared with common methods such as equal allocation and historical budgeting, the model provides better resource distribution and higher overall efficiency. Sensitivity analysis indicates that the model responds logically to changes in budget and input parameters, maintaining stable allocation patterns. Some limitations remain, particularly the use of linear assumptions and fixed input values, as well as dependence on data quality. Despite these constraints, the study demonstrates that combining MIS with optimization techniques offers a practical and systematic way to improve financial resource allocation in organizational settings.
Ahmed Erfan Nahian (Tue,) studied this question.