Mathematical Modeling and Statistical Forecasting for Business Decision Making
Abstract
- In today's competitive business environment, organizations need reliable methods to analyze historical data and predict future business conditions. Accurate forecasting can help organizations understand changes in sales, demand, revenue, and other important business indicators. However, business data often contains fluctuations, trends, seasonal patterns, and uncertainty, which can make decision-making difficult. This research focuses on developing an integrated framework that combines mathematical modelling and statistical forecasting to analyze historical business data and support systematic decision-making. The proposed framework applies mathematical modelling to identify relationships among important business variables and uses statistical forecasting techniques to estimate future trends. Different forecasting approaches can be evaluated using performance measures such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The forecasting results are then interpreted from a business perspective to identify expected demand patterns, potential risks, and future business conditions. This approach aims to provide more meaningful information than forecasting alone by connecting quantitative predictions with practical business decisions.