# Time Series Methods and Business Intelligent Tools for Budget Planning--Case Study
###### Abstract  
Corporate budget planning involves forecasting expenses and revenues to support strategic goals, resource allocation, and supply chain coordination. Regular updates to forecasts and collaboration across organizational levels ensure adaptability to changing business conditions. Long-term sales forecasts form the foundation for budgeting, guiding resource allocation and enhancing financial efficiency. The budgeting process in organizations is complex and requires data from various operational areas, which is collected over a representative period. Key inputs include quantitative sales data, direct costs indirect costs, and historical revenues and profitability, which are often sourced from ERP systems. While ERP systems typically provide tools for basic budgeting, they lack advanced capabilities for forecasting and simulation. We proposed a solution, which includes dynamic demand forecasting based on time series methods such as Build-in method in Power BI (which is ETS--exponential smoothing), linear regression, XGBoost, ARIMA and flexible product groupings, which are simulations for cost changes. The case study concerns a manufacturing company in the mass customization industry. The solution is designed to be intuitive and easily implemented in the business. data-driven decision making; predictive methods application; AI-related management; machine learning; ERP and BI systems +
Footnote †: journal: applied sciences  
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monitoring. Budgeting is crucial to effective business management [2]. Effective budgeting involves regularly updating cost and revenue forecasts and adapting them to changes in business conditions. It is a dynamic process that requires the participation of management and employees at every level of the company. Budgeting requires long-term sales forecasts that serve as the foundation for demand planning in the supply chain. Based on these forecasts, a company determines the quantity of raw materials, products, or services needed, which in turn influences the allocation of funds in the budget. For organizations dealing with complex decision-making environments, budgeting methods can provide sophisticated tools to enhance objectivity and manage uncertainties. By understanding and selecting the appropriate budgeting method, organizations can optimize their financial and volume planning and improve overall effectiveness [3]. Therefore, in the rapidly changing market environment, where companies must respond quickly to new challenges and opportunities, accurate forecasting and a flexible approach to budgeting are crucial. Unfortunately, traditional methods often prove insufficient in the face of increasing data complexity and the need to integrate various scenarios and data. There is a lack of tools that intuitively support this process. The contribution of the paper is a solution to support appropriate budget planning in the company using an ERP system that combines time series data prediction methods with a BI system. A solution allows the use of advanced forecasting methods, with the possibility of hierarchical forecasting at different levels of aggregation of the company's data, along with the possibility of simulating changes in exchange rates or including the possibility of simulating the growth defined by the management. An important aspect of our solution is its ease of implementation in the industry, which is facilitated by the utilization of Business Intelligence (BI) systems. These systems are increasingly adopted by companies primarily due to their user-friendly interfaces, which enable seamless use by employees. The paper is organized as follows. In Section 2, we present a review of the literature on quantitative and value strategic planning methods for budget. Section 3 includes an analysis of the budgeting process and its support with the ERP system tools. In Section 4, we describe the case study and solution to support budgeting with predictive methods and Business Intelligence tools.