Digitization in Controlling
Forecasting Processes through Automation
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Beschreibung
Andre Große Kamphake deals with the digitization in controlling and focuses in this context on the analysis of automated forecasting processes within a chemical company. He aims at outlining to what extent and how accurate forecasting processes can be automated in the age of digitization and big data. Therefore, the forecast of the working capital is put at the center since it plays a leading role for the cash collection process. Based on data from 2015 to 2018, two different forecasting models are combined to optimally predict the different components contained in the working capital. The author manages to prove that both a trained forecasting algorithm achieves a prediction accuracy of 92.49 % and statistical methods in machine learning lead to a significant increase in forecasts compared to naive forecasting models.
Contents
- The Challenge of Digitalization Projects
- Optimization of Working Capital Management
- Proceeding for Data-Driven Data Mining Forecasts
- Application of the Decision Tree Algorithm C 5.0
- Implementation of the ARIMA Time Series Model
- Combination of Forecasting Methods Aiming Better Results
- Lecturers and students of management, corporate governance, controlling
- Controllers and data scientists
Andre Große Kamphake deals with the digitization in controlling and focuses in this context on the analysis of automated forecasting processes within a chemical company. He aims at outlining to what extent and how accurate forecasting processes can be automated in the age of digitization and big data. Therefore, the forecast of the working capital is put at the center since it plays a leading role for the cash collection process. Based on data from 2015 to 2018, two different forecasting models are combined to optimally predict the different components contained in the working capital. The author manages to prove that both a trained forecasting algorithm achieves a prediction accuracy of 92.49 % and statistical methods in machine learning lead to a significant increase in forecasts compared to naive forecasting models.
Autor*in
Andre Große Kamphake
Themen in »Digitization in Controlling«
Controlling Forecasting Predictive Analytics Digitization Digitalization Decision Tree ARIMA