東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Chemical industry- Forecasting" ]

切換: 標籤 | MARC模式 | ISBD

Digitization in controlling = foreca...

Kamphake, Andre GroBe.

FindBook

Google Book

Amazon

博客來

Digitization in controlling = forecasting processes through automation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Digitization in controlling/ by Andre GroBe Kamphake.
其他題名:	forecasting processes through automation /
作者:	Kamphake, Andre GroBe.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2020.,
面頁冊數:	xv, 70 p. :ill., digital ;24 cm.
內容註:	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
標題:	Chemical industry - Forecasting -
電子資源:	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 /
LDR:02838nmm a2200337 a 4500 001 2214991
003 DE-He213
005 20200501150137.0
006 m d
007 cr nn 008maaau
008 201119s2020 gw s 0 eng d
020 $a 9783658287412 $q (electronic bk.)
020 $a 9783658287405 $q (paper)
024 7 $a 10.1007/978-3-658-28741-2 $2 doi
035 $a 978-3-658-28741-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TP145 $b .K367 2020
072 7 $a KJC $2 bicssc
072 7 $a BUS041000 $2 bisacsh
072 7 $a KJC $2 thema
082 0 4 $a 660 $2 23
090 $a TP145 $b .K15 2020
100 1 $a Kamphake, Andre GroBe. $3 3446059
245 1 0 $a Digitization in controlling $h [electronic resource] : $b forecasting processes through automation / $c by Andre GroBe Kamphake.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Gabler, $c 2020.
300 $a xv, 70 p. : $b ill., digital ; $c 24 cm.
490 1 $a BestMasters, $x 2625-3577
505 0 $a 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.
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.
650 0 $a Chemical industry $x Forecasting $x Automation. $3 3446060
650 0 $a Big data. $3 2045508
650 1 4 $a Business Strategy/Leadership. $3 1565354
650 2 4 $a Innovation/Technology Management. $3 1565353
650 2 4 $a Business Process Management. $3 2134548
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a BestMasters. $3 2056364
856 4 0 $u https://doi.org/10.1007/978-3-658-28741-2
950 $a Business and Management (Springer-41169)