USING PREDICTIVE MODELS BASED ON ARTIFICIAL INTELLIGENCE TO REDUCE THE IMPACT OF UNCERTAINTY AND IMPROVE THE ACCURACY OF COST CALCULATION IN PREPARING FLEXIBLE BUDGETS: AN APPLIED STUDY IN THE GENERAL COMPANY FOR VEGETABLE OIL INDUSTRIES

Abstract

The research aims to study the possibility of using predictive models based on artificial intelligence techniques to reduce the impact of uncertainty and improve the accuracy of cost calculations when preparing flexible budgets, through an applied study on the General Company for Vegetable Oil Industries for the period 2020-2024. I see that industrial companies have problems when they make budgets because raw material prices go up and down production rates change and the economy is uncertain. The research used an analytical approach to look at the companys financial statements. The research used models such as artificial neural networks, multivariate regression and random forests to forecast variable costs and fixed costs. The predictive models gave accurate results, than traditional methods. The results of the application of these models were also analyzed and compared to the company's previous budgets to determine the extent to which the accuracy of the estimates was improved and the differences between actual and expected costs were reduced. I read the study. Saw that using predictive models that use artificial intelligence helps cut the gap between the real costs and the expected costs. The predictive models can look at a lot of the financial data. The predictive models find patterns and hidden links, between the factors that affect costs. The predictive models also make the flexible budgets better. The predictive models give guesses of the variable and the fixed costs. The predictive models let the management move the money efficiently. The predictive models let the management plan the production better. The use of predictive models also reduces the impact of uncertainty on financial decisions, as it provides the company with predictive tools that help adapt to unexpected economic and operational changes, such as volatility in raw material prices or sudden changes in demand, enhancing the flexibility of financial planning and reducing the risks associated with making decisions based on inaccurate estimates.