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Digitization in controlling = foreca...

Kamphake, Andre GroBe.

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Digitization in controlling = forecasting processes through automation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Digitization in controlling/ by Andre GroBe Kamphake.
Reminder of title:	forecasting processes through automation /
Author:	Kamphake, Andre GroBe.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2020.,
Description:	xv, 70 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Chemical industry - Forecasting -
Online resource:	https://doi.org/10.1007/978-3-658-28741-2
ISBN:	9783658287412

Digitization in controlling = forecasting processes through automation /
Kamphake, Andre GroBe.

Digitization in controllingforecasting processes through automation /[electronic resource] :by Andre GroBe Kamphake. - Wiesbaden :Springer Fachmedien Wiesbaden :2020. - xv, 70 p. :ill., digital ;24 cm. - BestMasters,2625-3577. - BestMasters..

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.

Andre GroBe 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 Target Groups Lecturers and students of management, corporate governance, controlling Controllers and data scientists The Author After successfully completing his master's degree in business administration in major Finance at the University of Cologne, Germany, Andre GroBe Kamphake works as a controller in the field of business development with a focus on reporting and data analysis.

ISBN: 9783658287412

Standard No.: 10.1007/978-3-658-28741-2doiSubjects--Topical Terms:

3446060
Chemical industry
--Forecasting

LC Class. No.: TP145 / .K367 2020

Dewey Class. No.: 660

Digitization in controlling = forecasting processes through automation /
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520 $a Andre GroBe 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 Target Groups Lecturers and students of management, corporate governance, controlling Controllers and data scientists The Author After successfully completing his master's degree in business administration in major Finance at the University of Cologne, Germany, Andre GroBe Kamphake works as a controller in the field of business development with a focus on reporting and data analysis.
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