Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study
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 foreca...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/287 |
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| author | Katarzyna Grobler-Dębska Rafał Mularczyk Bartłomiej Gawęda Edyta Kucharska |
| author_facet | Katarzyna Grobler-Dębska Rafał Mularczyk Bartłomiej Gawęda Edyta Kucharska |
| author_sort | Katarzyna Grobler-Dębska |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-424153df4e0d44008f12505f2a85b699 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-424153df4e0d44008f12505f2a85b6992025-01-10T13:15:02ZengMDPI AGApplied Sciences2076-34172024-12-0115128710.3390/app15010287Time Series Methods and Business Intelligent Tools for Budget Planning—Case StudyKatarzyna Grobler-Dębska0Rafał Mularczyk1Bartłomiej Gawęda2Edyta Kucharska3Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, PolandFaculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, PolandFaculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, PolandFaculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, PolandCorporate 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.https://www.mdpi.com/2076-3417/15/1/287data-driven decision makingpredictive methods applicationAI-related managementmachine learningERP and BI systems |
| spellingShingle | Katarzyna Grobler-Dębska Rafał Mularczyk Bartłomiej Gawęda Edyta Kucharska Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study Applied Sciences data-driven decision making predictive methods application AI-related management machine learning ERP and BI systems |
| title | Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study |
| title_full | Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study |
| title_fullStr | Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study |
| title_full_unstemmed | Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study |
| title_short | Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study |
| title_sort | time series methods and business intelligent tools for budget planning case study |
| topic | data-driven decision making predictive methods application AI-related management machine learning ERP and BI systems |
| url | https://www.mdpi.com/2076-3417/15/1/287 |
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